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THE OPERATIONAL LAYER FOR AI

AI came with a promise to solve everything. It hasn't.

Flannel turns your messy data and processes into a safe, controlled and shareable MCP application that any AI agent can instantly run. Measurable return on investment and no engineering needed.

5 min

From sign-up to your first governed agent.

50%

Less tokens. Same answers.

1 source of truth

Every team, every model, one operational layer.

Product

One platform. Grows with your AI maturity.

Databases

Notebooks

Workflows

MCP

Governance

Connectors

Buckets

Database

Bring in a spreadsheet, connect a source, or describe the data model you want. Flannel generates an operational database with full row history, so teams and agents can query, write, and act with attribution built in.

Note books

Each team gets a notebook with consistent data, assembled context, and built-in actions in one place. It is a token-efficient operating surface for teams and AI agents to analyze, decide, and execute with more accuracy.

Workflow

Flannel’s workflow layer brings iPaaS-style power to AI operations. Describe what you want, and Flannel generates deterministic workflows across SQL, Python, LLM, search, and system actions with scheduling, logging, and auditability built in.

MCP

Expose Flannel through MCP so any model or agent can build on shared capabilities instead of raw tool access. Access is scoped to the right data, actions, and controls through one governed operational layer.

Governance

Governance is built into the system, not bolted on after. Flannel gives you whitelisted connector permissions, RBAC, policies, approvals, attribution, and a complete audit trail for every read, write, and execution path.

Connectors

Connect SaaS tools, APIs, databases, and internal systems, then compile them into agent-ready context. Flannel bakes in ontology, reduces retrieval overhead, and gives agents and your employees a cleaner, more consistent understanding of the business.

Buckets

Buckets make unstructured data operational. Store files, search them with RAG, derive key details with AI, and route the outputs into workflows, databases, and downstream system actions.

the problem

AI Is everywhere. But there’s no shared data layer.

Every team is running AI agents, building automations, wiring up integrations. The tools are incredible. But underneath them there's no shared foundation -- no compiled data, no cross-system governance, no operational memory that persists across conversations or models.

Raw connections compile nothing.

Your AI can connect to CRMs, support platforms, billing systems, all at once. But it's doing ad hoc compilation at runtime, every time, from scratch. 80x more tokens. No consistency. Two people asking the same question get different answers.

No governance on the cross-system picture.

Each system has its own permissions. But when Claude pulls from four systems, there's no audit trail on what it compiled or how. No row-level security on the combined view. No record of the source, the logic, or who authorized it.

Data remains isolated to individual models.

Claude Skills only work in Claude. The team on Codex starts from zero. The team on Cowork starts from zero. There's no centralized backend every AI tool in the company connects to.

AI mAturity curve

Where most companies are right now.

ExperimentingConnectingExpandingOperatingReplacing
stage-1: Experimenting

AI is helping individuals. It is not yet helping the business.

Your team is already finding value in AI. Reps are saving time, analysts are moving faster, and employees are discovering new ways to get work done.

But the value is trapped inside individual tools, prompts, accounts, and workflows. Nothing compounds across the company.

What This Means

You do not have an AI adoption problem. You have an operationalization problem.

The Risk

If this continues, every employee builds their own AI workflow, but the company never builds shared leverage.

How Flannel Helps

Flannel turns scattered AI usage into a shared operational foundation. Your teams can work from the same data, same context, and same workflows—without copy/paste, shadow systems, or personal prompt libraries.

Drop in your messiest spreadsheet. We'll show you what your AI-ready operating system looks like.

Stage-2: Connecting

You connected AI to your systems. Now every answer is different.

You have moved beyond basic experimentation. Your team is connecting AI to tools, workflows, databases, and files.

That is progress—but without a shared operational layer, every AI interaction rebuilds context from scratch. Two people can ask the same question and get two different answers.

What This Means

You are not missing integrations. You are missing a consistent source of operating truth.

The Risk

The more AI connections you add, the more inconsistency you create.

How Flannel Helps

Flannel unifies your data, files, workflows, and business context into a governed system your team and AI can rely on. Instead of every model pulling raw data at runtime, Flannel gives AI structured, annotated, reusable context.

Connect your systems once. Give every team and every AI the same operating truth.

Stage-3: Expanding

AI is spreading faster than your systems can support it.

AI adoption is accelerating across your company. Every team wants access. Every team wants workflows. Every team wants its own connection to its own tools.

But growth without a shared layer becomes sprawl.

What This Means

Your AI usage is scaling, but your operating model is not.

The Risk

Costs rise. Answers diverge. Security loses visibility. Cross-system workflows break. Power users become single points of failure.

How Flannel Helps

Flannel gives every team a shared, governed operational layer for AI. Teams can build reusable workflows, work across structured and unstructured data, and preserve company knowledge instead of trapping it in individual users.

Turn AI sprawl into a governed system your whole company can build on.

Stage-4: Operating

Your AI is doing the job. Now you need control.

AI is no longer just assisting. It is updating systems, routing work, generating outputs, and influencing decisions.

That means the bar changes. You now need attribution, approvals, auditability, access controls, and policy-driven execution.

