Your AI tools know how to write code. Nylo/gram tells them how your product works.

Nylogram keeps the rules, roles, workflows, components, patterns, and decisions behind your product, with every revision and its source. When an AI tool works on a task, it gets the facts that apply, and only those.

Request a pilot See how it connects

$ claude mcp add nylogram https://api.nylogram.com/mcp

One record, four revisions. The instrument every Nylogram screen is built from.RULE / profile_complete · ctx 0042

It does not know your product.

An AI tool does not know

why a feature exists
who is allowed to use it
which pattern your team approved
which rule applies
which exception matters
what your team already decided

Every prompt starts from zero. The rules, roles, workflows, and decisions that make your product yours live in people, documents, and old tickets, and none of that reaches the tool.

Nylogram gives the AI tool only the facts that apply to the task.

Task 0042
Add approval controls to supplier onboarding.

Connect your sources.

Design files, docs, repositories, and decision logs become one product model.

DESIGN · DOCS · REPOS · DECISIONS
→ PRODUCT MODEL · 210 objects

The AI tool asks for a task.

Nylogram evaluates every object against the task and returns the ones that apply.

ctx.request("add approval controls")
→ 5 objects · 1 conflict

Your team sees what the tool used.

Each object arrives with its source. A conflict stops the tool and goes to a person.

[KNOWN] exists · [APPLIES] used · [REVIEW] a person decides

Connect it to what your team already runs.

Context goes in and comes back out the same two ways. Both are live today.

MCP

Claude Code, Cursor, and any other MCP client. The tool asks for the context a task needs and gets back only the objects that apply, each with its source.

$ claude mcp add nylogram https://api.nylogram.com/mcp

reads · writes

REST API

Post a document or a question, then read the answer back with its claims, the evidence under them, and whether anything has changed since it was written.

POST /api/inquiries
GET /api/inquiries/:id/graph

reads · writes

The AI tool writes the code. Nylo/gram supplies the facts about your product.

Nylogram stores product knowledge as connected objects, not as a pile of documents. A tool can ask for the rules of a workflow, the roles that can run it, or the decision behind it.

This example model has 210 objects in five groups.

Every answer comes with its chain.

Ask which role can approve a supplier and Nylogram returns the records the answer rests on, each with its revision, its confidence, and the source it was read from. Change a source and everything above it is flagged.

A design system knows what a button looks like. Nylo/gram knows what the button does in your product.

Nylogram checks each fact before the AI tool uses it.

Works with the tools your teams already use.

The tool gets the five facts for task 0042 inside the editor your team already uses.

const context = await nylogram.context({
  task:  "Add approval controls",
  scope: "supplier-onboarding"
})

// 5 objects for task 0042, no unresolved conflicts

MCP and REST: availableSDK: in development

$ claude mcp add nylogram https://api.nylogram.com/mcp

Where Nylo/gram sits.

Nylogram sits between your sources and the AI tools. It reads your design files, code, docs, and decisions. It serves one product model to the tools through MCP, an SDK, and a REST API. Your team does not need to open another app.

Two plans. Neither one counts seats.

The price follows the product model and how often your tools ask it, not how many people sit in the room. Everyone on the team can use it.

Studio

$990a month

One product, every tool your team uses.

Product models1
Context requests10,000 a month
SeatsUnlimited
Past the included volume$0.10 a request

MCP, REST API and SDK
Every revision with its source
Email support

Start a pilot

Platform

$100Ka year

$8,333 a month, billed annually

Product context across teams, agents and tools.

Product modelsUp to 10
Context requests100,000 a month
SeatsUnlimited
Past the included volume$0.05 a request

SSO and role-based access
Audit export
A dedicated engineer and a private Slack channel

Starts with a $35K pilot over four to six weeks, then a $25K deployment.

Request a pilot

Seats are unlimited on both plans. Past the included volume, requests are metered. More than ten product models, talk to us.Prices in USD

Every task should start from what the product already knows.

Cartisien sets up the product model with your team. Your tools read it through MCP, an SDK, or the REST API.

Request a pilot Read the docs