Loop / Context

The context layer for revenue: one MCP call, every agent.

Deliver the right context pre-enriched with your won/loss patterns. Soevery model reads your deals, plays, and guardrails the same way.

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See how it works
THE PROBLEM

Querying connectors one by one creates fragmented context.

When a model hits each connector separately, the picture arrives in pieces. And every custom MCP script you wire by hand is one more thing your best operator has to monitor and keep alive.

WHY IT HAPPENS

Context graphs resolve relationships. They don't create judgment.

Enterprise search and knowledge graphs map what exists. But they can't tell you which patterns predict wins, because they don't train on the outcome.

How it works

Context API in three steps.

01

Unify your revenue data into one layer.

Loop reads CRM, call transcripts, emails, calendars, Slack messages, and more to trace the path each deal takes. Alongside your sales process, territories, operating rules, and rep patterns. All joined to the revenue outcome.
02

Add the judgment a context graph can't create.

A private ML model trained on your outcomes attributes the impact that champions, compelling events, comparable cohorts, and deal trajectories all create. Not just the relationships they hold, like a Notion or Glean.
03

Deliver that context to your agents and reps in one MCP call.

Any agent that speaks MCP pulls the same enriched context — no DIY servers to keep alive— and it stays put when you swap models or vendors.
Customer outcomes

Teams that ship Context API see compounding results.

—70%
token reduction
3.5x
Close rate, ml-promoted
-7d
cycle compression
"We finally have a straight line from an agent's action to the deal it moved. That conversation with our CRO went very differently this quarter."
GTM AI Lead
Defensestorm
How it compares

Fluint vs. Context Graphs & Search. Why resolving context isn't enough.

A graph carries your context. Fluint's model gives it judgment. Resolving relationships is table stakes. Predicting and compounding isn't.
What matters
Fluint
Recommended
Context graphs * search
e.g. glean, notion ai
Revenue judgement
Deal dimensions with revenue impact isolated & labeled
Map connections
Improves over time
Yes, closed loop retrains on the outcome
Re-curated when relationships change
Carries process & rules
Yes, into every workflow
Varies, mostly entities
Delivery & upkeep
One MCP call, fully managed
MCP, with DIY inputs to maintain
Model-agnostic
Yes, outlives any one model
Usually
What matters
Fluint Recommended
Context graphs & search
e.g. glean, notion ai
Revenue judgement
Deal dimensions with revenue impact isolated & labeled
Map connections
Improves over time
Yes, closed loop retrains on the outcome
Re-curated when relationships change
Carries process & rules
Yes, into every workflow
Varies, mostly entities
Delivery & upkeep
One MCP call, fully managed
MCP, with DIY inputs to maintain
Model-agnostic
Yes, outlives any one model
Usually

Questions about AI for RevOps.

What is a context API for AI agents?

A context API is a single endpoint that serves structured, enriched context to AI agents on demand. Instead of each agent querying raw data sources, it calls one API and receives decision-ready context — champions, comparable deals, risk signals — already enriched by a model trained on your revenue outcomes.

How is Fluint different from a context graph like Glean or Notion AI?

A context graph resolves relationships between entities. Fluint goes further: it trains a private model on your closed-won history to add revenue judgment — which patterns predict wins, which signals indicate risk — then serves that enriched context over MCP. Graphs map what exists; Fluint predicts what matters.

What does "model-enriched context" mean?

It means the context your agents receive has been processed by ML models trained on your revenue data. Instead of raw CRM fields, agents get labeled deal dimensions — champion strength, compelling event, comparable cohort — each scored by their actual impact on closed-won outcomes in your business.

Why use MCP instead of a custom API?

MCP is an open protocol that AI agents already speak. Using it means any agent — Claude, OpenAI, Glean, custom — can pull your context without a proprietary SDK or custom integration. One endpoint, no lock-in, and it survives model swaps.

Does the context improve over time?

Yes. Because the context layer is powered by a closed-loop model, every outcome — closed-won, lost, stalled — feeds back into training. The model retrains on new labels each cycle, so the context served to agents gets sharper automatically.

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Give every agent context worth reading.

Start free, connect your stack, and serve enriched context to your firstagent today.

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