From Call Accounting to AI-Driven Communication Intelligence: RSI Shadow Analytics Grows With You

 

Your communications environment tells a story—about your customers, your people, and how your business operates. Turning that story into better decisions has been RSI’s focus since 1990.

Today, that mission spans proven call accounting software, call center analytics, and a growing portfolio of AI-driven Communication Intelligence solutions.

As technology evolves, our commitment remains consistent: help you understand the environment you have today and support the direction you choose tomorrow.

Supporting Your Environment—and Your Next Step

Every organization moves at its own pace. Some continue to rely on established on-premise systems. Others operate hybrid environments, adopt cloud services, or manage several platforms across locations and departments.

RSI supports that diversity.

Our compatibility spans platforms such as Avaya IP Office and Avaya Communication Manager, Mitel, and Cisco Unified Communications Manager, alongside cloud services including RingCentral, Webex Calling, Zoom Phone, and Microsoft Teams.

These are examples of our broader vendor support. Across on-premise, hosted, hybrid, and cloud environments, RSI helps organizations turn communication activity into useful business insight.

From desktop phones to softphones, from established infrastructure to emerging AI resources, your need for visibility continues as your environment changes.

Shadow All In One Analytics: Proven Insight for Established Systems

For organizations relying on traditional communication platforms, Shadow All In One Analytics remains a trusted, actively supported part of the RSI portfolio.

Its call accounting and call center analytics capabilities help organizations understand usage, track missed calls, investigate routing, monitor service levels, and manage communication costs.

Historical reporting gives managers perspective on demand, staffing, and performance over time. Call journey analysis helps reveal what happened as a caller moved through the organization.

These are everyday operational needs with real consequences for customer service and business performance. RSI continues to invest in serving the customers and partners who depend on them.

Shadow Analytics: A Broader View Through AI

For organizations seeking deeper analysis and new ways to work with their data, Shadow Analytics delivers AI-driven Communication Intelligence.

Bringing communication data into a shared analytical foundation helps teams connect activity, performance, and customer experience.

Call journey analysis reveals friction across queues, transfers, and handoffs. Pattern intelligence helps identify changing demand and performance trends. Voice analytics adds perspective from available recordings and transcriptions, including sentiment and customer experience signals.

As AI agents become part of customer service, visibility must extend to their performance too. Understanding how interactions are handled—and when they move from AI to a person—helps organizations evaluate the experience as a whole.

RSI’s strength is the breadth of that perspective: operational reporting, customer journeys, human and AI performance, and ecosystem health working together to support better decisions.

Shadow Analytics MCP: Put Your Questions to Work

Shadow Analytics MCP makes that intelligence accessible through supported AI assistants such as ChatGPT and Claude.

Using the Model Context Protocol, it connects AI tools with the data and analytical capabilities inside Shadow Analytics. Teams can ask questions in plain language and explore the findings through follow-up questions.

For example:

• Where did missed calls increase this month?

• Which queues are experiencing longer waits?

• What patterns should influence our staffing plans?

• How are AI-handled interactions moving to human agents?

This conversational approach gives business users another practical way to investigate performance and turn findings into action.

Experience for Today. Intelligence for What Comes Next.

Technology decisions involve investment, operational needs, and timing. RSI’s portfolio gives organizations choices throughout that process.

Continue gaining value from established systems with Shadow All In One Analytics. Explore AI-driven Communication Intelligence with Shadow Analytics. Bring that intelligence into your AI workflows with Shadow Analytics MCP.

Wherever you are in your communications journey, RSI is ready to help you see more clearly.

Better Insights. Better Decisions. Better Outcomes.

Explore RSI’s communication analytics portfolio or view our technology partners and platform compatibility.


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The Total Picture: Shadow Analytics Across Your Entire Communications Ecosystem

Your communications platform vendor can tell you what happened on its own platform. It cannot tell you what happened to your business. That gap is where most communications reporting quietly fails, and it is the gap Shadow Analytics was built to close.

Every vendor in your stack ships dashboards, reporting APIs and, increasingly, MCP servers. Each one is useful. Each one is also a window onto a single room. Your customers, your employees and your executives do not live in a single room: a customer calls in on one platform, gets transferred, and the follow-up happens in a Teams meeting. Looking at any one vendor in isolation tells you about one chapter of a story that spans the whole book.

A day in the life of a major enterprise communication manager using RingCentral RingCX and RingEX in remote sites.

Nicholas is a communications manager, and his call center runs like clockwork. Calls come in, get routed, get answered. From the outside, everything looks fine. But anyone who has managed a contact center knows that "running smoothly" and "understood" are two different things.

Then the hard questions arrive

The trouble starts when someone asks why. Why are costs creeping up? Where are customers getting stuck? Are we about to miss our service levels?

The phone system records everything, yet the answers stay buried. Nicholas and managers like him run into the same three gaps:

• No visibility. Data exists, but nobody can see the whole customer journey in one place.
• No intelligence. Numbers tell you what happened, not what it means or what to do next.
• Service levels at risk. By the time the report is built, the problem has already reached the customer.
  

Shadow Analytics, connected through MCP

Shadow Analytics sits on top of the call data and turns it into something you can use. The change in this story is how Nicholas gets to it.

MCP, the Model Context Protocol, is an open standard that lets AI assistants connect directly to the
tools and data a business already runs on. With Shadow Analytics connected through MCP, Nicholas doesn't open a report builder or write a query. He asks a question in plain language and gets an answer.

That removes the two things that slow most managers down: learning the tool, and waiting for someone who has.

What it looks like in practice

In the video, Nicholas opens his laptop and types a request: show call volume growth by year. A chart builds on screen, bar by bar, with the peak year highlighted. There is no report to schedule and no analyst to wait for.

The real value is that the next question costs nothing. A manager can keep going:

• Which queues are closest to missing their service level today?
• What is driving the rise in call volume?
• Where did cost per call change, and when?

These are examples of the kind of question the approach is built for. Each one used to be a separate report request. Now each is a sentence.

It doesn't just report. It answers.

That is the idea behind Shadow Analytics with MCP: better insights, better decisions, better outcomes. Reports tell you what happened. Answers tell you what to do about it, while there is still time to act.

If your team has data it can't easily question, ask whether the bottleneck is the data or the way you reach it. To see Shadow Analytics in action, visit www.rsicloud.com.

Contact us for more information

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Different Stakeholders Have Different Objectives: How the RSI Communication Intelligence Data Lake Can Be Consumed By Different Roles

Every person in an organization looks at communications through a different lens. With Shadow Analytics MCP, each of them can ask their own questions of the same trusted data, and see only what their role allows.

Communication intelligence is more than counting calls. It is understanding how conversations flow, how teams perform, how customers feel, and how all of that connects to the goals of the business. The insight an agent needs is very different from the insight an executive needs, yet both come from the same source.

Shadow Analytics brings that source together in the RSI data lake. The Shadow Analytics MCP connector then opens it up to the AI assistant each person already uses at work, whether that's ChatGPT, Claude, Copilot, Gemini, or another MCP-compatible assistant. The result is one body of communication intelligence viewed from many perspectives.

