Flow AI

Ship customer-facing data agents inside your product

Build analytical AI agents that natively reason over structured data, rules, and customer context – and generate visual insights directly in your UI.

Great for data-heavy SaaS

Pricing & revenue optimization

Forecasting & planning

Operational intelligence

Market simulation & consumer analytics

Financial modeling

Supply chain & logistics analytics

Risk & portfolio analytics

Pricing & revenue optimization

Forecasting & planning

Operational intelligence

Market simulation & consumer analytics

Financial modeling

Supply chain & logistics analytics

Risk & portfolio analytics

Flow AI Hero
Flow AI Problem

[ Problem ]

Your product has rich data — but agents can’t use it as-is.

Complex schemas

Cryptic column names

Thousands of enums

Data quality issues

Temporal rules

Domain formulas

Complex schemas

Cryptic column names

Thousands of enums

Data quality issues

Temporal rules

Domain formulas

Compare revenue for Q3 vs Q4.

Thought for 1min 34s

I couldn't find the 'revenue' column. Please try again.

Customer-facing agents fail on real analytical tasks

LLMs cannot natively interpret multi-tenant schemas, custom logic, and legacy models. They guess — your product cannot.

"Exclude test data before 2021"

"Elasticity must use log-normal demand model"

"Revenue calculations exclude refunds"

"Customer segments use fiscal year boundaries"

Your domain logic lives outside your data schema

Rules, formulas, constraints, and definitions are spread across people and documentation, inaccessible to agents.

AI response

Simple chat interfaces can't deliver analytical clarity

Your users expect interactive charts, comparisons, and scenarios they can trust — not static, unverified text outputs.

Flow AI Icon

[ Solution ]

Infrastructure for data-heavy SaaS to ship production-grade data agents

A unified layer for data, reasoning, and UI generation that lets you ship reliable analytical agents — without rebuilding your stack.

Semantic data layer

Agentic reasoning

Generative UI

Enterprise runtime

Semantic data layer

Agentic reasoning

Generative UI

Enterprise runtime

Flow AI Problem
Semantic data layer

Turn complex data models and rules into agent-ready knowledge

Structure extracted automatically
Tables, relationships, and constraints are transformed into a clear, governed representation that agents can reason over reliably.
Business rules encoded as logic
Definitions, naming standards, and exceptions, are captured and encoded so agents operate with full domain understanding.
Continuous improvement from real usage
User corrections, query patterns, and feedback refine the system, making agents less error-prone and more aligned with how your customers think.
Table
schemas
Documents
docs
Logic graph
logic
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Semantics diagram
semantic layer
Flow AI Problem
Agentic reasoning

Deterministic reasoning and data operations for analytical agents

Compare revenue for Q3 vs Q4.
Listed tables
Pulled table info
Searched metric
{ name: "revenue" ...
Planning...
Plan: 1. Query the quarterly-reports table directly since it already has aggregated quarterly revenue figures - this is the most efficient path. 2. Filter for Q3 and Q4 of the current year (2024) using the quarter and year columns. 3. Select total_revenue for both quarters ...
Agents reason over your schema and rules
Your agents operate on a structured model grounded in your schemas, definitions, and business rules.
Transparent, reviewable steps for every query
Each request produces a reasoning plan of data selections, filters, transformations, and model calls. Edit, constrain, or approve – then let the agent run it.
Agents can perform multi-step data operations
Agents can read data, apply multi-step transformations, write updated snapshots back to your database, trigger internal predictive models, and retrieve results.
Flow AI Problem
Generative UI

Structured visual outputs that fit natively into your product

Which of our carriers caused the most delivery delays last quarter?
DPD and FedEx had the highest delay rates last quarter. Here's the breakdown per carrier.
Generative UI
Break down delays by shipping lane
Compare carriers on cost vs. delay rate

Validated components your agents can trust

A validated registry of charts, tables, comparisons, KPIs, and controls ensures agents only produce safe, renderable UI.

Works anywhere in your UI, not just chat

Agents can output charts, tables, and comparisons that you can embed inside your product — or surface through a chat interface.

Fully native to your product experience

Use your layouts, styling, and design system. Flow AI supplies the structure and validation; you own the brand.

Flow AI Problem
Enterprise runtime

Scalable execution layer for analytical agent workloads

Run your agents securely in your stack with zero lock-in. Use your preferred models, control execution, and deploy anywhere.

Models

OpenAI, Anthropic, Gemini, Llama, Mistral, Qwen, and more

Hosting

AWS, Azure, GCP

Deployment

On-premise or SaaS

Data residency

EU or US

[01]

models

OpenAIAnthropicGemini
MetaMistralQwen

[03]

deployment

On-prem

SaaS

[04]

data residency

USEU

[02]

hosting

AWSAzureGCP
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[ About us ]

Built by early pioneers in AI agents

From the original generative writing assistant to industry-leading evaluation models, we've spent years turning LLMs into reliable, real-world products.

Team member 1
Leaderboard
Team member 2
Card 1

"Flow Judge evaluator model outperformed every LLM we tested for the same task."

Team member 3
Card 2
Team member 4
Card 3

"They helped catch failures in our agent we didn't even know to look for"

Phi
Team member 1
Leaderboard
Team member 2
Card 1

"Flow Judge evaluator model outperformed every LLM we tested for the same task."

Team member 3
Card 2
Team member 4
Card 3

"They helped catch failures in our agent we didn't even know to look for"

Phi
Project A
Seedcamp
Lifeline
Moonfire

Backed by

Flow AI Problem

[ Timeline ]

Demo to production in 3 weeks

We help you turn your existing data and tools into a reliable data agent embedded directly in your product UI.

W1

Establish the foundation

Connect to your data, extract schema, parse documentation, and build the initial semantic layer.

W2

Build reasoning and UI

Configure the agent's reasoning and the UI components it will render.

W3

Integrate and release

Embed the agent into your product, validate outputs, and ship the first version.

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