From signal to signed,
in one closed loop.
Capture every signal your GTM stack produces
Prove it: context backed by data science, and your revenue outcomes
Every signal joins the outcome it created. Your private models grade each behavior against closed-won, by segment, instead of an old playbook's opinions.
Do it: proactive agents run your top plays with reps
We'll recommend the highest-value, but least-executed sales plays, then let you build an agent to proactively execute those plays alongside your reps.
The right context delivered anywhere reps work and agents run
One MCP call serves the same trusted context, predictors, plays and citations, to every model, tool and agent you use. Swap vendors; the context comes with you.
Proof, not just a graph.
Action, not just context.
A relational graph shows what's connected; Fluint proves which behaviors win. A context layer alone waits to be asked; Fluint launches agents to run the plays that move those predictors.
“Fluint gave us months of AI engineering work with a single integration. Now everything we build is grounded in how we win, with the right context for every agent and seller.”
“Fluint keeps our team on our sales process and calls out the gaps in every deal. Our win rate is up, and it’s helped us 6x the efficiency of our sales organization.”
“No other platform captures the right context in fewer words than Fluint.”
You might be wondering...
Tap a question to ask Loop. More in the docs.
A context layer sits between your revenue systems (CRM, calls, email, calendar) and the AI tools your team uses. It resolves the data into one consistent, queryable picture so every model and agent works from the same facts. Fluint is a context layer that also grades that context against revenue outcomes.
Most context graphs store relationships: which people, accounts, and conversations are connected. Fluint's context graph is built on a time series of events, and Fluint trains private ML models on it to prove which behaviors predict a closed-won deal. So you get predictors ranked by measured win-rate lift, and plays to act on them.
A predictor is a behavior or signal in your deals, such as whether the CFO attended a meeting, that separates wins from losses in your own history. Fluint ranks predictors by win-rate lift versus a matched cohort, per stage and segment, and retrains as deals close.
Each predictor becomes a play with a trigger and a channel. When a deal is missing a predictor, a Fluint agent acts on it: drafts the executive brief, pings the rep in Slack, or creates the CRM task. Reps stay in the loop; the agent does the setup. Every run is measured for lift.
No. Fluint handles the ETL, builds the time-series data store, trains and retrains private models scoped to your org, and serves the results over MCP. Your weights and data are never shared with other customers.
Through a single MCP call (or REST). Add Fluint as a tool in any agent framework and it receives pre-materialized, cited context: predictors, active plays and deal evidence. No model swap, no migration.
A 14-day free trial for up to 30 sales reps, worth up to $1,250. Connect Salesforce and one call source, and Fluint builds your context layer and first predictors in a session. You can consume that context from any agent immediately.
Build your context layer, free
A 14-day free trial for up to 30 sales reps, worth up to $1,250. Connect Salesforce and one call source, and Fluint builds your context layer and first predictors in a session. You can consume that context from any agent immediately.