Fluint · Model

Stop renting AI:
own the model.

Run a private LLM engineered with your unique GTM data, served from
infrastructure you control. All for one fixed cost with unlimited usage.
Test your Model now
See how it works
YOUR INFRASTRUCTURE · YOU CONTROL IT
PRIVATE · OWNED
Your GTM DAta
Unified deal context
Win / loss / stall patterns
Buying evidence
Revenue trends
PRIVATE LLM
engineered on your data
your model — not shared
Served to your team
$
One fixed cost
one predictable bill
∞
Unlimited usage
every agent & workflow
◎
Tuned for revenue
fine-tuned to your patterns
The problem

Your AI stack is costing
more, delivering less, and
leaking data.

CFO

Unpredictable spend

Every agent, every workflow, every prompt is a metered API call. Usage-based pricing means your AI spend scales with activity, not outcomes. And it only goes up.
cro

Generic output

The LLMs you're using today are trained ont he internet. Not on your buyers, win patterns, or plays that actually close revenue. So your reps get the same output as every competitor using the same model.
ciso / cto

Data you can't control

Every connector, agent, and workflow sends proprietary data through client-side systems your security team can't see or govern.
How it works

How Model works

01 · FOUNDATION
dedicated

A dedicated LLM, purpose-built for revenue teams.

No shared infrastructure. No multi-tenant model. Your GTM team gets a foundational model deployed and managed by Fluint.
02 · KV CACHE / DATA
KV Cache

Loaded with your GTM data.

Your deal history, win/loss patterns, buyer signals, and revenue context stored inside the model: persistent context, available at inference with
zero hallucinations and instant response.
03 · ML ENRICHMENT
Loop

Continuously enriched by Loop.

Fluint's Loop layer enriches your data with cross-account patterns from our ML work. What's working in deals like yours, right now. The model
learns what generic AI never will.
04 · SERVER-SIDE
dedicated

Served from secure infrastructure.

All data processing happens server-side inside Fluint's SOC II Type 2, GDPR, and ISO 42001 compliant environment. No client-side transfers. No
data leaving the perimeter.
what it replaces

What Model Replaces

Before · the old way
×
Usage-based pricing across multiple vendors
×
Generic foundation models trained on public data
×
Client-side data processing through unaudited tools
×
Per-seat and per-call costs that grow with headcount
×
Fragmented AI stack with no unified data layer
After · with Model
✓
One fixed annual cost covering all GTM AI use cases
✓
A model trained on your revenue data and win patterns
✓
Server-side processing inside a compliant environment
✓
Flat pricing that doesn't scale with usage or headcount
✓
Unified model layer powering every agent, workflow, and prompt
Outcomes

What happens when
you switch.

CFO

Fixed costs and predictable AI spend

Every AI use case your GTM team runs (any agent, any workflow, any API call, any human prompt) is covered by a single, fixed annual cost. No metering. No overages.
CRO

Output that compounds

Because the model is trained on your deal data and enriched by Fluint, it gets better as your data grows. Reps get answers tailored to what wins here, not what's average everywhere.
CISO / CTO

Full security control

All connectors and data processing run server-side inside Fluint's compliant infrastructure. No shadow AI, no user-level risk. With one system to govern, not twenty.
Security & Compliance

Compliant by design

Pricing

Simple Pricing

One plan · unlimited usage
From $4K /mo
Fixed annual cost
Covers every AI use case your
GTM team runs
No per seat charges
No usage metering
No overages, ever
Start for $0 for the first 14 days

Questions about Model

What it is
What is Fluint Model?

Context engineering is the practice of building a structured, judgment-enriched data layer that AI agents can use to make revenue-relevant decisions. Instead of dumping raw CRM data or transcripts into an agent's prompt, context engineering pre-processes your GTM data through ML models trained on real deal outcomes, then delivers pre-materialized context anywhere your agents run via MCP or API.

How is Model different from using Claude or GPT directly?

Context engineering is the practice of building a structured, judgment-enriched data layer that AI agents can use to make revenue-relevant decisions. Instead of dumping raw CRM data or transcripts into an agent's prompt, context engineering pre-processes your GTM data through ML models trained on real deal outcomes, then delivers pre-materialized context anywhere your agents run via MCP or API.

Does Model replace all our AI tools?

Context engineering is the practice of building a structured, judgment-enriched data layer that AI agents can use to make revenue-relevant decisions. Instead of dumping raw CRM data or transcripts into an agent's prompt, context engineering pre-processes your GTM data through ML models trained on real deal outcomes, then delivers pre-materialized context anywhere your agents run via MCP or API.

cost & Security
What does Model cost?

Context engineering is the practice of building a structured, judgment-enriched data layer that AI agents can use to make revenue-relevant decisions. Instead of dumping raw CRM data or transcripts into an agent's prompt, context engineering pre-processes your GTM data through ML models trained on real deal outcomes, then delivers pre-materialized context anywhere your agents run via MCP or API.

Is my data secure with Model?

Context engineering is the practice of building a structured, judgment-enriched data layer that AI agents can use to make revenue-relevant decisions. Instead of dumping raw CRM data or transcripts into an agent's prompt, context engineering pre-processes your GTM data through ML models trained on real deal outcomes, then delivers pre-materialized context anywhere your agents run via MCP or API.

GET STARTED

Ready to stop renting generic AI?

Deploy a private model for your GTM team — fixed cost, unlimited usage, secure by default.
Get started with Model