why we exist

AI for revenue shouldn't mean renting generic models and hoping for the best.

Every revenue team is racing to deploy AI. But the tools available today are generic frontier models trained on the internet, usage-based pricing that scales with activity instead of outcomes, and context scattered across tools no one governs.

The result: unpredictable spend, output your competitors also get, and no way to prove what any of it actually earned.

We built Fluint because we believed revenue teams deserved their own AI stack — one that learns from their data, compounds with their outcomes, and pays for itself.
What we built

Three layers. One system. Compounding every cycle.

Fluint is three products that work as one system:

Model

A private LLM deployed for your organization, loaded with your GTM data. Fixed cost, unlimited usage, served from secure infrastructure. No per-seat charges, no usage spikes.

Loop

The context engineering and revenue attribution layer. Loop unifies your GTM data, trains private ML models on your deal outcomes, and pre-materializes enriched context served to any agent over MCP. Every agent action is traced back to the revenue it influenced — and those outcomes retrain the model for the next cycle.

Agent

Any agent your team runs (Claude, OpenAI, Glean, custom) reads the same enriched context through one MCP endpoint. Or deploy Fluint Chat: a fully managed, white-labeled sales assistant that ships in a week.
The three layers form a closed loop: outcomes feed back into the model, the model sharpens the context, and agents get better because the system did. Every deal compounds the next.
OUR APPROACH

Engineering-led. Revenue-native. Built to compound.

We didn't start with a chatbot and add analytics. We started with the infrastructure: data unification, private ML training, context materialization, and closed-loop attribution. Then we built the products on top.

That's why Fluint's architecture looks more like a modern data platform than a sales tool. It's designed to be the system underneath your agents — not another agent bolted on top
Private by default
Every customer gets their own model with private weights. No shared infrastructure, no data commingling.
Closed-loop
Every action is traced to an outcome. Every outcome retrains the model. The system improves whether you're using it or not.
Fixed cost
AI spend should be predictable. Model is unlimited usage at a flat rate. No surprise bills.
Open delivery
MCP and REST API. No proprietary SDK, no lock-in. Your context survives vendor swaps.
Leadership

Built by people who've scaled engineering and revenue systems.

Nate Nasralla
Nate Nasralla & Jon Crawley — Co-Founders / CEO & CTO

Jon built and led architecture teams at JumpCloud, where he managed 500+ developers and shaped the DevOps practices that scaled the platform through hypergrowth. Nate built and scaled enterprise sales teams and go-to-market operations — watching revenue teams try to retrofit tools built for other functions.

Fluint came from a simple observation: revenue teams were about to face the same infrastructure challenges that engineering teams faced a decade ago. They'd need their own DevOps — a system for building, testing, deploying, and measuring the AI that runs their motion. We set out to build it before the gap became a crisis.

See what your AI stack should look like.

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