Knowledge Engineering
The grounded knowledge backbone behind every Skygena agent.
Most AI projects fail at the retrieval layer, not the model layer. Models hallucinate when they are handed messy, unstructured, untagged documents and asked to find a needle. Skygena Knowledge Engineering — led by our Chief Scientist — designs the ontology, the retrieval architecture and the document understanding pipelines that make every downstream agent grounded, accurate and explainable. It is the work most AI vendors skip and most AI projects regret skipping.
Outcomes we commit to
Hallucinations driven near zero
Every answer cites a source. No source, no answer. Confidence thresholds enforced at the runtime, not just at the prompt.
Faster, smaller, cheaper agents
When the knowledge layer is right, the agent on top is small, fast and predictable — and your model bill drops.
Audit-grade traceability
Every retrieval call is logged with the documents that informed the answer. Regulators love it. So do your auditors.
How we work
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1. Source mapping
We inventory every relevant document source — file shares, intranet wikis, contract repositories, ticketing systems, drawings, transcripts — and prioritise what should feed which use case.
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2. Ontology & retrieval architecture
We design the entity model and the semantic structure your agents will reason against, and pick the right hybrid retrieval mix (pure vector is rarely the answer in the enterprise).
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3. Extraction & indexing
We build extraction pipelines for the messy real-world formats — PDFs, scans, engineering drawings, call transcripts — and index them into a hybrid retrieval store.
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4. Evaluation & operation
Every retrieval call is measured against a golden set of question/answer pairs you co-author with us. We never ship a knowledge layer that has not been evaluated, and we wire the refresh policy that keeps it current.
Frequently asked
- Do we need a vector database?
- Often yes, sometimes no. Pure vector search is one tool — for many enterprise use cases, hybrid lexical + vector + structured filters outperforms vector alone. We pick the right mix per project, not a one-size-fits-all stack.
- How do you handle data freshness?
- Every Skygena knowledge layer ships with a refresh policy and an automated re-indexing pipeline. You decide whether updates are real-time, hourly, nightly or on a publishing event — we wire it.
- Can this run inside our perimeter?
- Yes. We default to EU-hosted under DPA, and we can deploy fully inside your cloud, on-prem or in an air-gapped environment if your compliance requires it.