Ask any RevOps leader what’s hiding in their sales calls, and you’ll get the same answer: everything.
Buyer objections, competitor mentions, pricing feedback, pain points, new use cases — the single richest data set in your GTM motion lives in your sales calls, emails, and messages. But 99% of it goes unused because no one has time to watch every call, take structured notes, and update the CRM.
Attention was built to fix that. Founded to bridge the gap between unstructured conversational data and structured operational systems, it’s an AI-native platform that turns every sales conversation into CRM-ready, actionable insights — automatically.
In this demo, Rory McDermott (Account Executive at Attention) walks Matthew Volm, CEO of RevOps Co-op and Eventful, through how Attention is reinventing how revenue teams collect, analyze, and act on customer conversations. The result: fewer manual updates, cleaner data, and faster decisions across the entire GTM engine.
Every RevOps leader knows that first-party data from calls, meetings, and emails is invaluable. Yet most of it remains unstructured, scattered across call recordings and note docs, disconnected from CRM workflows. Even when teams use call recording tools, it’s still up to reps or managers to manually log next steps, buyer roles, or qualification details.
That’s time-consuming, inconsistent, and impossible to scale past a handful of sellers. When your sales team hits 25+ reps, data hygiene becomes a full-time job — and that’s before you even try to act on insights.
Attention solves this by taking the unstructured chaos of conversation data and transforming it into structured, reportable, and operationally useful information.
Attention combines AI-powered conversational intelligence with a no-code automation layer, turning what used to be static call notes into live data that flows directly into your CRM, analytics, and collaboration tools.
Here’s how it works step-by-step:
Attention integrates with your existing call recorders (or provides its own) to ingest all your customer interactions — Zoom calls, VoIP recordings, emails, and more. Once connected, every touchpoint automatically syncs to the right CRM objects: contact, account, and opportunity.
Instead of dumping transcripts or call summaries, Attention uses large-language-model prompts to extract specific fields you define:
Each field can be configured as text, picklist, number, or date — meaning RevOps finally gets normalized data that fits reporting models, not random note dumps. Learn more about the CRM auto-update here.
Attention pushes those insights directly into your CRM (like Salesforce or HubSpot) — without any rep intervention.
No toggling between tabs. No “update your opp notes” Slack messages. Just clean data appearing in the right fields, every time.
The real magic happens in Attention’s automation builder, which looks like a modern iPaaS (think Zapier or Make) but is powered by conversation data as the trigger.
Teams use it to build workflows such as:
Each workflow can be templated, customized, or even built for you by Attention’s forward-deployed engineering team, who design complex automations during onboarding.
Automation is powerful, but Attention’s latest evolution — the Super Agent — goes beyond static rules.
Instead of needing predefined triggers, Attention’s AI agents can be asked to perform an analysis or task and will determine how to execute it autonomously.
Example:
A Head of Sales wants to understand if talk time correlates with win rate. Instead of pinging RevOps for a report, they simply type in Slack:
“@Attention Helper, show me the correlation between talk time and closed-won deals this quarter.”
Attention’s agent queries conversation data, CRM fields, and even connected sources like Snowflake or Airtable, then returns a complete correlation analysis — instantly.
No dashboards. No filters. No waiting.
The agent handles point-A-to-point-Z reasoning automatically, reducing time-to-insight from hours to seconds.
This same model can power more advanced scenarios:
Attention calls this focus on decision velocity: “optimizing for time to the correct decision.”
Deploying Attention doesn’t require an overhaul. Most customers go live within 1–2 weeks, thanks to out-of-the-box templates and dedicated setup support.
Here’s what onboarding looks like:
From there, Attention’s team continues to partner with you — building new agents, workflows, or integrations as your use cases evolve. Implementation includes both self-service options and white-glove onboarding with a technical CSM.
Attention integrates with nearly every major RevOps system:
This means you can automate cross-functional workflows — from creating a product bug ticket when a prospect raises an issue, to updating renewal risk fields in your customer success dashboards.
Attention is built for B2B SaaS companies running high-velocity or hybrid sales cycles, typically with 10–500 sellers.
Current customers include Horizon HQ, Abridge and Unify, all of whom use Attention to unify conversation insights, CRM hygiene, and automation in one AI-native layer.
While most conversational intelligence tools (like Gong or Chorus) focus on transcription and coaching, and most automation tools (like Zapier or Workato) require manual triggers, Attention merges the two.
It’s not just “AI-assisted automation” — it’s AI-directed action.
Attention’s true differentiator lies in its ability to interpret data, choose the next step autonomously, and execute — all in one system.
This makes Attention less of a “sales tool” and more of an AI-driven revenue operations platform, purpose-built for teams who want to automate insight generation and execution.
Attention uses a platform-based pricing model — all modules included, no feature gating. Customers get access to the full platform (conversation intelligence, automations, and agents) from day one.
Customers typically see measurable ROI within the first quarter — improved data accuracy, shorter sales cycles, and faster reporting turnaround.
Attention doesn’t just record conversations — it operationalizes them.
By unifying conversational intelligence, CRM automation, and AI-native reasoning, it’s redefining what “automation” means for revenue teams.
For operators tired of chasing reps for notes, wrestling with data discrepancies, or building manual workflows that never scale, Attention delivers a new paradigm: let AI handle the busywork, so you can focus on the revenue work.
👉 Join the conversation with the Attention team in the RevOps Co-op Community Slack Group
👉 Check out the Attention blog for more great resources
Looking for more great content? Check out our blog and join the community. See all our partners and explore more RevOps Demos.
