A blueprint for enterprise AI you can actually put into production.

Enterprise data context

AI that understands the meaning, relationships and structure within your fragmented data estate.

Enterprise data does not arrive as a clean corpus. It arrives as forty systems that describe the same customer four different ways, a decade of documents nobody has re-read, and the tacit knowledge that makes those systems legible to the people who use them daily. The knowledge layer is where that estate becomes something a model can reason over: entities resolved, relationships mapped, documents parsed with their structure intact, and every retrieved answer traceable back to the record it came from.

• Multi-modal ingestionStructured records, documents, transcripts, tickets and code — parsed with structure and provenance preserved.
• Entity and relationship graphResolve the same entity across systems and model the relationships that make enterprise questions answerable.
• Retrieval with evidenceEvery answer carries citations to source records, so an auditor can follow the reasoning rather than trust it.
• Training data generationTurn your operational corpus into supervised fine-tuning sets for domain models, without a labelling programme.
• Domain-scoped knowledge basesMulti-tenant, permission-aware knowledge scoped by business unit, jurisdiction and sensitivity.
• Freshness operationsDetect stale content, changed policy and drifted documentation, and keep the corpus current as the business moves.

Token economics, sovereignty and control

Domain-specific intelligence that complements expensive frontier calls — and the freedom to run on any cloud, any infrastructure, with any model.

Most enterprise AI economics break at scale for the same reason: a frontier model is called for work a much smaller, domain-tuned model does at least as well. The model layer routes each task to the cheapest model that meets the quality bar, fine-tunes small models on your own operational data, and keeps the whole arrangement portable. Nothing in the architecture assumes a particular vendor, and nothing prevents you from changing your mind about one.

• Token economicsRoute by task, not by habit. Domain-tuned small models handle the volume; frontier models handle the hard tail, at a fraction of the blended cost.
• Automated fine-tuningProduce and maintain domain models from the knowledge layer's training sets without standing up an MLOps function.
• Model-agnostic by designProprietary, open-weight and self-hosted models behind one interface, swappable without rewriting the agents above.
• Sovereignty and residencyRun in your cloud, your data centre, or air-gapped. Data residency and processing boundaries are enforced, not promised.
• Regression on model changeSwapping a model is a change like any other: the eval suite runs, and you see what moved before it reaches production.
• Cost and performance accountingCost, latency and quality per task, per process and per business unit — attributable and chargeable.

Build, orchestrate and supervise

Design agents in natural language, orchestrate them across your systems, and supervise them the way you supervise any other operation.

An agent that can act is an operational asset with real privileges, and it needs the governance that implies: a defined scope, permissions on the tools it may use, an evaluation history, an escalation path, and someone accountable when it is wrong. The agentic layer is a runtime for building and orchestrating agents across your business systems, and an operating console for running them once they are live.

• Natural-language agent builderDescribe the process; refine it visually. Business owners can read and correct the definition without reading code.
• Multi-agent orchestrationCompose specialists into workflows across systems, with hand-offs, retries and compensating actions defined explicitly.
• Tool and permission governanceEvery agent has a declared scope of systems and actions, reviewable and revocable, enforced at the runtime.
• Evaluation and regression harnessEach process carries a test set. Quality is measured continuously and regressions are caught before deployment.
• Human supervision consoleException queues, approval gates and confidence thresholds — the human path is designed, not improvised.
• Full audit and lineageWhat the agent did, what it read, which model answered, what it cost and who approved it — for every action.