Protected by Roles, From the Start

Opening data to AI only works when the right people see the right information. In Shadow Analytics, every connection runs under the user's own role. Administrators decide who may use AI connectors at all, and row-level restrictions limit each person to the agents, sites, or departments they are responsible for. Those limits apply to every AI request, exactly as they do inside the application, and every request is recorded in the audit log.

That means the same connector can serve an entire organization safely. Each person gets a view that is both personal and protected.

The Agent: A Personal Performance Coach

For the people handling conversations every day, Shadow Analytics MCP acts as a private performance coach. Agents can review their own activity, track how their handling times, outcomes, and customer sentiment are trending, and understand where they are improving and where they can grow.

Because their role limits them to their own data, agents can explore freely and with confidence. Instead of waiting for a periodic review, they can take ownership of their development whenever they choose.

The Supervisor: Queue Health and Team Efficiency

Supervisors live in the details of daily operations. With Shadow Analytics MCP, they can dig into queue KPIs such as volume, wait times, abandonment, and service levels, and see how efficiently their team is handling demand across the day.

They can compare performance across agents and queues, surface coaching opportunities, and spot emerging problems before they grow. As AI agents take on a growing share of customer conversations, supervisors can also compare how AI-handled and human-handled interactions perform, helping them decide where each works best.

The Manager: Objectives, Campaigns, and Service Levels

Managers are measured on results over weeks and months. Shadow Analytics MCP helps them track progress against tasked objectives, evaluate campaign performance, and confirm that service levels are being met consistently across teams and sites.

Managers can also measure the impact of AI integration across their operation: how automation is affecting workload, customer experience, and team capacity. Detailed breakdowns and trend analysis turn this into clear evidence for planning and for reporting upward.

The Executive: Targets, Forecasts, and the Bottom Line

Executives need the big picture, and they need it quickly. Shadow Analytics MCP turns large volumes of communication data into concise executive summaries that connect operational activity to organizational targets and goals.

Forecasts built on historical trends help leaders anticipate demand and plan resources. Clear views of efficiency, customer experience, and AI adoption show how the communications operation is contributing to the bottom line, without anyone needing to assemble reports by hand.

Same Data, Different Questions, Aligned Answers

The real power of this approach is alignment. When agents, supervisors, managers, and executives all draw from the same data lake, they aren't debating whose numbers are right. They are each asking the questions that matter to their role, and the answers fit together.

Agent performance rolls up into queue efficiency. Queue efficiency drives service levels and campaign results. Those results shape the targets and forecasts leadership relies on. Shadow Analytics MCP makes every layer of that story visible to the people who need it.

The Bottom Line

Communication intelligence shouldn't belong only to the analysts who know how to build reports. With Shadow Analytics MCP, everyone in the organization can gain insight from the RSI data lake, in their own words, through the AI they already trust, and always within the boundaries of their role.

One data lake. Many perspectives. Better decisions at every level.

Contact us for more information




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Your search:

• Shadow Analytics MCP
• communication intelligence
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• AI vs human agent performance
• executive summaries and forecasting
• ChatGPT, Claude, Copilot, Gemini integration

Unified Communication and Call Center Morphing Into One: Shadow Analytics Manages the Entire Ecosystem

Modern organizations don't run a "phone system" anymore. They run a complex ecosystem using many channels mixed with human and agentic resources. Shadow Analytics gives that ecosystem one clear view.

Today's business communications are spread across many systems: unified communications for internal teams, contact center platforms for customer-facing staff, text messaging, team chat, call recordings, transcripts, and the routing logic that connects them all. Every one of these systems produces valuable data. The trouble is that each one usually reports on itself, in its own portal, in its own format.

That leaves leaders with partial answers. They can see one part of the operation clearly, but rarely the whole picture. Shadow Analytics from RSI (Resource Software International) is built to close that gap.

The Ecosystem Challenge

Customer relationships don't stay inside one platform or one channel. Conversations move between systems, between teams, and between voice and text. When the data behind those conversations is scattered, it becomes hard to measure service quality, spot problems early, or understand what the customer actually experienced.

Traditional call accounting tools were designed to count calls and assign costs. They were never meant to follow a conversation across platforms and channels. Shadow Analytics was.

One Reporting Layer, Many Platforms

Shadow Analytics connects to the leading communications platforms, including RingCentral, Microsoft Teams, Zoom, Webex, and Intermedia. Where a provider offers both unified communications and contact center services, Shadow Analytics can bring the two together, so results can be viewed side by side or combined into a single picture.

Because the reporting layer stays consistent, organizations keep the same trusted view of their operation even as their technology evolves.

Complete Visibility Into Every Interaction

Shadow Analytics shows the full path of every call on a single timeline, synchronized with recordings and transcripts where they exist. Instead of piecing together fragments from separate systems, teams can see the complete story of an interaction from start to finish and resolve questions quickly and confidently.

Beyond Voice

On supported platforms, Shadow Analytics also captures and indexes text messaging and team-messaging history, making those conversations searchable alongside call data. Voice and written communications no longer live in separate silos, which gives organizations a fuller understanding of every customer relationship.

Real-Time Awareness

Historical reports explain what already happened. Operations teams also need to know what is happening right now. Where the communications platform provides a live data feed, Shadow Analytics delivers real-time dashboards and wallboards, along with configurable alerts that notify supervisors within seconds, in the application, by email, or through webhooks into the tools they already use.

Intelligence, Not Just Counting

Shadow Analytics includes the core capabilities organizations expect from call accounting, including complete call tracking, missed and abandoned call reporting, and cost and usage analysis. It then goes further with communication intelligence:

• Sentiment and transcription analytics that reveal trends and themes across recorded conversations.
• Recording search by transcript content, topic, sentiment, team, and date, with playback, download, and bulk export.
• Traffic analysis that highlights peak periods and caller patterns to support staffing and planning.
• AI-assisted report building that turns a plain-language request into a report the whole organization can use.

And for teams that want to take AI even further, Shadow Analytics works with the AI tools they already use.

Bring Your Own AI

For organizations that already use AI at work, Shadow Analytics goes a step further. Through the Shadow Analytics MCP connector, the AI assistant your team already relies on, whether that's ChatGPT, Claude, Copilot, Gemini, or another MCP-compatible assistant, can work directly with your communications data, securely and within the permissions you set.

By combining the depth of the Shadow Analytics data lake with the reasoning power of your chosen AI, teams can produce:

• Granular, complex reporting that goes well beyond standard report templates.
• Forecasts built on historical trends across your communications ecosystem.
• Executive summaries that turn large volumes of data into clear, decision-ready insight.
• Detailed breakdowns of performance across teams, channels, and time periods.

Connecting is simple. Users create their own connector from within Shadow Analytics, and connect it to the AI assistant of their choice, with step-by-step setup guidance for leading platforms. Administrators stay in control: role-based permissions decide who may use AI connectors, every user's existing data restrictions still apply, and each AI request is recorded in the audit log. Your AI, your data, your rules.

