The context layer for revenue

Prove what works, then put agents to work.

One context layer that ranks the next best action in every deal, then runs it with a proactive agent.

Start free with $300 in credits → Book a call

From signal to signed,in one closed loop.

1

Capture every signal your GTM stack produces, without 7+ connectors

CRMFields, stages, dates
CallsTranscripts, attendees
EmailThreads, latency
CalendarMeetings, responses
SlackDeal-room chatter
DocsPlans, notes
Agent runsRecs, drafts, tool calls
2

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.

AEvent timelineMid-market · 348 deals
Connecting 7 sources
CRM
Calls
Email
Calendar
Slack
Docs
Agent runs
Outcome
JanMarMayJulAug 27
event_tsdeal_idsourcefeatureoutcome
2026-07-14 09:12d_0418calendarcfo_attended = 1won
2026-07-21 16:40d_0418callspain_validated = 1won
2026-08-02 11:05d_0533emailthreads_4plus = 0lost
2026-08-27 15:02d_0601crmstage = closed_wonwon
3

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.

CPlaysvalue vs. execution
Mid-market
HIGH VALUE · RARELY RUN
LiftRun in % of deals →CFO attendedPain validatedMulti-threadedExec briefMEDDPICCSecurity review
RECOMMENDED PLAYGet the CFO into Evaluation with an exec brief+3.1ptprojected win-rate liftCloses the gap on cfo_attended· run in 22% of deals · 78% won when run
4

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.

EContextone MCP endpoint
Serving context
ATimelineBPredictorsCPlaysDAgent
ONE MCP CALLpredictorsplayscitationsagent state
Claude
ChatGPT
Copilot
Gemini
Agentforce
Glean
Notion
Slack
5

Your private model retrains daily, and pre-computes updated context

Every outcome, and whether the play was followed, falls straight back into step 2. Predictors re-rank, plays re-order, agents adjust.

FRetrainnightly · 02:00
Collecting outcomes
MODEL
v42v43
00061218
9 won5 losttoday
Predictors · v42Lift
1CFO attended+56pt
2Pain validated on call 1+31pt
4Exec brief before demo↑2+19pt
3Multi-threaded+27pt
✓Plays re-ordered
✓Agents adjusted
✓Context served over MCP
Trained on your outcomes only · private to your orgNext retrain in 24h

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.

Can it…
Context graph
Clearskies, Von, GTM.ai
Agent only
Zapier, Claude, n8n
Fluint
Context + proof + agents
Unify CRM, calls, email into one context
Entity-resolved, served to any AI
✓
◐Partly
✓
Time-series store with feature labels
Structured for model training
×
×
✓
Prove which behaviors predict a win
ML-graded lift vs. matched cohort
×
×
✓
Recommend the right next sales play
Stack ranked by projected outcome
◐Partly
◐Generic
✓
Deploy agents to run the play
Trigger, channel, rep + agent
×
✓
✓
Regression tested to retrain on outcomes
Closed-won flows back into the model
×
×
✓
Motorola neo4j Honeycomb Bullhorn Raft Bloomfire Adthena

“Reps trust the output because it cites real deals, not boilerplate. We saw it in week one.”

VP Revenue Operations · Public, $400M ARR

“The context layer was the hardest part of our agents. Fluint replaced months of work with one integration.”

Head of GTM Engineering · Series C, $85M ARR

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.
✳
A context graph stores relationships: which people, accounts and conversations are connected. Fluint stores a time series of events and trains private ML models on it to prove which behaviors predict a closed-won deal. You get ranked predictors with measured lift, not just a map of what is connected.
✳
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.
✳
$300 in credits. 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

Get $300 in credits and see your first revenue predictors and top plays inside your first session. SOC 2 Type II, SSO, private models with enterprise-grade protection.