"Where and how do we actually win?"
You get: the winning and losing deal patterns, with the calls behind them.
Win-rate forensics
for B2B revenue teams
Your CRM says you lost on
price.
Your data says otherwise.
Every tool you own now has an agent that can analyze a deal. What they can't do is analyze your deals collectively to find why you win and why you lose. Rev-Lens joins all of your customer engagement data from across your tech stack and finds the insights you can act on this quarter.
The overview · 2:38
The answer already exists. It's spread across five systems.
A short walk through one account, five sources and the finding none of them shows on its own. The same pattern sits behind Case 01.
The evidence
The truth about a deal is spread across five systems. The CRM holds the least of it.
What the CRM recorded
Closed lost. Reason: Price.
Stage, amount, close date, and whatever a rep had time to type. It is the field your forecast, your board deck and your comp plan all read.
What actually happened
The team downsized the configuration to fit the budget and communicated it in Slack. The buyer then downloaded the competitive comparison document from your website and went quiet.
Said on a pricing call, agreed in an internal thread, sent as a revised quote. None of it reached a system of record.
CRM
Opportunity openedabove $50KAmount revised downno reason setClosed lostreason: price
Recorded calls
Budget ceiling stateddiscoveryConfiguration removed to fitpricing call
Email
Revised quote sentreduced scopeThread goes quiet21 days
Slack / Teams
Internal descope agreednever reaches CRM
Website visits
Viewed configuration pagespre-callCompetitive comparison downloadedlate stageVisits stoppost-download
Action
Change the tactic on budget-constrained deals, or defocus them
fast. Owner: sales management, to inspect these deals and coach
the reps. This quarter.
One account is an anecdote. Fourteen accounts with the same shape and zero wins is a finding. How many of the deals in your pipeline share it right now?
Beyond agents
Agents can reach every system now. They can't analyze your GTM execution.
Through MCP, the agents in your stack pull data from other systems one deal at a time. Rev-Lens joins every deal's data in one place, so the patterns across all of them can be counted.
Who brings us in
Five leaders. One set of deals. Finally the same answer.
"Which pipeline turns into revenue?"
You get: source-to-close evidence your CFO can check.
"Is the market hearing what we built?"
You get: feature gaps priced in open pipeline.
"Every cross-system question lands in my queue."
You get: the join you never had time to build, in your warehouse.
"Three executives, three dashboards."
You get: one answer, with owners, before the next plan is set.
How it runs
Five weeks from first access to decisions with owners.
Week 1
Scope and access
Agree the questions. Connect to the sources you name. Stand up one governed copy inside your infrastructure.
Weeks 2 to 4
Join and analyze
Match calls, email, threads and activity to the same accounts and opportunities. Run agents tuned to your Sales, Marketing and Product questions on the model you already license. Check every candidate finding against its source records.
Week 5
Findings and decisions
A prioritized set of findings, each traceable, each with a recommended action and a named owner. Challenge any of them.
The data
A joined, governed GTM dataset in your own warehouse. Gong exports, CRM history and threads, matched to opportunities.
The agents
Tuned to your questions and your deals, running on your LLM license. Your team keeps running them after we leave.
The decisions
Findings with owners and dates. The part no software ships.
Your data stays where it is.
- Access scoped to the sources you name
- Built inside your infrastructure, not ours
- Never pooled with another customer's data
- Never used to train models
- Nothing licensed, no platform change
- Terms in writing before access is granted
Works with what you run
We don't replace your stack. We make it answer harder questions.
Keep Gong for coaching, Salesforce for the record, your LLM for daily work. Rev-Lens lands their data in one joined model and gives you the cross-system answer none of them produces on its own.
SalesforceHubSpotGongSalesloftZoomGmail · OutlookSlack · TeamsSnowflakeDatabricksBigQueryClaudeChatGPT EnterpriseAzure OpenAI
Resources
Go deeper.
Product demo
See the console your team would use
Pick a role, open three finished reports, and watch the 75-second walkthrough. Runs in your browser.
Try the demo ↗ White paperReach Is Not Analysis
What MCP-connected agents can and cannot tell you, and where a GTM data lake fits.
Read → White paperThe Model Layer
Why GTM data is moving inside the walls, and what to decide before it does.
Read → Case study 02One feature. One year of calls. Under two hours.
2,470 calls scanned. $2.09M of open pipeline. Three named blockers.
Read →Straight answers
What buyers ask on the first call.
We already pay for Gong. Isn't this what it does?
Gong is excellent at calls, coaching and deal-level insight, and its agents can now pull context from other systems through MCP. But they work one opportunity at a time, in Gong's cloud, and they don't count patterns across your pipeline. We use Gong's export as one of our inputs. You keep Gong. For the longer version, see how Rev-Lens compares to conversation intelligence, forecasting and win-loss research firms.
Couldn't we just point Claude or ChatGPT at our systems?
For a question about one deal, yes, and you should. For a question about 400 deals over a year, connectors return summaries one call at a time, with rate limits and no join between systems. You get a cited answer, not a counted one. We build the joined dataset so your LLM can count, and you keep it.
What if our data can't answer the question?
We'll tell you on the first call. The first conversation is about whether your data supports the question, not a pitch.
Who needs to be involved on our side?
One executive sponsor, one RevOps or data contact to grant access, and an hour from each leader whose question is in scope. We don't need rep time.
What does our security team need to know?
Access limited to the sources you name, a governed copy inside your own cloud account, your licensed model, no pooling, no training. We put the specifics in writing before any access is granted.
What happens after week five?
You keep the data lake and the agents. Most teams re-run them quarterly. If you want us to stay on for quarterly reads, we can. It isn't required.
Start here
Bring the one question your stack can't answer.
A 30-minute call. We'll check whether your data can support the question. If it can't, we'll say so.
- What you'll get: a yes or no on feasibility, and the sources it needs
- What it won't be: a demo, a license, a platform change
- Who you'll talk to: the founder who runs the analysis