Governance That Scales

An ecosystem-wide view is only valuable when the right people see the right information. Shadow Analytics uses role-based access to control which features, reports, and records each user can reach. A built-in audit log records activity across the platform, and scheduled reports are delivered automatically by email, webhook, SFTP and MCP, with date ranges that update themselves for every run.

The Bottom Line

Communications ecosystems will keep growing, with more channels, more platforms, and more data. The organizations that succeed won't be the ones with the most dashboards. They'll be the ones that can see every conversation in one place and act on it with confidence.

That is what Shadow Analytics was built to do.

Ready to see your entire communications ecosystem in one view?
Contact RSI to schedule a demonstration of Shadow Analytics.

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RSI Video Collection: Meet Ryan - AI-driven Communication Intelligence Analytics - Shadow Analytics

 

Shadow Analytics unifies every call, message, recording, customer journey and AI-driven interaction into one platform — turning fragmented communication data into clear insights, confident decisions and measurable business outcomes.

Connect Shadow Analytics directly into your AI tools and agentic workflows with our new MCP integration layer. AI agents and assistants — including Claude, ChatGPT, Microsoft Copilot, and custom in-house agent frameworks — can query call volumes, agent performance and customer journey data on demand, without a custom integration project.

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RSI Video Collection: Meet Sy - A call center manager needing immediate help from Shadow Analytics MCP

A Day in the Life of a Call Center Manager, Made Easy with Shadow Analytics MCP

Meet Sy, a call center manager who just watched their queue wait times spike out of nowhere. Ten minutes ago, everything was calm. Now calls are stacking up, agents are scrambling, and Sy's phone is buzzing with questions from leadership. This is the moment that used to mean scrambling for a login, hunting through report folders, and waiting on IT for a custom pull. Not anymore.

Instead, Sy just asks Claude, plain and simple: "What's driving the spike in queue wait times right now?" Within seconds, Shadow Analytics pulls live data straight from the realtime dashboard feed, showing exactly which queue is overloaded and how long callers have been holding. Sy follows up: "Show me what happened on the longest-held call in that queue." Claude pulls up the full call journey, tracing every leg, the IVR prompts, the hold, the transfer, the agent who finally picked it up so Sy can see precisely where the breakdown happened.

With the root cause in hand, Sy asks one more thing: "Set up an alert so I know the next time this queue crosses five calls waiting." Done, in one sentence, no wizard to click through, no forms to fill in.

By the time leadership calls back, Sy already has the answer, backed by real data, delivered in plain English, with a monitoring rule in place so this doesn't sneak up again. What would have taken twenty minutes of digging through menus took under two.

That's the whole idea behind Shadow Analytics MCP: immediate, natural-language answers to the questions call center managers actually ask all day, pulled straight from the data that already lives in the platform. No new dashboards to learn, no reports to schedule and wait on. Just a conversation, and an answer, exactly when it matters most.

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RSI Releases Modern Shadow Analytics AI-driven Communication Intelligence Platform for Call Center and Unified Communications Providers

OSHAWA, Ontario, Canada — September 16, 2026 —  Resource Software International Ltd. (RSI), a communication intelligence provider serving enterprises across RingCentral, Microsoft Teams, Cisco Webex, Zoom, Intermedia, and other leading unified communications and contact center platforms, today announced the evolution of its flagship Shadow Analytics platform from a traditional call accounting system into a full AI-driven communication intelligence platform.

The shift reflects a broader change in how organizations communicate. Voice now shares the stage with messaging, video, SMS, and AI agents handling interactions of their own, and most enterprises run more than one communications platform at a time — often from more than one vendor. RSI says that growing complexity is exactly why call accounting, while still a necessary component, could no longer stand on its own as the full picture of an organization’s communication ecosystem.

“For decades, call accounting answered one question: what happened on this call,” said Rito Salomone, President of RSI. “That’s not the question our customers are asking anymore. They want to know what’s happening across every platform they run, every channel their customers use, and increasingly, how their AI agents are performing alongside their human ones. Shadow Analytics had to become communication intelligence because that’s what the job actually is now.”

Expanded Capabilities

As part of the evolution, Shadow Analytics now brings together:

• Pattern intelligence and trend-line analysis — surfacing traffic patterns and peak periods before they become operational problems, not just after.
• Omnichannel analytics — measuring voice, messaging, video, SMS, and AI-agent activity together rather than as separate, disconnected exports.
• Real-time alerting alongside historical dashboards — so supervisors see problems as they happen without losing the longer-term trend view.
• Cradle-to-grave call journey reporting across voice and data channels, with recordings and transcripts synchronized to the full interaction timeline.
• AI Builder, which turns complex reporting requests into plain-language conversations, producing custom reports without a technical report-writing queue.
• Shadow Analytics MCP, connecting the platform directly to the AI tools and agentic workflows organizations already use, via the open Model Context Protocol standard, so teams can ask questions from their own AI assistant instead of logging into a separate portal.
• Agentic AI performance comparison against human agents, as AI increasingly handles live customer interactions alongside people.
• Multi-platform, multi-vendor support, unifying reporting across organizations running multiple platforms and one to several UC or CC vendors side by side.
• Ecosystem health monitoring, tracking adoption and risk signals across the full communication environment rather than a single system.
• Scheduled, automated delivery of reports and dashboards directly into the tools and inboxes teams already use.
• A unified data lake underpinning all of it — a single foundation for every form of communication data, replacing the patchwork of channel-specific exports most organizations rely on today.

AI Optional, By Design

RSI emphasized that the platform’s expansion into AI-driven capabilities is not mandatory for customers who prefer traditional reporting. Shadow Analytics includes a master configuration switch that fully disables AI tooling for organizations that want the platform’s reporting depth without any AI involvement.

“Not every organization wants AI in the loop, and that’s a legitimate choice, not something we try to talk customers out of,” Salomone said. “We built a switch that turns all of it off and hands you the same powerful traditional reporting engine everything else is built on. Communication intelligence should be a choice, not a requirement.”

About RSI

Resource Software International Ltd. (RSI) has been building communication intelligence solutions since 1990. The company’s Shadow Analytics, Shadow Agent, and Shadow Analytics MCP products serve enterprises across RingCentral, Microsoft Teams, Cisco Webex, Zoom, Intermedia, Mitel, and other major unified communications and contact center platforms. RSI is a RingCentral ISV Premier Partner and a Cisco Preferred Technology Partner. The company is headquartered in Oshawa, Ontario, Canada.

For more information, visit www.rsicloud.com or www.telecost.com.

Media Contact

Rito Salomone
President, RSI
Email: rsalomone@rsicloud.com
Phone: +1 905 576-4575
Web: www.rsicloud.com

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From Call Accounting to Communication Intelligence: Why RSI Shadow Analytics Evolved

 

In an era of agentic AI, communication intelligence has gotten a lot more complicated. It used to be simple: count the calls, track the cost, pull a report when someone asked. That was call accounting, and for a long time, it was enough.

