Loop

Predictors

Trained on your entire GTM dataset, Fluint surfaces the predictors that actually drive your deals, so you don't rely on an LLM's opinion.

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See how it works
THE PROBLEM

From a map of your data to a model of your outcomes.

Context graphs capture the relationships you already know. LLMs give you opinions on a limited set of data. Neither proves your specific win patterns with facts, like "CFO-attended deals close at 3.2×."

An LLM gives you opinions. A model gives you facts.

Why it matters

General models guess at what matters. Fluint trains on your closed-won history to prove which behaviors and signals actually predict wins — with statistical significance, not a prompt.

How it works

Private Model in three steps.

01

Learn from your entire GTM dataset.

Gradient-boosted and regression models train on your deal history, to learn which behaviors and signals predict revenue with hard facts (not opinions).
02

Surface predictors on its own.

When a new signal like cfo_attended starts predicting wins, the model finds the statistical significance and promotes it to a top predictor and shows the lift.
03

See deal health and its trajectory, live.

Every deal gets a trajectory and health assessment, compared to the patterns from deals that already closed.
CUSTOMER OUTCOMES

Teams running the model see compounding results.

3.2×
CFO-attended close lift
Auto
Predictor promotion
Live
Deal trajectory scoring
"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 chat, skill, agent and seller."
Quincy L, GTM AI Lead
Defensestorm
HOW IT COMPARES

Fluint vs. knowledge and context graphs. Why graphs aren't enough.

Graphs map relationships. Fluint learns what closes.
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
Graphs & static scoring
Glean, GraphRAG, Einstein
Learns from your outcomes
Yes, trained on closed-won
No, encodes relationships or fixed rules
Predicts deal trajectory
Yes, scored live
No, describes current vs. future state
Finds new predictors
Yes, promotes them automatically
No, models relationships not predictions
Updates over time
Retrained as your motion shifts
Re-curated periodically
Private to your org
Yes, private weights
Graph or templated logic

Questions about Predictors.

What kind of model does Fluint train?

Fluint trains gradient-boosted and regression models on your deal history. These are purpose-built ML models that learn which behaviors and deal signals statistically predict revenue outcomes — not a general-purpose LLM prompt.

How does predictor promotion work?

When the model detects a new variable — like CFO attendance or multi-threading — that starts predicting wins with statistical significance, it automatically promotes that variable to a top predictor and surfaces the lift it creates.

Is this different from Salesforce Einstein?

Einstein uses fixed rules and basic scoring. Fluint trains a private model on your outcomes and continuously discovers new predictors as your motion evolves. It's the difference between a static rubric and a learning system.

How much data does the model need?

The model starts learning with as few as 100 closed deals. Prediction quality improves as more outcomes flow through the loop — every closed-won, lost, or stalled deal makes it sharper.

Does the model retrain automatically?

Yes. Every outcome feeds back into training. As your market shifts and your motion evolves, the model retrains on new labels and updates its predictors to reflect what's working now, not what worked last year.

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Get the model that predicts your wins.

Start free and go beyond a simple graph to a model that learns.

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