Every sales call your team makes is a goldmine of intelligence — churn signals, competitive threats, product gaps, coaching moments. The problem? For most revenue teams, that signal dies the second the call ends. Reps move on, CRMs stay stale, and operators are left chasing data that was right there in the conversation.
That’s the gap Attention was built to close. In this RevOps Co-op live demo, Jacob Fleisher, Head of Sales at Attention, walked through exactly how modern go-to-market (GTM) teams are turning raw conversation data into automated, actionable intelligence — without adding headcount or duct-taping tools together. Aliza Kogan, who hosts for RevOps Co-op and carries a quota herself, brought a practitioner’s lens to the conversation throughout.
If you’re tired of knowing the answers are in your sales calls but not being able to operationalize them, this one’s for you.
“Your customer interactions should be the single best data set at your organization — but it can be really challenging to get the right insights at scale.” – Jacob Fleisher
Attention’s fundamental premise is deceptively simple: your calls and emails contain everything you need to know about your pipeline, your customers, and your team’s performance. The challenge isn’t capturing that data — it’s getting the right insight to the right person in the right system at the right time.
Fleisher framed it this way: Attention is about “what you want to know, when you want to know it, where you want to know it.” That could be as simple as automatically filling a CRM field after a call, or as sophisticated as pulling all closed-lost opportunities from the CRM, identifying the most common product gaps that caused those losses, and quantifying the revenue impact.
The platform operates in two modes. Some customers use Attention as their primary go-to-market operating layer — their entire team lives in it. Others use it purely as a middleware solution, generating insights and orchestrating data on top of existing tools without requiring anyone to adopt a new platform. Either way, everything Attention surfaces can be pushed into other systems in real time.
This flexibility is increasingly important as RevOps teams navigate AI-readiness challenges and look for tools that integrate with — rather than replace — their existing stacks.
The demo kicked off with one of Attention’s most immediately practical features: automated CRM field population. But what makes Attention’s approach distinctive is how it thinks about the type of data being captured.
Attention distinguishes between two categories of CRM fields:
This distinction matters enormously for data quality. If you’re trying to understand where an opportunity stands, you want the aggregated picture. If you’re trying to know what was agreed on the last call, you want the freshest data point.
Fields aren’t limited to the opportunity object, either. Attention can populate custom objects, accounts, contacts, and leads — all driven by natural language prompts. Building a new field is as simple as naming it, choosing the data type, setting the scope, and writing a prompt. Attention even has an “optimize” function that re-engineers your prompt to work better with its systems, so you don’t need to be a prompt engineer to get great results.
For teams struggling with CRM data hygiene and pipeline visibility, this kind of automated, structured data capture is a game-changer. One particularly powerful use case Fleisher highlighted: backfilling closed-lost reasons. Most orgs have terrible closed-lost data because reps don’t fill it in accurately. Attention can analyze all historical calls for closed-lost opportunities, identify the real reasons, populate the pick list, and then run an agent going forward to keep that data clean automatically.
Attention’s coaching functionality goes well beyond basic call recording and review. The platform dynamically applies scorecards based on what was actually discussed in a call — not just what type of call it was scheduled to be.
Here’s why that matters: if you apply a demo scorecard to every demo call, but pricing wasn’t discussed on a particular call, you’ll get zeros for pricing-related criteria. That drags down the overall score and makes the call look worse than it was. Attention solves this by using call tags — automatically applied based on what was discussed — to trigger the appropriate scorecard.
In the demo, Fleisher showed a call that was tagged as a demo, which triggered the demo scorecard. But if the AI agent builder had been discussed, an AI agent scorecard would have been applied simultaneously. The result: you’re always scoring 100% of relevant conversations with the right playbook, and the data you get is statistically meaningful.
Scorecard components are fully customizable. You can use out-of-the-box frameworks like MEDDIC or BANT, or build your own based on your specific sales methodology. Each scorecard breaks down performance by component, highlights what went well, and surfaces specific areas for improvement.
Kogan noted the performance management implications: “This is such a great visual tool for frontline managers to be able to use — whether it’s coaching people up or coaching people out. The data is always so hard to really find.”
“I can very quickly see that over the last month, clarifying questions when prospects ask about features is our weakest area. That gives me a lot of information to go and just build content around that, versus having to do this whole data collection exercise and listening to calls manually.” – Jacob Fleisher
At the team level, managers can view aggregate scoring across all reps for a given time period, identify the weakest areas across the entire organization, and drill down to individual rep performance. This is the kind of enablement intelligence that used to require hours of manual call review — now it’s available at a glance, based on 100% of conversations.
One of the most compelling capabilities Fleisher demonstrated was Attention’s Super Agent — a natural language interface that lets you query across your CRM data, call data, email data, calendar data, and any other connected systems.