It isn’t anymore. Voice now shares the stage with messaging, video, SMS, and AI agents that place and handle interactions of their own. Organizations run more than one platform at a time — a UC system here, a contact center platform there, sometimes two vendors covering the same function during a migration that never quite finishes. The ecosystem got more diverse, more dynamic, and a lot broader than the phone system it used to be.

Managing that complexity is genuinely hard. And that’s exactly why call accounting — still a real, necessary component — had to evolve into something bigger: communication intelligence across the whole corporate ecosystem, not just the phone line.

What That Evolution Actually Looks Like

Pattern intelligence and trend-line analysis. Knowing what happened on a given day matters less than knowing the shape of things — when you’re actually busiest, where call volume is coming from, which patterns are building before they become a problem. That’s a shift from a report you pull to a pattern you can see coming.

Omnichannel analytics. Calls were never the whole story, and pretending otherwise gets harder every year. The direction here is measuring voice, messaging, video, SMS, and AI-agent activity together rather than as a pile of separate exports — worth flagging that this view is still an early look for us, running on sample data while we finish bringing it to production, not a shipped feature yet.

Alerts, real-time and historical dashboards. A queue backing up or abandon rates spiking needs to reach a supervisor in seconds, not show up in Monday’s report. Real-time alerting paired with dashboards that also hold the historical view means you’re not choosing between knowing now and understanding the trend.

Cradle-to-grave call journey reporting, across voice and data channels. “What actually happened on that interaction” is still one of the hardest questions to answer fast in most environments. A full leg-by-leg journey — queues, transfers, holds, recordings, transcripts — turns that into a lookup instead of a reconstruction project.

Complex reporting refined into natural language. This is where AI Builder comes in: describe the report you need in plain language, refine it in conversation, and save it as something your whole organization can run — instead of waiting on a custom report request to work its way through a queue.

Agentic AI performance vs. human performance. As AI agents start handling real interactions alongside human agents, “how are they actually doing, side by side” becomes a real operational question, not a hypothetical one. We’re building toward that comparison now — like omnichannel, it’s currently an early preview running on sample data rather than a live production view, and we’ll say clearly when that changes.

Multi-platform and multi-vendor blends. RingCentral in one division, Microsoft Teams in another, a legacy platform still running somewhere in between — that’s normal now, not an edge case. Communication intelligence has to answer the same questions the same way regardless of which vendor handled the interaction, with the honest caveat that depth of detail can still vary by what each platform actually publishes.

Ecosystem health check. Not “is the call center okay” but “is the ecosystem okay” — adoption, risk signals, and early warning across everything running, not just the piece that used to be the phone system.

Scheduled, automated delivery. Intelligence that only lives inside a portal only helps the people who remember to log in. Scheduled, integrated delivery gets the right report to the right inbox, dashboard, or system on its own.

A data lake for any form of communication data. Underneath all of it is one foundation instead of a dozen disconnected exports per channel — the thing that makes every capability above possible at all, instead of one more silo to reconcile by hand.

Why Shadow Analytics Is No Longer Just a Call Accounting System

Put that list together and the answer is obvious: a tool built to count calls on one phone system was never going to cover pattern intelligence, omnichannel activity, agentic AI comparisons, and multi-vendor ecosystem health. So Shadow Analytics didn’t stay a call accounting system with some extra features bolted on. It became what the environment actually needed — an AI-driven communication intelligence platform, with call accounting as one component inside something much larger, not the whole product anymore.

AI Optional, By Design

Not everyone wants AI in the loop, and that’s a legitimate position, not a gap to talk someone out of. For organizations that want the full depth of reporting without any AI tooling involved, Shadow Analytics has a master configuration switch that removes access to the AI tools entirely and falls back to our traditional, powerful reporting engine — the same engine communication intelligence is built on top of, available entirely on its own terms.

Curious what communication intelligence looks like for your own ecosystem? Let’s talk.

Contact Us

www.rsicloud.com

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RSI Executives Attending WebexOne 2026

RSI leadership will be attending WebexOne 2026, taking place October 5–8 at the Fairmont in Austin, Texas. We’re going to listen, learn, and spend time with the community building the future of Webex.

What WebexOne Is

WebexOne is Cisco’s flagship event for its collaboration and customer experience ecosystem, and this year it’s billed as the biggest AI event focused on customer and employee experience. The 2026 program runs hybrid — in person in Austin or virtual — with more than 200 hours of keynotes, breakout sessions, hands-on technical labs, and partner and customer roundtables covering where AI is headed across collaboration, contact center, and workplace technology. The week closes out with a keynote fireside chat featuring Tom Brady on leadership and resilience, which is as good a reason as any to still be in the room on day four.

Why RSI Is There

RSI has been a Cisco Preferred Technology Partner for years, building Shadow Analytics and Shadow Agent specifically for Cisco and Webex Calling — both premise and cloud deployments. That relationship is exactly why showing up matters. WebexOne is where Cisco lays out what’s actually coming next for Webex Calling and Webex CC, and where the partners, customers, and engineers shaping that roadmap are all in the same rooms and hallways for four days. For a partner that builds directly on top of the platform, that access is worth more than a sponsorship line item some years.

Our executive team will be there to sit in on the roadmap sessions, hear directly from the Webex product and engineering teams, and catch up with the partners and customers we already work alongside in the Webex ecosystem.

No Booth to Find — Just Reach Out

Since we’re attending rather than exhibiting this time, there’s no Vendor Showcase table to swing by. If you’re going to be in Austin and want to compare notes on Webex Calling analytics, contact center reporting, or where AI fits into your own communications stack, reach out ahead of time — RSI leadership will be around all week and happy to grab coffee or find time between sessions.

About RSI

RSI (Resource Software International Ltd.) has been building communication intelligence solutions since 1990 — more than three decades bridging the gap between what UC and CC platforms offer out of the box and what teams actually need to run, report on, and improve their operations. As a Cisco Preferred Technology Partner, RSI builds Shadow Analytics and Shadow Agent natively for Webex Calling, alongside support for RingCentral, Microsoft Teams, and other major UC platforms.

Heading to WebexOne 2026 yourself? Let us know — we’d like to say hello.

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RSI Sponsoring the RingCentral Tech Summit in Manila, Philippines

We’re proud to announce that RSI will be attending, sponsoring and presenting at RingCentral Tech Summit 2026, taking place November 5–7 at The Westin Manila. We’re going as a sponsor, and we’re bringing the full Shadow portfolio with us.

What Tech Summit 2026 Is

RingCentral’s Tech Summit brings together the people who build, sell, and support RingCentral solutions every day — Solutions Engineering, Professional Services, CSM leadership, and Contact Center Sales teams from across the globe, alongside RingCentral executives and partner leadership. It’s a room full of the people closest to RingCentral customers, which makes it exactly the right place for an ISV partner to show up and show what’s possible.

As a RingCentral ISV Premier Partner since 2017, RSI is honored to be part of it.

