Object Edge // Hive

Your company already knows. Its systems can’t say it.

Hive is a company brain — one connected layer across the systems you already run. Enterprise data fabrics are measured in quarters, because they ask you to model the business before you get any value out of it. Hive inverts that. Live in one to two weeks, because the engineering went into extraction and persistence instead of into your modelling homework.

SOC 2 compliantCISO-certifiedDeploys in your cloudModel-agnostic



Definition

What is a company brain?

A company brain is a semantic layer over the systems a business already runs — one model of what the company is made of and how those things relate, kept current against the systems that change.

Underneath, that model is a knowledge graph: typed entities, typed relationships, temporal history, and provenance on every edge. It is built on your own ontology — your vocabulary, not a generic schema — so a cruise operator reasons about decks and port calls while a distributor reasons about territories and renewals.

The distinction from search is the whole point. Search returns documents that mention a thing. A company brain returns the thing itself, what it connects to, who owns it, what changed last week, and where every claim came from.

Product site: hive-os.ai ↗

Common Questions

What is a company brain?

A company brain is a semantic layer across the systems a business already runs, implemented as a knowledge graph with typed entities, typed relationships, temporal history, and provenance. It lets people and AI agents query the business as one connected system instead of a dozen disconnected ones.

How is a company brain different from a data warehouse or lakehouse?

A warehouse stores rows for analysis. A company brain stores meaning for reasoning — entities, the relationships between them, how they changed over time, and where each fact came from. Warehouses answer what the numbers were. A company brain answers who owns this, what depends on it, and what changed.

What is a semantic layer?

A semantic layer is a model of what a business is made of — its entities, their relationships, and the vocabulary it uses for them — sitting between raw systems and the people or agents asking questions. It is what lets a query be phrased in business terms rather than table joins.

Why does an ontology matter for enterprise AI?

An ontology encodes a company’s own vocabulary and structure. Without one, an AI system reasons in generic terms and produces generic answers. With one, it reasons in decks and port calls, or queues and escalations, or territories and renewals — whatever the business actually uses.

Does Hive replace Copilot, ChatGPT, or Claude?

No. Hive sits underneath whatever AI seats an organization has already deployed and gives them shared company context. Organizations that would rather consolidate can route through Hive’s own hosted models instead, but replacement is optional.

How long does Hive take to deploy?

One to two weeks to connect systems and stand up the company brain. Four to eight weeks to put a complete solution to a named business problem into production, on a fixed bid.

Is Hive secure enough for regulated industries?

Hive is SOC 2 compliant and deploys inside your own cloud. Access is role-based and enforced at the query rather than by keeping data in separate silos, and every answer carries provenance and an audit trail.

Can we use our own AI models with Hive?

Yes. Hive is model-agnostic. Sayya, the agent harness, routes through your existing Microsoft, OpenAI, or Anthropic enterprise agreement, through Object Edge’s provider keys, or through hosted models — and different queries can route to different providers.


See Hive in Action

The report writes itself. You approve it.

A heartbeat fires at 10 a.m. with nobody watching, reads the overnight across email and meetings, and drafts the leadership update in the CEO's own voice — every line traceable back to the meeting or thread it came from. Ninety seconds of judgment instead of an hour of writing.

Bring us the problem

Tell us which system boundary keeps costing you — the renewal that slipped, the AI rollout that never had a foundation, or the week you can’t get back.