Give AI agents context they can trust.
Orient turns organisational work into structured meaning objects: claims, sources, evidence, assumptions, contradictions, decisions, confidence, and permissions. So agents reason from context instead of guessing from files.
// structured meaning — not a pile of docs context = assembleMeaningContext(…) current_answer → "enterprise-first" grounding_status → "resolved" weak_spots → ["pricing ageing"] must_not_claim → ["compliance demand"] safe_to_act → true
Without Orient, agents retrieve information and improvise.
With Orient, agents inherit the organisation's settled understanding.
Most agent stacks bolt retrieval onto a model and hope. The model still has to guess what matters, what's current, and what it's allowed to claim. Orient resolves that upstream.
Agents retrieve chunks and pretend they understand. They search documents, tickets, transcripts and wikis, then infer what matters from fragments. The result is fluent uncertainty: agents that sound confident while reasoning from incomplete organisational meaning.
- → Whatever passages look most like the question
- → No sense of what is current or stale
- → No record of what's contested or unproven
- → No constraints on what may be said
- → No memory of what's already been decided
Agents act from resolved understanding. Orient gives each agent the current answer, its evidence, contradictions, confidence, permissions and freshness. The result is trustworthy action: agents that don't just access company knowledge, but inherit the organisation's meaning.
- → The current answer and its grounding status
- → Supporting evidence, contradictions and weak spots
- → What's resolved vs. still an open question
- → The constraints, tone and claims it must respect
- → Whether it's safe to act — or time to escalate
Orient is the meaning layer that turns enterprise agents from fluent searchers into trustworthy actors.
Five things every agent can ask of Orient.
The same surfaces that make Orient useful to people make it usable by machines — because every human action already produced structured meaning underneath.
Context
The right working memory for the job — the question, the current answer, the relevant claims, the weak spots and the constraints. Meaning an agent can use, not chunks to sift.
Grounding
Every claim tied back to support, contradiction, provenance and freshness — so an agent acts from evidence instead of hallucinating confidence.
Policy & Constraints
Durable rules of meaning and behaviour — tone, lexical contracts, taboo phrases, accepted risks and decision principles. Agents inherit how the organisation thinks and speaks.
Task Framing
A human request, turned into an agent-ready frame — desired outcome, audience, evidence standard, risk level and memory scope. Agents understand the job, not just the prompt.
Evaluation
The same Meaning Health Score people get on their work, turned on agent output — did it stay clear, supported, coherent and on-contract, against source, intent and audience? Integrity, not just factual correctness.
Ask anything for its meaning — and get back what that thing actually is.
A document is a source. An answer is a position. A deck is a sequenced argument. A person is a perspective. One call returns the right kind of meaning for each.
Orient makes organisational meaning addressable.
Agents shouldn't scrape a folder and improvise. Under the surface, Orient is epistemic infrastructure for organisations and AI agents — every object carries provenance, boundaries and confidence an agent can inspect.
Four families, one flow.
Every object in Orient sits somewhere on the path from raw material to shared understanding — and an agent can consume meaning at any stage.
Source
“What meaning is inside this material?”
Synthesis
“What position have we formed from many sources?”
Communication
“How has that meaning been shaped for an audience?”
Propagation
“How did it move, and who actually understood it?”
Company uncertainty, turned into an agent work-queue.
Resolver isn't a knowledge base — it's a live state of inquiry. It tells agents what's known, what's decided, what's still open, what's too weak to claim, and when to ask a human.
can_answer_from → enterprise-first positioning, Q3 pricing confidence → high on positioning · ageing on pricing must_not_claim → compliance demand, SOC-2 timeline should_investigate → mid-market willingness to pay should_escalate → data-residency commitments
Every agent in the company, working from the same understanding.
One meaning layer, many consumers — each pulling the slice of understanding its job requires.
Research agents
Pick the highest-priority open probes, gather evidence, flag contradictions and propose resolutions.
Writing agents
Draft from resolved answers, preserve caveats and confidence, and flag when copy runs ahead of evidence.
Sales agents
Use approved positioning and claims; turn recurring customer objections into new probes.
Support agents
Answer from resolved questions, escalate the open ones, and never invent policy.
Product agents
Turn feedback into probes; see which unresolved questions block a roadmap decision.
Executive agents
Brief leaders on what's known, unknown, and what genuinely needs human judgement.
Meeting agents
Build agendas from unresolved probes; capture, assign and update them afterward.
Coding agents
Know why a decision was made; avoid building against requirements that are still open.
Six things agents can finally do well.
Answer from resolved knowledge
Use the organisation's current, defensible answer instead of re-deriving it.
Refuse unsupported certainty
Say “this is still an open probe” rather than turning a guess into a confident claim.
Investigate what matters next
Work the highest-value unresolved questions, not whatever's nearest in the index.
Escalate at the right moment
Hand back to a human exactly when the decision requires judgement.
Preserve decision memory
Respect what's already been decided instead of casually reopening it.
Improve the graph
Write back new evidence and what was learned, so understanding compounds.
Give your agents the organisation's understanding.
Orient is the meaning layer between messy human knowledge and intelligent agent action. Request access to the platform.