What We’re Bringing: The Shadow Portfolio

We’ll have a Vendor Showcase presence throughout the event, plus dedicated breakout sessions, where we’ll be demonstrating the full Shadow lineup:

Shadow Analytics — our AI-driven communication intelligence platform, unifying voice, messaging, video, SMS, and AI agent activity into one reporting foundation instead of a dozen disconnected exports.

Shadow AgentCX — the CX-lite layer that gives RingEX and RingCX queues real call-center functionality — break codes, dispositions, leaderboards, intelligent callback — without requiring a full CX license.

Shadow Analytics MCP — our newest release, and one we’re especially excited to walk people through in Manila. It connects Shadow Analytics directly to the AI tools teams already use, so instead of building a report, you ask a question in plain language and get an answer, grounded in real data and governed by real permissions.

Together, that’s live demonstrations of call analytics, CX tooling, and agentic AI access — all built specifically for the RingCentral ecosystem, running side by side.

Why We’re Excited About This One

Solutions Engineers and Professional Services teams are the ones in the room with customers when the hard questions come up — what’s possible on RingEX, what a queue actually needs, what a customer’s data can tell them if someone would just ask it the right way. Manila is a chance to put the Shadow portfolio directly in front of the people best positioned to put it to work, and to hear firsthand what they need next.

About RSI

RSI (Resource Software International Ltd.) has been building communication intelligence solutions since 1990 — more than three decades bridging the gap between what UC and CX platforms give you out of the box and what teams actually need to run, report on, and improve their operations. We’ve been a RingCentral ISV Premier Partner since 2017, with Shadow Agent, Shadow Analytics, and Shadow Analytics MCP all built natively for the RingCentral ecosystem.

Find us at the Vendor Showcase in Manila, or reach out ahead of time if you’d like to book a walkthrough of the Shadow portfolio while we’re there.

www.rsicloud.com

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Your Next Call Center Analyst Can Be an MCP Connection to Shadow Analytics MCP

Every contact center leader has lived this cycle: something feels off — abandon rates creeping up, a queue that’s suddenly slower, a site that’s quietly underperforming — and getting a straight answer means opening three tools, exporting a few CSVs, and spending days stitching it into a story someone can act on. By the time the report is ready, the week is half over and the problem has moved.

Shadow Analytics MCP exists to collapse that cycle into a conversation. It puts an AI interface directly on top of your communications data — voice, queues, agents, messaging — so instead of building a report, you ask a question and get an answer, in your own tools, with your own guardrails. Here’s what that actually looks like in practice.

An Ecosystem Health Manager — Whatever Ecosystem You Run

Most organizations aren’t on one platform anymore. RingCentral in one division, Microsoft Teams in another, a legacy system somewhere in between during a migration. Shadow Analytics unifies queue and agent health, service levels, and usage across those platforms so the same questions get answered the same way, regardless of which vendor handled the call.

Worth being straight about here: depth of detail can vary by platform, since some metrics depend on what a given vendor publishes, and Shadow Analytics doesn’t pretend otherwise. But the core view holds steady across the board — queue performance, agent activity, service levels, and alerting run across every platform you support. You get one honest picture of ecosystem health, not a patchwork of dashboards that only tells part of the story.

Complex Call Journey Analysis, in Plain Language

“What actually happened on that call?” is one of the hardest questions to answer quickly in most UC environments — who handled it, where it waited, why it transferred, why it took nine minutes. Shadow Analytics already tracks the full leg-by-leg journey (IVRs, queues, ring groups, agents, transfers, holds) synchronized with the recording and transcript where they exist. Through MCP, that same journey becomes something you can ask about directly instead of clicking through a call log — find the call, get the full story, in one pass.

Anomalies Get Caught Before They Become a Trend

Shadow Analytics already supports realtime alert rules — calls waiting above a threshold, a burst of abandons in a ten-minute window, a queue or agent metric crossing a line you define — delivered in-app, by email, or by webhook the moment they happen. What MCP adds is the ability to interrogate those events conversationally: not just “an alert fired,” but “why did it fire, what did the ten minutes before it look like, and has this queue done this before.” That turns alerting from a notification you triage into an anomaly investigation you can actually finish.

Forecasts and Projections — Ask, Don’t Build a Model

You don’t need a separate BI project to see where a trend is headed. Because the same natural-language interface sits on top of your full historical warehouse, you can ask for a trend across the last two, six, or twelve months and get a projection back in the same conversation — abandonment rate, average handle time, queue volume by site, whatever the question actually is. It’s your data, asked the way you’d ask a person, answered with the numbers to back it up.

Custom Reporting That Plugs Into What You Already Use

Shadow Analytics already lets you turn a natural-language request into a report against your own warehouse, refine it conversationally, and save it as a custom report the whole organization can run. Shadow Analytics MCP extends that same capability outward — into your own AI tools, internal BI stack, and agentic workflows — instead of keeping it locked inside one more standalone portal. If your organization already has a BI team, a data warehouse, or an internal AI assistant, this is the connector that lets your communications data join that ecosystem instead of sitting apart from it.

BYOAI: You Bring the AI, You Keep the Guardrails

This is the part most “AI-powered analytics” pitches skip: who’s actually in control of the data once an AI is asking the questions. Shadow Analytics MCP is built around bring-your-own-AI, on purpose. You choose the AI client. The data access, the roles, and the permission boundaries stay exactly where they already are — a supervisor’s assistant sees what a supervisor can see; alert authoring and sensitive cross-organization data stay gated behind the same permissions they’re gated behind today. You’re not handing your call data to a vendor’s black-box model. You’re pointing your own AI at your own governed data.

Design Your Own Data Requests

Every organization eventually hits a question no canned report was built to answer. Shadow Analytics MCP means you’re not waiting on a vendor roadmap to add that report — you describe the analysis you actually need, in your own words, and get it built against your real data, on your timeline.

Like Having Your Own AI Call Center Analyst, Data Scientist, and Manager — On Call

Put it together and the shift is real: an analyst who can pull cradle-to-grave call journeys on demand, a data scientist who can trend and project without a modeling project, and a manager who can check ecosystem health across every platform you run — available the moment you have a question, not the moment someone has bandwidth to build a report.

Executive Summaries in Minutes, Not Days

This is the payoff. Instead of piecing together queue reports, agent scorecards, and platform-specific dashboards into a Monday morning briefing, you ask for the summary — across your whole ecosystem, RingCentral and Teams and everything else you run — and it comes back as an answer, not a project. What used to take days of exporting, reconciling, and formatting now takes as long as it takes to type the question.

A Few More Places This Goes

A couple of angles worth adding to the list: onboarding new supervisors faster, since they can ask the platform basic “how do I read this metric” and “what’s normal for this queue” questions instead of shadowing someone for a week. Cross-team consistency, since the same natural-language question produces the same answer whether the person asking is in operations, finance, or the executive team — no more three versions of “the numbers” showing up in the same meeting. And audit-ready accountability, since every alert rule, every saved report, and every access boundary already lives inside a permissioned, logged system — the AI layer doesn’t bypass that, it works inside it.