The use cases here span from tactical to strategic:
That last example produced a particularly striking insight in the demo. Attention analyzed the conversation data and identified that “implementation readiness” was the strongest correlative signal the team wasn’t tracking — and that deals with clear technical champions moved two to three times faster. That kind of insight, surfaced automatically from existing data, is exactly what AI-ready revenue systems should be delivering.
The Super Agent is also available directly in Slack, so operators and reps can query their GTM data without ever leaving their workflow.
Attention’s pipeline view gives revenue leaders a deal-level command center that goes beyond what’s in the CRM. The platform analyzes calls and emails on each opportunity and generates a deal score based on variables you define as important to your sales cycle.
For Attention’s own team, that includes signals like whether there’s an active competitive evaluation, whether there’s an appetite for coaching, and other deal-specific factors. The result is a score (e.g., 57 out of 100) that gives sales leaders a clear, data-driven view of deal quality — not just rep sentiment.
Those scores can be pushed into the CRM and used to trigger alerts. For example: any deal over six figures that hits commit stage with a score below 70 automatically sends a Slack notification to the sales leader. This kind of process management automation is where Attention’s orchestration layer really shines — getting the right data to the right people without requiring anyone to manually monitor dashboards.
The section of the demo that generated the most audience interest was Attention’s agent builder. Attention offers a template library of pre-built agents — things like AE-to-post-sales handoff agents, pre-call prep agents, and follow-up email drafters — that can be deployed with a single click. But the real power is in building custom agents tailored to your specific business.
The process is genuinely accessible. Fleisher demonstrated building an agent by simply describing what he wanted in plain language. Attention asked clarifying questions, incorporated the answers, and built the agent. The whole process took about 10 minutes, with no technical background required.
One of the most compelling real-world examples Fleisher shared came from a fast-growing AI startup that scaled from 12 to 250 customer-facing employees in 18 months. Their challenge: how do you ramp 238 new reps efficiently? Their solution: the “SportsCenter Top 10” agent.
Every Friday morning, the agent analyzes all discovery calls from the week, scores them against the company’s playbook, and posts the top 10 to a Slack channel — complete with scores, key learnings, and highlight clips. New reps, eager to make a name for themselves early, started following the playbook religiously because that’s what it took to make the top 10. The result: enablement adoption shot up, and onboarding and ramp time decreased by over 60%.
“How did you actually end up on the top of SportsCenter Top 10? By following their playbooks and their enablement to a T — because that’s what the scoring was based off of.” – Jacob Fleisher
This is the kind of creative application that emerges when you give operators and sales leaders a flexible, accessible agent-building platform. As Kogan observed, this isn’t just for ops teams — individual AEs can build their own agents to surface their top accounts for the week, set reminders, or prep for upcoming calls. For high-volume teams managing large pipelines, that kind of self-service intelligence is a significant productivity multiplier.
Organizations using Attention have anywhere from 5 to over 100 agents running across their GTM motion. Each agent can be given its own context — uploaded files, operating procedures, business rules — so it operates with the right knowledge for its specific job.
During the Q&A, an audience member asked about Attention’s key differentiators versus other AI tools. Fleisher’s answer was instructive.
The core distinction is that Attention is purpose-built for revenue data. Analyzing sales conversations isn’t like analyzing a 10-K or a support ticket. A single transcript might reference pricing in a dozen different ways — “how much does this cost,” “what’s the investment,” “can we talk about budget” — and correlating those moments across thousands of transcripts requires a fundamentally different approach than general-purpose AI.
Attention has built what Fleisher calls an “ontology” or “brain” for revenue organizations — a deep understanding of how revenue conversations work, how to segment and analyze them, and how to extract meaningful signals at scale. That insights layer is what makes the orchestration layer trustworthy. As Fleisher put it: “To be effective, you need to trust that the insights are going to be world class first. So you need to nail the insights layer.”
This philosophy aligns with what the broader RevOps community has been grappling with: how to implement AI that actually solves problems rather than just adding complexity. Attention’s answer is to start with the data layer — making sure the intelligence is accurate and trustworthy — before building automation on top of it.
For teams exploring how AI fits into a modern GTM stack, Attention’s approach offers a practical model: instrument your conversations, build a reliable insights layer, then orchestrate that intelligence across your systems and workflows.
The gap between what’s said on sales calls and what ends up in your CRM has always been one of the most frustrating inefficiencies in revenue operations. Attention is making a compelling case that this gap is now closeable — not with more process or more headcount, but with AI that’s purpose-built for the way revenue teams actually work.
For RevOps and Sales Ops leaders looking to move beyond passive conversation analysis and into proactive, automated GTM intelligence, the demo made one thing clear: the technology is here, it’s accessible, and the use cases are limited only by your imagination.
Learn more about Attention’s AI Sales Agents and CRM Auto-Update capabilities to see how it fits into your GTM stack.
Check out our blog, join our community and subscribe to our YouTube Channel for more insights.
.png)
.jpg)
.jpg)
.jpg)