What This Means

You are moving from AI as a tool to AI as an operator.

The Risk

Without the right foundation, AI can act faster than your governance model can keep up.

How Flannel Helps

Flannel gives your company the structure required for operational AI: governed access, shared context, audit trails, reusable workflows, and visibility across departments. With Flannel, AI can take action without becoming a black box.

Give agents the context to act—and the controls to act safely.

Stage-5: Transforming

AI is becoming your operating model.

You are approaching the next stage of business operations: agents working across departments, systems, and workflows with shared context and policy-driven autonomy.

This is where AI stops being a productivity layer and becomes the way the business runs.

What This Means

You are no longer just adopting AI. You are redesigning work around it.

The Risk

Legacy SaaS, siloed systems, and fragmented workflows will hold back what your agents can actually do.

How Flannel Helps

Flannel becomes the operational layer for AI-native work. It turns spreadsheets, systems, files, workflows, and institutional knowledge into governed capabilities that agents and employees can use together. This is how companies move from isolated automation to compounding intelligence.

Build the operational layer your agents can run on.

stage-1 Experimenting

AI is helping individuals. It is not yet helping the business.

Your team is already finding value in AI. Reps are saving time, analysts are moving faster, and employees are discovering new ways to get work done.

But the value is trapped inside individual tools, prompts, accounts, and workflows. Nothing compounds across the company.

What This Means

You do not have an AI adoption problem. You have an operationalization problem.

The Risk

If this continues, every employee builds their own AI workflow, but the company never builds shared leverage.

How Flannel Helps

Flannel turns scattered AI usage into a shared operational foundation. Your teams can work from the same data, same context, and same workflows—without copy/paste, shadow systems, or personal prompt libraries.

Drop in your messiest spreadsheet. We'll show you what your AI-ready operating system looks like.

Experimenting
Experimenting
Stage-2: Connecting

You connected AI to your systems. Now every answer is different.

You have moved beyond basic experimentation. Your team is connecting AI to tools, workflows, databases, and files.

That is progress—but without a shared operational layer, every AI interaction rebuilds context from scratch. Two people can ask the same question and get two different answers.

What This Means

You are not missing integrations. You are missing a consistent source of operating truth.

The Risk

The more AI connections you add, the more inconsistency you create.

How Flannel Helps

Flannel unifies your data, files, workflows, and business context into a governed system your team and AI can rely on. Instead of every model pulling raw data at runtime, Flannel gives AI structured, annotated, reusable context.

Connect your systems once. Give every team and every AI the same operating truth.

Connecting
Connecting
Stage-3: Expanding

AI is spreading faster than your systems can support it.

AI adoption is accelerating across your company. Every team wants access. Every team wants workflows. Every team wants its own connection to its own tools.

But growth without a shared layer becomes sprawl.

What This Means

Your AI usage is scaling, but your operating model is not.

The Risk

Costs rise. Answers diverge. Security loses visibility. Cross-system workflows break. Power users become single points of failure.

How Flannel Helps

Flannel gives every team a shared, governed operational layer for AI. Teams can build reusable workflows, work across structured and unstructured data, and preserve company knowledge instead of trapping it in individual users.

Turn AI sprawl into a governed system your whole company can build on.

Expanding
Expanding
Stage-4: Operating

Your AI is doing the job. Now you need control.

AI is no longer just assisting. It is updating systems, routing work, generating outputs, and influencing decisions.

That means the bar changes. You now need attribution, approvals, auditability, access controls, and policy-driven execution.

What This Means

You are moving from AI as a tool to AI as an operator.

The Risk

Without the right foundation, AI can act faster than your governance model can keep up.

How Flannel Helps

Flannel gives your company the structure required for operational AI: governed access, shared context, audit trails, reusable workflows, and visibility across departments. With Flannel, AI can take action without becoming a black box.

Give agents the context to act—and the controls to act safely.

Operating
Operating
Stage-5: Transforming

AI is becoming your operating model.

You are approaching the next stage of business operations: agents working across departments, systems, and workflows with shared context and policy-driven autonomy.

This is where AI stops being a productivity layer and becomes the way the business runs.

What This Means

You are no longer just adopting AI. You are redesigning work around it.

The Risk

Legacy SaaS, siloed systems, and fragmented workflows will hold back what your agents can actually do.

How Flannel Helps

Flannel becomes the operational layer for AI-native work. It turns spreadsheets, systems, files, workflows, and institutional knowledge into governed capabilities that agents and employees can use together. This is how companies move from isolated automation to compounding intelligence.

Build the operational layer your agents can run on.

Replacing
Transforming
Maturity-curve
Maturity-curve
Expectations
Reality
With Flannel
Tell Us Your Stage
Tell Us Your Stage
capabilities

A unified system built to scale AI operations.

Document-chain
Agentic Document Creation

Make documents executable.

Search business artifacts with RAG, derive key details with AI, and route the outputs through deterministic workflows, governed databases, and downstream system actions.

Compiled data
COMPILED DATA

Compile any system into agent-ready context.

Turn fragmented systems into governed operational models with baked-in ontology, historical state, and workflows so agents reason with less retrieval, fewer tokens, and better semantic understanding.

