OM2 Custom GraphThe context graph your agents need, in weeks, at a fraction of the cost.

OM2 Custom Graph turns the systems behind any domain into a context graph your agents can rely on: structured facts, kept current, traced to their source and scoped to each person’s access. Coworker runs the whole pipeline, so you don’t build or operate a graph.

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Built on OM2·MCP or API·Hosted or on your own servers

Custom graphExample

Claude

ChatGPT

Your agents

Your toolsCustom context graphYour agents

84.5%better quality, preferred in blind review

64%faster to complete a task

9xcheaper per answer

Benchmarked against the same agent using tool calls alone.See the benchmarks →

The problem

Agents fail on the details of your business.

Every part of your business runs on specifics, like which service owns a field or which pricing rule applies to an account. Most agents only see documents, so when an answer depends on how those specifics connect, they guess.

The usual fixes fall short. Search and RAG over each system can return a different answer to the same question. Modeling an ontology by hand takes specialists months, and it starts over for every new team or client.

What you get

A context graph behind every agent you deploy.

Structures the messy stuff

Turns documents, tickets, chat, email and records into typed facts, each linked to its source.

For your teamAgents work from facts instead of guessing across documents.

For partnersA context graph in a box: one method for every client’s data.

Stays current

Syncs from chat, tickets, email, code, CRM and meetings, plus any exports you push.

For your teamNo re-modeling as the business changes.

For partnersHand over a graph that keeps itself up to date.

Your ontology, learned by AI

Drafted from your data with evidence for each type, or bring your own. A person approves every change.

For your teamYour team reviews instead of modeling by hand for months.

For partnersStand up each client from a reviewed draft.

Reliable by design

Agents query typed facts with the caller’s access, every answer traces to its source, and a benchmark measures quality.

For your teamConfident enough to put agents into production, with lower cost as a byproduct.

For partnersPut your name on an agent in front of a client.

Use cases

Where teams are putting context graphs to work.

Any part of the business with systems behind it can have its own context graph. These five are the ones teams ask for most often today.

Client AI deployments

“Which of this client’s workflows should we automate first, and who owns each one?”

ValuePartners deliver engagements faster with fewer people, and every project on the account builds on the same context instead of adding tech debt.

Codebase graphs

“If I change this contract, which services break, and who is mid-migration?”

ValueAgents make changes in the right places, so fewer PRs get rejected and teams ship faster without shipping more bugs.

Operations graphs

“Which fare rules apply on SFO to JFK next week, and which one takes over on Monday?”

ValueAgents answer with the business’s own rules every time, and the partner delivers them without hand-modeling the ontology first.

Finance operations

“Which payments cover these Northwind invoices, and which invoices are still open?”

ValueManual matching hours go away, with an agent the finance team can rely on.

Customer and shipment graphs

“Where is this order, and is a top-tier customer about to get it late?”

ValueAgents answer “where is my shipment” without a person digging through four systems, and flag a late order for a top-tier customer before they have to ask.

Have a different use case in mind?

Clinical trials, insurance claims, manufacturing lines, legal matters. If your agents need to know it, OM2 can graph it.

Tell us your use case

How it works

Five steps from your systems to a context graph your agents can use.

Step 1ConnectPoint it at the systems you want in the graph, with their permissions.

Step 2Draft the ontologyA model proposes the types that matter, each with a quote as evidence.

Step 3ReviewYour team or partner approves the draft. Nothing changes without a sign-off.

Step 4Build and measureThe graph builds, and a benchmark scores it on your own questions.

Step 5Give agents toolsCustom tools go live on MCP, scoped to each person’s access.

Devs: we built the hardest parts, from entity resolution and access on every record to incremental updates, extraction at scale and a benchmark harness. Build on them easily with OM2 Builder.See OM2 Builder →

Deployment

Runs where your data lives.

Reach it through MCP or the API, with any model. Coworker can host it, or you can run it in your own cloud. The files are the same either way, so the choice isn’t a one-way door.

OM2 Custom Graph vs. building or buying a graph.

Embeddings plus a vector database is a weekend demo, but production takes years of work underneath it: matching records across systems, carrying permissions into every fact, keeping it current, and running extraction, deletion and audit at scale. Partners would rebuild all of it for every client.

	OM2 Custom Graph	Build in-house on a graph database	Hand-modeled ontology platforms	Cloud platform bundles	MCP or vector RAG over each system
Ontology	Drafted from your data, reviewed by your team	Designed by your engineers	Modeled by hand	Tied to that platform	None
Unit of knowledge	Facts	Facts, once you build it	Facts	Varies	Documents and chunks
Matches people, accounts and services across systems	Yes	You build it	By hand	Varies	No
Same answer every time	Yes	If you build it	Yes	Varies	No
Stays current without full rebuilds	Yes	You build it	Varies	Varies	Varies
Permissions checked at every hop	Yes	You build it	Yes	Within that platform	Per tool
Runs in your VPC on your own models	Yes	Yes	Varies	In that vendor’s cloud	Varies
Benchmark on your own questions	Built in	You build it	Varies	Varies	No
Engineering work on your side	Review and approve	A team, ongoing	Months of modeling	Setup per platform	A pipeline per system

Same engine as OM2

Same engine as OM2, with your own ontology.

The same engine as OM2’s organization-wide graph, with a custom ontology on top. Start with one use case, and extend across your company on the same platform.

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FAQ

OM2 Custom Graph FAQ

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What is OM2 Custom Graph?

OM2 Custom Graph builds a custom context graph: a permission-aware graph of facts about one part of your business, such as a codebase, an airline’s operations or a finance team’s accounts payable. Agents query it through MCP or API, so they answer with the business’s own rules, from the same facts every time.

How is it different from OM2’s organization-wide graph?

It runs on the same engine. The organization-wide graph maps your whole company; a custom context graph adds your own ontology on top of the standard OM2 graph. OM2 Custom Graph is its own product, built on that same engine.

Do we have to model the ontology ourselves?

No. OM2 drafts the ontology from your data, and your team, or your partner, reviews and approves it.

Which agents and models can use it?

Any agent that speaks MCP, or anything you build on the API. It works with any model, including your own.

Where does it run?

Hosted by Coworker, or on your own servers so everything stays inside your network. It is decided per engagement.

Can partners deploy it into their clients?

Yes. Forward-deployed partners stand up a custom context graph for each client and deploy it inside the client’s own cloud, without rebuilding the pipeline each time.

How does it stay current?

Changes flow in from the source systems and the graph is continuously updated. When a fact changes, the old one is marked superseded, so agents never mix old and new answers.

Start with one use case

One use case, one question set, two weeks.

Bring a use case and a sample of real data. In two weeks your team will have a context graph built from that data, a reviewed ontology, custom tools your agents can call, and a benchmark against your current retrieval.

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