What OCTOPUS is
A harness around the models you already use: a memory the company owns, a record of every decision, people for the calls only people make, and agents that work inside bounds.
In one sentence
OCTOPUS by ARAMAI is the harness for enterprise AI: context graphs that remember, traceable decisions and agent fleet management under a governance plane, inside your own tenant. It runs frontier language models over a typed, linked knowledge graph, so the work they do is grounded, recorded and bounded.
Why a model needs a harness
An AI harness is everything around a model that makes it dependable at work: a memory that keeps what the organization knows, the right context checked before each task, a record of every decision, and bounds on what agents may do. The model is the engine. OCTOPUS is the harness. A model on its own forgets last week, cannot explain why it acted and runs without guardrails.
You keep your model
Yes. OCTOPUS does not replace the model. Frontier language models still do the reasoning: they plan, draft and deliberate. OCTOPUS sits between your data and those models. Knowledge lives in your tenant as a typed graph, context is validated before any model sees it, and decisions are recorded independently of the model that helped make them.
The parts, one line each
- Memory the company owns: what the organization knows, kept as linked facts that persist.
- The decision record: every decision, who made it and on what grounds, readable later.
- The decision ladder: which decisions an agreed rule answers, and which go to a person.
- The Desk: where a person makes the decisions and approvals that belong to a person.
- Agents at work: tasks with a definition of done, closed on evidence, inside bounds you set.
- A copy for every team: a separate copy that holds only one team's material. What stays separate, stated as outcomes: What stays separate between teams.
Who it is for
Enterprises adopting AI that need to hold it accountable: regulated industries, teams putting AI in front of customers, and anyone who must explain an AI decision to a board or an auditor. In practice that is the platform owner or operator who runs more than one agent, needs one shared memory, and wants decisions on the record.