Enterprise search & internal AI
Connect internal knowledge to AI through versioned ingestion, permission-aware retrieval, and cited answers. We test what the system finds, what it excludes, and when missing evidence should stop an answer.
A runbook may be superseded, a discussion may contradict approved guidance, or a document may belong to another team. Relevance alone cannot decide what enters an answer.
Inside the system
Illustrative question: Can I replay INV_SYNC_TIMEOUT, and when will sync recover? The answer depends on source versions, requester permissions, and what the retrieved passages actually support.
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Parse documents into section-aware chunks. Preserve source IDs, versions, access metadata, and sync checkpoints. Changes replace superseded chunks; deletion events remove their indexed copies.
Resolve tenant and group membership from trusted identity. Enforce document permissions on every search and fetch, before passages reach a reranker or model. Missing authorization fails closed.
Combine exact error-code matches with semantic retrieval. Merge and rerank authorized candidates, then check source versions and conflicts. Search scores rank relevance; they do not certify an answer.
Generate from the permitted passages. Validate citation IDs and source spans, then assess whether each claim is supported. Withhold unsupported instructions and distinguish an unknown recovery time from a documented procedure.
Inspect the behavior
Simulated snapshots at 10:00 UTC. The question stays the same; access and source state determine whether an answer is supported. No systems are connected.
Illustrative system · Synthetic inputsCan I replay INV_SYNC_TIMEOUT, and when will sync recover?
The runbook says to check the existing job before replaying. [RB-17 v7, §3] Recovery time remains unknown without live status evidence. No job is replayed.
Verification specification
We turn expected behavior into repeatable tests and operating controls. Acceptance thresholds are agreed for your workflow, data, and the consequences of an error.
Replay identical questions across roles, tenants, and revoked memberships; inspect retrieval, model context, citations, and caches.
No unauthorized content or identifying metadata in the tested paths.
Label required evidence for real questions. Measure recall@k and ranking, then test edits, deletion, permission changes, and connector lag.
Meet agreed retrieval targets; exclude deleted, superseded, or disallowed stale evidence.
Check citation spans and claim support separately. Include unanswerable questions, conflicting documents, and instructions planted inside retrieved content.
Meet reviewed answer-quality and abstention targets within the agreed latency and cost budget.
The handover
A defined implementation, reviewable evidence, and an operating model your team can own.
How we workConnector scope, document identity, ACL mapping, version fields, freshness policy, and deletion/revocation behavior.
Parsing and indexing, access filters, hybrid retrieval, reranking where justified, citation mapping, and evidence-gap responses.
Labeled queries, role-isolation and abstention cases, sync monitoring, trace retention, and reindex/recovery procedures.
Architecture decisions
We assess each source's permission model and connector capabilities. User identity and document permissions must be enforced during retrieval, before content enters model context. If a connector cannot preserve those boundaries, we restrict the source or design a separate access layer.
Often, the first task is improving retrieval and context rather than training a model. We evaluate retrieval-augmented generation against real questions before considering fine-tuning. The choice depends on the required behavior, available data, and how frequently the underlying knowledge changes.
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