Notebooks
NOTEBOOKS

Give every team an AI operating surface.

Stand up notebooks that unify data, documents, workflows, history, and semantic understanding into one shared operating surface for analysis, execution, and human-plus-agent operations.

Headless ai
HEADLESS AI

Turn desktop AI into a governed capability layer.

Let teams build agents on shared Flannel tools, datasets, workflows, permissions, and security boundaries so they can replace narrow systems, reduce license sprawl, and run more of the business from one operational layer.

llm agnostic
LLM AGNOSTIC

Add governed capabilities to any model.

Connect your systems once, expose them through MCP, and give any LLM secure, scoped access to query, write, and execute against your business with control built in.

Unified workflow
UNIFIED WORKFLOWS

Run deterministic workflows across rows and files.

Combine structured data, unstructured inputs, business rules, and agent steps in one governed execution path built for operational processes.

Audit trail
AUDIT TRAIL

See every action, change, and decision.

Track what AI accessed, what changed, who triggered it, and how state evolved over time with scoped permissions, approvals, and full execution history.

Token efficiency
TOKEN EFFICIENCY

Reduce token spend before inference starts.

Compile context into operational views, workflows, and semantic structures so agents do less retrieval, use fewer tokens, and return more consistent answers.

Controlled access
CONTROLLED ACCESS

Govern every query and every write.

Let agents read and act across business systems with secure, scoped access, approvals, attribution, historical state, and full execution history.

Telemetry
TELEMETRY

See how agents actually operate.

Track what agents queried, what context they used, what they changed, and how state evolved over time so agent behavior becomes inspectable and improvable.

get started

Two paths in. Both take minutes.

Start With Your Data
Connect Your Systems

Upload what you have. Connect your AI. Done.

Drag in a spreadsheet or describe your data model. Flannel generates a governed database, notebook, and MCP endpoint. Work directly in Flannel, or plug in Claude, Cursor, or ChatGPT to use Flannel as your headless system of record. Your team and your AI run on the same system.

Schema-controlled
Schema-controlled
Row level security
Row-level security
Full chance history
full change history
Scale to millions
scale to millions
Write attribution
write attribution
Mcp ready
mcp-ready

Connect your systems. Ground every answer.

Salesforce, Zendesk, HubSpot, Linear, Google Sheets, and any REST or GraphQL API. Point, authenticate, sync. Data lands in Flannel databases with ontology, history, and rules already baked in. Your AI reaches the right context faster, answers accurately, and spends fewer tokens getting there.

Salesforce
Salesforce
Zendez
Zendesk
Google drive
Google Drive
Hubspot
HubSpot
Linear
Linear
Any rest
Any REST/GraphQL
Slack
Slack
tronclad
ironclad
+ More
Comparison

None of these solve it alone.

You've tried SaaS, iPaaS, warehouses, and raw MCP connections. Each solves a piece. None of them give you a compiled, governed, AI-native backend.

SaaS Apps

iPaaS

Warehouses

Raw MCP

Cross-System Data

Siloed. Each app sees itself.
Moves data. Doesn't compile it.
Stores everything. Can't act on it.
Ad hoc at runtime. Every time.
Compiled. Persisted. Attributed.

AI-Native Access

None.
None.
SQL only. Not MCP.
Ungoverned.
Every notebook is an MCP server.

Operational Context

Data models locked to one vendor.
No data models. No validation.
Schema. No validation layer.
None. Stateless.
Schema + validation generated in seconds.

Governance

Per-app only.
Audit on triggers. Not on data.
Query logs. No action governance.
Nothing on the combined picture.
Row-level security. Full audit. Write attribution.

Persistent Memory

None across tools.
None.
Historical. No AI context.
Resets every conversation.
Persistent. Versioned. Compounds.

Cross-Model Support

N/A
N/A
N/A
One lab's model only.
One backend. Every model.

Token Efficiency

N/A
N/A
N/A
Full context every call.
80x fewer tokens.
Pricing

Start free. Pay for what you use.

15-day free trial. All features unlocked. Then pick a plan that fits.

trial

Free

15 days — all features

Start Trial
Start Trial
Unlimited seats
15,000 credits included
All features unlocked
Workflows, agents, databases
Start Trial
Start Trial
pro
most popular

$20

/seat /month

+ usage

Get Started
Get Started
No commitment — cancel anytime
All features included
Buy credit packs as needed
Overage billed at usage rates
Get started
Get started
Enterprise

Custom

Volume pricing + dedicated support

Talk to Us
Talk to Us
Custom seat + credit packaging
Monthly or annual commit
Volume discounts
Dedicated onboarding
Priority support
Talk to Us
Talk to Us

How Credits Work

Credits power everything you do in Flannel — workflows, notebook queries, connector syncs, and storage. Most teams use 10,000–15,000 credits per month. Buy them as needed in packs of 5,000+.

Workflows:
Unlimited
Agents:
Unlimited
Databases:
Unlimited
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Your team is already using AI. Make it actually work.

One platform. Governed data. Every model connects.

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THE OPERATIONAL LAYER FOR AI

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