The model is rented. The intelligence should be owned.
Engramic is where humans and agents author knowledge together — so every agent you deploy speaks your business, and the intelligence stays yours, not your model provider's.
How do you make an agent fluent in your business?
Fluency comes from the knowledge, not the model. Engramic is a knowledge graph your team authors: the decisions you've made, the goals you're working to, the constraints you operate under. It's delivered to every agent at the start of every task.
It's the knowledge layer the enterprise platforms are racing to build, without the complexity of large enterprise tools. No connector programme, no rollout, no consultancy. A team can be authoring on day one.
And it's authored, not observed. Your graph holds what your team decided and why, put there deliberately by people and agents working together. Shared with every agent at the start of every task. Over time, agents stop sounding generic and start sounding like your organisation.
Workflow · Create a goal
Create goal
Write a goal directly to your workspace
Increase customer retention by 50% by end of Q3
We've recruited a Forward Deployed Engineer to work on key customer accounts this FY; their contributions should help increase retention.
Ship customer-requested improvements from FDE account reviews
shipped per quarter → 3 1
Close FDE-raised product gaps within 30 days
median days to close → 30 62
Ask Alia.
Your graph has structure. You shouldn't have to learn it to ask a question. Alia is the agent librarian for your organisation's knowledge — ask her in plain language, from inside Claude, and she answers from what your team authored, with the decisions it rests on attached.
Meet AliaThinking about raising Tier B by 12% next quarter.
That would conflict with the Q3 forecast the team committed to in May.
Right — forecast gets revised before any price change ships, then.
That sounds like a constraint. Record it?
Tier B price changes wait on the Q3 forecast revision.
proposed by Alia · approved by Maya Patel · e_2026_0715
Why does your agent get it wrong when the documents say otherwise?
Anyone who has deployed an agent into a real organisation knows the moment: the agent did exactly what the documents said — and got it wrong. The branding changed for a project two days ago. The process doc it followed is the one everyone ignores. The customer nobody contacts mid-audit isn't flagged in any system.
That knowledge is what actually runs your organisation: the reasoning behind decisions, the exceptions, the constraints agreed in conversation. No retrieval system can reach it, because it was never captured anywhere. Your organisation runs on a language no model was trained on.
Engramic is where it gets written down. Authored by humans and agents together, as the work happens, before the thinking moves on.
What happens when the goal changed on Tuesday and the doc didn't?
The goal changed in Tuesday's stand-up. The doc still says otherwise. An agent working from your documents will act on January's truth in July — confidently.
Your agents have no yesterday. No last quarter, no sense of what changed or why. The gap is continuity. A document tells an agent what the organisation knows. It doesn't tell it what the organisation has been through.
Engramic adds temporal continuity. What was decided, what changed, what superseded what — the arc of how things got here. Fluency isn't just knowing the facts; it's knowing which ones are current. When something new conflicts with what was agreed, it surfaces. That's organisational memory.
- 16:48Q3 forecast committed — 18% over planPlan Agent
- 14:32Conflict surfaced — Tier B vs Q3 planAgent
- 14:22Pricing tier A revisedGeorge Harrison
- 11:05How will Tier B affect annual contracts?Paul McCartney
- 09:42Initial pricing model committedFinance Team
- 09:15Tier B raised — 12% uplift agreedJohn Lennon
graph · 214 items
- ConstraintTier B price changes wait on the Q3 forecast revision
- GoalIncrease customer retention by 50% by end of Q3
- Claude Opus 4.8Active
- GPT-5Active
- Gemini 2.5 ProActive
- Claude Sonnet 3.5Retired
What makes knowledge Agent-Ready?
Agent-Ready is a property of the knowledge itself. A decision is not a constraint is not a goal; everything is anchored to a shared vocabulary, ordered in time, and shared with agents on demand. That's the difference between a graph and a well-written wiki. Both are authored, but only one can be handed to an agent at the start of a task, with no human assembling a prompt.
