EngramAI Context Layer

Scalable collective
intelligence

Engram reads your company's knowledge — Notion, Confluence, GitHub — and distills work patterns, conventions, and principles into context every AI tool consumes automatically.

Claude Code

>

Sources

Notion
Confluence
GitHub
Google Docs

Teams

Engineering
Design
Marketing
Product

[ Engram ]

.claude/docs/engram/
api-conventions.md
auth-flow.md
security.md
design-system.md
accessibility.md
brand-voice.md
product-vision.md
positioning.md
competitive-intel.md

... 25 other docs skipped

Works with

Claude
Codex
Gemini
Cursor
Copilot
Windsurf
Amazon Q
Cline
OpenCode

The missing layer

Your AI stack has models and tools.
It's missing the context layer.

As AI handles more of the work, the people steering it need one place to define how your company operates — patterns, conventions, principles, rules — and have that context flow to every AI session automatically.

The problem

AI adoption without shared context is chaos

Every session starts from zero

AI knows nothing about your design system, brand voice, or API conventions. Engineers, marketers, designers — everyone wastes time repeating the same instructions in every chat.

Rule files don't scale

60,000+ repos have scattered CLAUDE.md files. Stale in two weeks, contradict each other across repos, and only help coders — leaving every other department without AI context.

Your knowledge base wasn't built for AI

Notion and Confluence are full of expertise — but AI can't navigate wikis or extract the actionable patterns buried in them. You need a layer that reads your existing knowledge and distills work patterns, conventions, and principles into context AI actually consumes.

Specialists can't scale

Your best designer reviews 5 PRs a day. AI makes 500 decisions that need that expertise. The bottleneck isn't knowledge creation — it's distribution.

End-to-end builders

Everyone becomes a builder

The old model: specialists in silos, waterfall handoffs, waiting for reviews. AI changes this — anyone can build end-to-end when their AI carries every specialist's expertise. No more bottlenecks. No more narrow lanes.

A developer

ships a full feature — UI, copy, security — without waiting for three other teams to review.

Because: Design system, brand voice, and security policies are already in every AI session.

A product manager

prototypes a working API integration without filing a ticket.

Because: API conventions, auth patterns, and error handling guidelines are built into the context.

A marketer

launches a campaign page that follows the design system and uses correct product terminology.

Because: Component usage, spacing rules, and product naming conventions are always available.

How it works

Three steps to shared context

01

Connect

Point at Notion, Confluence, GitHub, websites. Engram scans your existing knowledge — finds rules, identifies contradictions, spots gaps.

02

Structure

AI generates missing guidelines from your sources. Specialists review and approve. Quality scoring ensures consistency across domains.

03

Distribute

Engram compiles guidelines into the format each AI tool expects and keeps them in sync. Every session, every tool, every team — always up to date.

Smart routing

AI loads only what it needs

Engram compiles a lightweight routing hub (~200 tokens) for each AI tool. The AI reads the hub first, then loads only the relevant guidelines on demand.

CLAUDE.md — routing hub

# Engram Context

Load docs as needed from
.claude/docs/engram/

Available context:

~200 tokens — not thousands

Loaded guideline — full context on demand

Marketing team

.claude/docs/engram/brand-voice.md

Updated 2h ago
# Brand Voice

Direct, confident, no jargon.
Second person ("you").
Contractions OK.

## Forbidden terms
Never say "leverage", "utilize",
"synergy", or "best-in-class".

## Tone by context
- Error messages: empathetic
- Marketing: bold, concise
- Docs: neutral, precise

Guideline health

Always accurate, never stale

Engram continuously monitors your guidelines — not just at setup, but every day. When something drifts, you know before your AI does.

Duplicates

Finds overlapping rules across domains and repos

Contradictions

Catches conflicting guidelines before AI does

Staleness

Flags guidelines when code or practices drift

Enterprise ready

Your context never leaves your infrastructure

Self-hosted

Rust-powered, ~20MB binary, <50MB RAM

Single Docker image on your own infrastructure. Sub-10ms context delivery. No data leaves your network.

BYO-LLM

Anthropic, OpenAI, Azure, Bedrock, Ollama

All AI processing uses your own models and API keys.

SAML SSO

Any SAML 2.0 identity provider

Enterprise authentication with your existing identity provider.

Audit trail

Immutable version history

Full change tracking on every guideline. Who changed what, when, and why.

FAQ

Common questions

Give every AI session your company's context

Set up in minutes. Start with one team, scale to the whole company.