The data architecture under every sales conversation.
Claap captures the conversation, structures it into the fields you define, and exposes them as views your CRM, your dashboards and your agents can read. A data layer, not a note-taker.
- GDPR
- SOC 2 Type II
- No training on your data
Claap for RevOps
CRM fields that fill themselves
Manual entry errors and busywork go away: Claap fills your tools from the conversation itself, so completion stops depending on rep discipline.
Reporting that runs on what was said
Fragmented interactions become structured insight your strategy, execution and forecasting can actually run on.
Playbook adoption as a number
Automated scoring shows whether your framework is adopted and executed, with feedback and analytics at scale rather than on a sample.
Completion rate is a data-entry problem. Remove the data entry.
- CRM completion rates up 60% with conversation data captured and structured automatically.
- Around 20 minutes of manual entry saved per call, per rep.
- Pipeline data and analysis expand with no extra input from sales.
Fields
Use caseForecast categoryPainClose dateSeatsDeal objectionsWhen HubSpot already has a value
Only if emptyNever touches a rep’s value
Append when possibleAdds to what is there
Always overwriteLatest call wins
Structured, accessible context. Not a note-taker.
- Native MCP connector, so Claude, ChatGPT and any agent can query Claap directly.
- Automations from Business, the open API on Enterprise.
- Retro-analyze closed-lost deals by need, missing feature or chosen competitor.
Closed lost
Missing feature
Teams dialer integration
Raised on 4 lost deals · read by your agent over MCP
The playbook runs itself, on every call.
- Automated AI call scoring against the framework you standardized on.
- Instant, actionable feedback to every rep, not just the ones a manager reached.
- Performance measured against the playbook to find coaching opportunities.
Deals
Ready for Demo · Velto
✕No decision process mapped
1/5 on the call · gate needs 3
Search finds examples. Fields count every deal.
An agent that searches transcripts reads the passages closest to your question and answers from those. That is how you find a quote. To get a number, it needs fields and views.
Fields + viewsHow many deals, which ones, how much?
Read a view. It filters every deal on its fields, so the answer is the full list, each row linked to its call.
Fields + viewsHow does this number move month to month?
Read a view. The field keeps one definition, so this quarter compares cleanly with the last one.
SearchShow me where the buyer said it
Search the transcripts. You get the exact words in context, with the moment in the call.
SearchHas anyone raised this topic before?
Search the transcripts. You can explore a topic before anyone sets up a field for it.
Search, then a fieldA new topic keeps coming up on calls
Search first, then create a field. Search shows what deserves tracking, and the field tracks it on every call after that.
Design a field your agents can count on.
A good field starts from a decision. Answer these five questions before you create one.
Which decision does it feed?
The forecast, a deal priority or a rep’s coaching. If no decision uses the field, leave it out of your views.
Which type of value?
Yes or no, a closed list, a number or a date. Anything you want to count needs a typed value; free text is for reading.
What does each value mean?
Write the definition down. "Budget confirmed" means the decision maker stated an amount on the call.
What if the call never mentions it?
Add a "Not mentioned" value. The field then records a gap your views can count, and the model has no reason to guess.
How do you know it is right?
Run the field and a second model on the same 50 calls, review each disagreement, and tighten the definition until they agree.
Five fields cover most pipeline reviews: competitor named, budget confirmed, decision maker identified, main objection, and a dated next step.
10,000+
sales reps run their deals on Claap
50%
higher win rates, typically within the first quarter
2x
faster onboarding for new reps
Frequently asked questions
What does an agent actually read over MCP?
Saved views, not raw transcripts. Rows are calls, deals or accounts and the columns are AI fields Claap already extracted, so a model can aggregate them instead of re-reading every call.
Which plans include the MCP server and the API?
Claap MCP is on every plan, including the free one. Automations come with Business and Enterprise and the Claap API with Enterprise; none is sold as an add-on. What scales between the two is the AI credit pool.
Where is the data hosted?
GDPR, SOC 2 Type II, DPA on request, and no training on your data.
Why not let an agent search the transcripts instead?
Search reads the passages closest to your question and answers from those, so a count only covers the deals it happened to read. A view filters every deal on a typed field, so the total is complete and each row links back to its call. Use search to explore and fields to count.
How do we check that an AI field is accurate?
Pick 50 calls with enough yes, no and not-mentioned cases. Run the field and a second model with the same definition, review every disagreement, and tighten the definition until the two agree on at least 90% of calls. Then confirm on 50 new calls.
Give your agents something clean to run on.
A 30-minute demo on your real pipeline, including the MCP connector and the views your agents would read.