Curious what Shadow Analytics MCP could answer for your organization first? That’s the right first question to ask — reach out to RSI and we’ll show you. 

www.rsicloud.com



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The Middle Tier RingCentral Customers Actually Need: RSI Shadow AgentCX

Most RingCentral conversations about customer experience jump straight from “standard call queue” to “full contact center license.” That’s a big jump — and for a lot of organizations, it’s the wrong one at the wrong time.

Think about who actually lives in between those two extremes: a regional bank with three branch offices that need basic call handling and accountability, not a full CX seat, for each teller line. A healthcare group whose front-desk queues need disposition tracking and service-level visibility, but nowhere near the complexity of an omnichannel contact center. A distributed SMB with a five-person support queue that’s outgrown “just answer the phone” but isn’t ready to sign a contract for enterprise contact center licensing. A SOHO operation that needs one supervisor to see queue health in real time without standing up a full CX deployment to do it.

None of those are edge cases. They’re the majority of the market. And they’re exactly who Shadow AgentCX was built for.

What Shadow AgentCX Actually Is

Shadow AgentCX brings CX features to standard RingEX queues without the need for CX licenses. RSI describes it plainly: it bridges the gap between RingEX/RingCX and full contact center operations, turning everyday UC queues into a smarter CX-lite workflow.

That’s a deliberate middle tier, not a stripped-down demo of something bigger. It gives supervisors and agents the practical, everyday controls that actually run a queue — without the cost, complexity, or procurement cycle of a full contact center seat.

Who This Is Built For

Organizations not ready for full CX. If your team is asking “do we really need enterprise contact center software for this queue,” Shadow AgentCX is the honest answer for a lot of them: real call-center functionality on the RingEX/RingCX licenses you already have.

Remote and branch sites. Distributed locations rarely need — or can justify budgeting for — a full CX deployment per site. Shadow AgentCX gives each location basic-to-intermediate call handling, presence tracking, and supervisor visibility without multiplying contact center license costs across every branch.

SOHO and SMB environments with limited requirements. Smaller teams need accountability and visibility — who’s on a call, who’s in wrap-up, how the queue is performing right now — without the administrative overhead of enterprise contact center tooling built for hundreds of seats.

Anyone who wants room to grow. Because Shadow AgentCX runs on RingEX and RingCX rather than replacing them, it scales with the organization. Teams can start with basic controls on a handful of queues and expand coverage, add sites, or eventually move specific queues to full contact center licensing later — without ripping out reporting or retraining supervisors from scratch.

What’s Actually in the Toolbox

This isn’t a checkbox feature or two. Shadow AgentCX (built on the same engine as Shadow Agent for RingEX) includes the controls that supervisors and agents use every single day:

• Real-time agent and queue insight — presence, service levels, and break/AUX codes (lunch, training, wrap mode)
• Disposition codes, account codes, and presence override for tracking outcomes and enforcing compliance
• Customizable leaderboards and KPIs by agent, group, queue, or department
• Intelligent queue callback for abandoned calls, plus VIP “cherry pick” prioritization for important callers
• AI-powered transcripts and intelligent call handling
• Local Heads-Up Display (HUD) for at-a-glance queue status
• Supervisor tools including silent monitor, barge, raise hand, and panic mode
• Auto logout and remote agent management — built for distributed and hybrid teams

That’s intermediate-tier depth without contact-center-tier cost or complexity.

Rich, Granular Reporting — Not Just a Live View

Basic and intermediate controls are only half the story. Pair Shadow AgentCX with Shadow Analytics or Shadow All-In-One Analytics and you get the reporting depth that remote sites and SMB operations actually need to prove ROI and manage service levels: cross-platform historical reporting, consolidated dashboards across RingEX and RingCX statuses, cradle-to-grave call journey tracking, and granular, role-based visibility down to the individual queue or department.

That’s the part a lot of “lite” tools skip entirely — they give you real-time presence and call that CX. Shadow AgentCX gives you the live queue controls and the historical, granular reporting to back it up.

Cost-Effective by Design

The value proposition is direct: Shadow AgentCX adds enhanced functionality at a fraction of the cost of call center seat licenses. For a five-line SMB queue or a branch office that needs basic accountability, that’s the difference between a CX initiative that gets approved this quarter and one that sits in a budget request for a year.

Backed by an ISV Premier Partner

Shadow AgentCX isn’t a third-party workaround bolted onto RingCentral’s API. RSI holds RingCentral ISV Premier Partner status, with Shadow AgentCX listed directly in the RingCentral App Gallery for RingEX, RingCX, and RingCentral AIR. It’s built, tested, and supported as part of the ecosystem — not around it.

If You’re Evaluating Your Next Step on RingCentral

If your organization is somewhere between “basic call queue” and “full contact center,” Shadow AgentCX is worth a serious look before you sign a contact-center-tier contract you don’t need yet — or before you leave a remote site running on nothing but a phone number and hope.

See  Shadow AgentCX in the RingCentral App Gallery, or reach out to RSI to talk through the right tier for your queues, your sites, and your budget.

www.rsicloud.com

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Shadow Analytics: Turn Voice Interactions Into Customer Insights

Every call that lands in your contact center is a small deposit of truth. The caller tells you, in their own words, what's broken, what's confusing, what made them angry, and — every so often — what made their day. Then the call ends, the recording gets filed away, and in most centers that truth is never touched again. A QA lead might sample two or three percent of calls a month. The other ninety-seven percent simply evaporates.

That's the gap Shadow Analytics closes. Not by asking your team to listen more, but by having the platform listen to everything — every recorded interaction, transcribed, scored, and summarized automatically — and handing your supervisors the patterns instead of the audio.

From audio file to searchable text

The first problem with voice is that it isn't data. You can't filter a recording, you can't chart it, you can't search it for a phrase without pressing play and waiting. Shadow Analytics' Interaction Insights and Query Recordings pages start by solving exactly that: every recorded call is transcribed, turning a sound file into text you can query the same way you'd query a spreadsheet. Type a keyword, a product name, a competitor's name, or a phrase like "cancel my account," and every call where a customer said it is right there — searchable across transcript, summary, and topic, filterable by sentiment, agent, queue, and date range.

Sentiment, at the scale of every call

Once a call is text, it can be scored. Shadow Analytics assigns a sentiment reading to each recorded interaction, then rolls that reading up into trends — by agent, by queue, by day, by hour. A single angry call is a data point. A queue whose average sentiment has been sliding for two weeks is a warning your team would otherwise only discover when the complaints started arriving. Because the scoring runs on every transcribed call rather than a hand-picked sample, the trend line is honest — it isn't shaped by which calls a QA reviewer happened to pick that week.

Word clouds that surface what customers are actually saying

Ask a supervisor what customers have been calling about this week and you'll usually get an educated guess, built from whatever complaints happened to reach their desk. Shadow Analytics answers the same question by looking at the words themselves: a keyword word cloud built from the transcripts, weighted by frequency, so the topics customers are actually raising — a billing change, a shipping delay, a confusing menu prompt — visibly bubble to the top before they've been escalated by a single soul. It's the difference between hearing about a problem and watching one form.