And because the form is open, it travels. The same knowledge reaches any agent — Claude today, another model tomorrow, several side by side — because the knowledge was never inside any of them. Your graph lives in Engramic and moves over MCP, the open standard for connecting agents to context. No proprietary runtime between your organisation and the model. Swap the model; the fluency stays.
How does it actually work?
Three steps, and no rollout programme in front of them.
01
Author it as the work happens
A decision gets made in a chat, a call, a thread. It goes into the record then and there — by the person who made it, or by the agent they were working with. No workshop, no write-up afterwards.
02
Alia keeps it coherent
Alia is the librarian. She spots the gaps, surfaces the contradictions, and asks the question when a goal is missing a constraint. The answer lands back in the record.
03
Every agent starts briefed
Connect over MCP and any agent begins its next task knowing the goals in play, the live constraints, and what was decided recently and why. No re-explaining, no hand-built prompts.
What does one shared graph give a team?
One shared graph means shared visibility. When two decisions contradict each other, it surfaces for the team to settle while it's fresh, instead of six months later through an agent acting on stale truth. When a goal lacks a constraint, or a topic is half-specified, someone gets asked before an agent trips over it.
Alia is Engramic's librarian, keeping the collection coherent — spotting gaps, surfacing contradictions, asking the questions that turn what's in someone's head into something agents can use. She learns the language your team speaks, so every agent that connects can speak it too. Over MCP, any model can draw on the same checks.
12 open · 8 answered (24h) · 5 topics sharpened
How does this interact with existing annual contracts?
Triggered by Tier B raised — 12% uplift. The graph has 23 active annual contracts on Tier B. Mid-term uplift may conflict with the renewal clause in the standard MSA.
Questions people ask before they start
What is organisational memory for AI?
Personal AI memory tracks what you prefer and what you have said before. RAG retrieves documents when an agent needs a fact. Organisational memory is the third thing: the decisions your team has made, the constraints you operate under, and the reasoning behind both — authored deliberately, ordered in time, and handed to every agent at the start of every task.
What organisational memory for AI meansWhat's the difference between AI memory and RAG?
RAG retrieves what has already been written down. Memory persists what happened in a session. Neither reaches the trade-off agreed on a call last Tuesday, because it was never a document and was never said to an assistant. Engramic is where that gets written down — once, by the person who decided it, in a form every agent can read.
AI memory and RAG, comparedWhere does my organisation's AI context actually live?
For most organisations: in at least five places — a Copilot deployment, a RAG pipeline over the docs, a couple of agent frameworks, several assistant memories — under terms you did not negotiate, not designed to be inspected, and largely non-transferable if you change any of the tools. Engramic keeps it in one graph you own and can export.
Where your AI context livesDo I need a vector database for my AI agents?
Probably, if your agents search a large body of documents. But a vector database retrieves whatever you put into it; it does not hold what your organisation has decided or the constraints its agents should work under. Two different problems. Most teams need both — Engramic is the second one.
Vector databases and what they leave untouchedDoes anything we author become training data?
No. The graph lives in Engramic, not inside a model. It is yours to read, export and take elsewhere, and nothing your team authors is used to train a model. Agents propose; a person confirms. What ends up in the graph is what someone chose to put there — which is why it can be trusted later.
Which models and tools does Engramic work with?
Engramic connects over MCP, the open standard for giving agents access to context, so Claude, Copilot, Mistral and any custom MCP client read the same graph. You can run several at once, and change which one you run without touching the graph.
What does Engramic cost?
Free for one person, forever. Standard is £9.99 per seat per month; Pro is £24.99. Both exclude VAT, added at checkout where applicable. A seat is a human member — named agents are free and unlimited on every plan.
See the full pricingSpeaks MCP
Engramic plugs into any AI client that speaks the Model Context Protocol — no custom integration, no infrastructure project.
Works with
How long does it take to connect?
Minutes. Engramic connects via MCP — no infrastructure deployment required to begin. Connect, and your agents start every task drawing on context your team has deliberately authored.
We're working with a small number of teams now. If you're building with agentic AI and ready to own your intelligence rather than rent it, get started.
No commitment required. Connect via MCP and your team can be authoring in minutes.