Summaries that save the scroll

Nobody has time to read a full transcript for every call that matters. So each interaction also gets a short, automatically generated summary — what the call was about, what happened, how it resolved — sitting right alongside the sentiment score and the topic tags in the drill-down grid. A supervisor triaging a spike in negative sentiment can scan twenty summaries in the time it used to take to listen to one full call, then open the transcript or the recording itself only for the ones that warrant a closer look.

The real payoff: turning conversations into performance and CX metrics

Transcription, sentiment, word clouds, and summaries are the raw material. The value shows up once you aggregate them:

Agent performance stops being guesswork. Instead of coaching from a handful of monitored calls a month, a supervisor can see an agent's sentiment trend across every call they've handled, spot the specific interactions that pulled the average down, and open the transcript to see exactly what was said — turning a vague "your customers seem unhappy" into a specific, coachable moment.

Queue health gets a customer-experience layer that queue-timing metrics alone can't provide. Two queues can have identical average handle time and service level, and completely different sentiment trends — one is a queue that's technically fast but leaving customers frustrated, and now you can tell them apart.

Customer experience monitoring becomes continuous rather than a quarterly survey. Sentiment trends and emerging topics are visible day by day, which means a product issue, a confusing new IVR prompt, or a billing change that's driving complaints gets caught while it's still small — not after a month of accumulated frustration shows up in a churn report.

Root-cause investigation connects straight back to Call Journey — once a concerning call is surfaced through sentiment or a trending keyword, its full path (queue, transfers, hold time, the agent who handled it) is one click away, synced with the recording and transcript, so "what happened" never requires a guess.

Coverage, not sampling

The underlying shift is simple to state and easy to underestimate: your contact center goes from reviewing a sample of conversations to understanding all of them. Every call becomes a data point instead of an anecdote, and the patterns that used to take a QA team weeks to notice — a sliding sentiment trend, a new complaint topic, an agent who needs a specific kind of coaching — surface on their own.

Your customers are already telling you what's working and what isn't, call after call. Shadow Analytics just makes sure someone — or something — is finally listening to all of it.

Learn more at rsicloud.com or call +1 905 576-4575.

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Modern Call Center Analytics: How Shadow Analytics Pattern Intelligence Catches What Your Team Misses

 

Your call center already told you something was wrong. The question is whether anyone was listening.

Somewhere in last Tuesday's call volume, there was a spike nobody flagged. Somewhere in the last three months of average handle time, there's a slope quietly bending upward. Somewhere in your ecosystem, right now, something is drifting off course — and if you're finding out about it in a weekly report, you're finding out too late.  Is it agentic or human resources?  A new business rollout?  Seasonal change?  Growth?

This is the problem Pattern Intelligence, part of Shadow Analytics, was built to solve.

The Problem With Weekly Reports

Most call center reporting works the same way: data happens, someone builds a report, someone reads the report, someone reacts. Every one of those steps takes time — and time is exactly what turns a small anomaly into a real problem. By the time a spike shows up in Friday's PDF, it's not news. It's history.

Pattern Intelligence skips the wait. It's built to notice things while they're happening, not after they've already cost you a customer, a shift's worth of overtime, or a bad review.

Two Kinds of Patterns: The Spike and the Slope

Not every problem looks the same, so Pattern Intelligence is built to catch two very different shapes in your data.

The spike. This is the anomaly — the sudden, out-of-nowhere jump in call volume, wait time, or abandonment that doesn't match anything around it. Spikes are urgent precisely because they're sudden. Shadow Analytics flags the outlier the moment it happens, not the next time someone happens to glance at a dashboard.

The slope. This is the trend — the slow, easy-to-miss drift that never look alarming on any single day, but adds up over weeks. Rising average handle time. A queue that's gradually running hotter every month. Slopes don't announce themselves. Pattern Intelligence tracks demand and performance over time and surfaces where the line is actually headed, not just where it's been.

Between the two, you get something most teams never have: a system that catches both the emergency and the slow burn.

An AI That Never Clocks Out

Here's the part that makes this genuinely different from a dashboard you have to remember to check: Pattern Intelligence doesn't wait for you to look.

Think of it as an unattended watchdog — always alert, always on, running a real-time health check across every channel in your ecosystem whether anyone's watching or not. Adoption issues, risk signals, outages — it's built to catch them as they surface, not at the next scheduled review.

And when you do want to dig in, you're not stuck writing a custom report or waiting on an analyst. You ask, in plain language, and Shadow Analytics answers — like having a real-time consultant on call for deep, granular analysis. No custom SQL. No ticket to IT. Just a question and an answer. That's the whole idea behind Conversational Analytics: your data already knows what happened. Now it can just tell you.

Taken together, that's what modern communication management looks like — less time spent building reports, more time spent acting on what they say.

Why It Matters

The business case for catching patterns early isn't complicated. Every spike you catch in the moment is a spike you didn't have to explain after the fact. Every trend you see coming is a staffing decision you get to make proactively instead of reactively. Every anomaly resolved quickly is a customer who never noticed anything was wrong in the first place.

Teams using Pattern Intelligence tend to see it show up in three places:

• Lower costs — fewer emergency staffing scrambles, less overtime spent reacting to problems that could have been caught earlier
• Faster decisions — staffing and scheduling calls made on what's happening now, not on what happened last week
• Better customer experience — problems resolved before they reach the customer, instead of after

The Bottom Line

The pattern is usually already there. The spike, the slope, the slow drift toward a problem — your data is telling you before your customers do. The only question is whether something is watching closely enough, and constantly enough, to catch it.

That's the job Pattern Intelligence does. Unattended. In real time. So you don't have to.

Better insights. Better decisions. Better outcomes.

Learn more at rsicloud.com or call +1 905 576-4575.


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Stop Digging. Start Asking. Introducing Shadow Analytics MCP

 

AI-driven communication intelligence, now available to the AI assistant you already use.

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Every contact center leader knows the feeling. The question is simple — "How did Tier-2 support actually perform last quarter, and what changed?" — and the path to the answer is not.

You pull a queue report. Then an agent report. Then you export it, pivot it, cross-check it against sentiment scores, and try to remember whether the dip in week six was a staffing issue or a bad release. Two hours later you have a number. What you wanted was an explanation.

That gap between having data and understanding it is the problem Shadow Analytics has always worked on. Today we're closing it further.

What we built

Shadow Analytics MCP is a Model Context Protocol server for the Shadow Analytics platform. In plain terms: it lets an AI assistant talk directly to your communication intelligence — securely, in real time, using nothing but a question.

No dashboard hunting. No export-and-pivot. No BI ticket sitting in someone's backlog. You ask, and the analysis comes back written like an analyst wrote it.

What you can ask it

Point it at any slice of your ecosystem and it goes to work:

Any window of time. A single shift, a bad Tuesday, a quarter, a full year. Time is just part of the question.

Any part of the org. One team, one queue, one agent, or the whole ecosystem side by side.

Any single interaction. Right down to one call, followed end to end.

And what comes back isn't a table — it's intelligence:

• Call journey — one call, cradle to grave. Every hop, hold, transfer, and touchpoint across the ecosystem, assembled into a single continuous timeline instead of five disconnected reports you have to stitch together yourself.
• Pattern intelligence — the behaviors, cycles, and trends that don't announce themselves in a dashboard, because no one thought to build that view.
• Anomaly detection — what broke, when it broke, how far it deviates from normal, and whether it's still happening.
• Performance insight — agent, queue, and ecosystem health, measured against each other rather than in isolation.
• Customer experience — sentiment and CSAT read across real interactions, not just the surveys that happened to come back.
• Executive summary — the health of your communication ecosystem at a glance: where things stand, what moved, and what needs attention, written up so you could forward it to your leadership team without editing it first.

Why MCP matters here

Most analytics tools ask you to come to them. You log into their interface, learn their filters, and work within the questions someone else anticipated.

MCP inverts that. It's an open standard for connecting AI assistants to real systems and real data, which means Shadow Analytics stops being a destination and becomes a capability — available wherever your team already works and thinks. The tool comes to the question, instead of the question waiting for the tool.

Practically, that changes who gets to ask. A supervisor who would never open a BI platform will absolutely type "which of my agents is trending down on sentiment this month?" into a chat window. That's the unlock.

What this looks like in practice

You ask: "Compare Tier-1 and Tier-2 for Q2. Flag anything unusual and tell me what it cost us."

You get: A written analysis identifying a mid-quarter handle-time spike isolated to one queue, the sentiment decline that tracked alongside it, the agents most affected, the probable driver, and a short list of recommended actions — with the supporting numbers underneath.

Or go the other direction entirely — from the whole ecosystem down to a single call:

You ask: "Trace the call from the customer who escalated on Tuesday. Where did it go wrong?"

You get: The full journey, cradle to grave. Where it entered, how long it waited, who touched it, every transfer and hold, where sentiment turned, and the moment the experience broke.

That second question is the one that used to be nearly impossible. A call that crosses an IVR, two queues, three agents, and a transfer leaves its story scattered across systems that don't naturally talk to each other. Call Journey reassembles it into one timeline.

The difference isn't speed alone. It's that follow-up questions are now free. Why? Since when? Is it still happening? Show me the same thing for last year. Analysis stops being a report you commission and becomes a conversation you have.

Available now

Shadow Analytics MCP is built for the RingCentral ecosystem and available now.

If you're heading to the RingCentral Denver Tech Summit, August 17–19 at the Hyatt Regency DTC, come see it live — bring your hardest question about your own contact center and we'll point it at the data in front of you.

Better insights. Better decisions. Better outcomes.

www.rsicloud.com

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RSI Launches Shadow All-In-One Analytics for Intermedia Unite®

Oshawa, Ontario — May 27, 2026 — Resource Software International Ltd. today announced the launch of Shadow All-In-One Analytics for Intermedia Unite®. The new solution extends RSI’s Shadow analytics platform to organizations using Intermedia’s cloud communications services, helping businesses transform everyday call activity into clear operational intelligence.

Intermedia Unite

 is positioned as a unified communications platform that brings together business phone service, video conferencing, chat, SMS, file management, and other collaboration tools in a cloud-based communications environment. (intermedia.com) With Shadow All-In-One Analytics, RSI gives Intermedia customers a more complete view of how that communications environment is being used, where service gaps may exist, and how teams are performing over time.

“Modern businesses do not just need a phone system. They need visibility,” said RSI. “Shadow All-In-One Analytics gives Intermedia Unite and Elevate customers the ability to understand call activity, user performance, missed opportunities, traffic patterns, and service quality in a way that supports better decisions.”

Turning Intermedia communication data into business intelligence

Shadow All-In-One Analytics is designed for organizations that need more than basic call history. It provides both real-time dashboards and historical reporting, helping managers, owners, IT teams, and service providers understand what is happening across the business communications environment.

The platform helps answer practical questions such as:

How many calls are being missed?
Which users, departments, hunt groups, or locations are handling the most traffic?
Where are customers waiting too long?
Are call volumes changing by day, season, campaign, or business unit?
Which employees or teams may need additional staffing, training, or process support?
Are communication resources being used efficiently?

Key Capabilities

Real-Time Communication Dashboards

Shadow All-In-One Analytics provides real-time operational views that help businesses monitor live activity across Intermedia Unite / Elevate environments. Managers can see current call activity, user activity, queue or group behaviour, and key service indicators without waiting for end-of-day reporting.

Historical Call and User Reporting

The platform captures and organizes historical communication activity so organizations can analyze trends over days, weeks, months, quarters, or custom business periods. This helps support long-term planning, staffing decisions, performance reviews, customer experience analysis, and cost optimization.

Call Journey Visibility

Shadow helps businesses follow the path of communication activity across users, extensions, hunt groups, voicemail, endpoints, and business units. This helps identify where calls are answered, transferred, held, missed, or routed away from the intended destination.

Missed Call and Response Analysis

Missed calls often represent missed revenue, customer frustration, or operational gaps. Shadow reporting helps identify where missed calls are occurring, when they occur most often, and which teams or locations may require attention.

Hunt Group and Department-Level Reporting

For businesses using call groups, departments, branch locations, or shared answering teams, Shadow provides reporting that helps compare performance across business units. This is especially valuable for organizations that want contact-center-style visibility without necessarily operating a full contact center.

Scheduled Reports and Executive Distribution

Reports can be scheduled for automatic delivery to managers, executives, users, or operational teams. This allows different stakeholders to receive the information they need without logging into the platform every day.

Quality of Service and Traffic Insights

Shadow can help organizations review communication traffic patterns and service quality indicators, giving IT and operations teams better visibility into potential network, endpoint, or usage concerns.

Built for Businesses and Service Providers

Shadow All-In-One Analytics is designed to support both direct business users and service providers managing multiple customer environments. For partners, MSPs, and telecom providers, the platform creates an opportunity to deliver more value around Intermedia Unite deployments by adding reporting, analytics, and business intelligence services.

For businesses, the benefit is straightforward: clearer visibility into the communication ecosystem they already depend on.

Better Insights. Better Decisions. Better Outcomes.

With the launch of Shadow All-In-One Analytics for Intermedia Unite / Elevate, RSI continues its mission to help organizations turn communication data into measurable business value.

By combining real-time dashboards, historical reporting, scheduled delivery, and actionable communication analytics, Shadow gives businesses the visibility they need to improve service, manage teams, reduce missed opportunities, and make better decisions.

Availability

Shadow All-In-One Analytics for Intermedia Unite / Elevate is available from RSI. Businesses and partners can learn more by visiting:

https://www.telecost.com

About Resource Software International Ltd.

Resource Software International Ltd. is a Canadian software company specializing in communication analytics, call accounting, reporting, and business intelligence solutions for cloud and enterprise communications platforms. RSI’s Shadow product family helps organizations improve visibility, accountability, and decision-making across their communication ecosystem.

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