Grounded enterprise AI through governed retrieval and evaluation.
Knowledge ingestion through observation — with access control, freshness, grounding, and evaluation designed to reduce hallucinations without promising their elimination.
- SourcesDocs, systems
- RetrievalGoverned, ranked
- GenerationGrounded
- ObservationSignals
Retrieval without governance
Knowledge ingestion through observation — with access control, freshness, grounding, and evaluation designed to reduce hallucinations without promising their elimination.
Operational properties we design for — never guaranteed metrics.
Governed RAG flow
Representative flow from sources to generation with governance at retrieval and evaluation gates.
Identify high-value unstructured data repositories and establish secure connectivity.
Extract and semantically chunk content while preserving critical document hierarchy and metadata.
Compute high-dimensional embeddings and store them with row-level security and access policies.
Execute hybrid search queries constrained by the user's explicit enterprise identity and permissions.
Dynamically assemble context windows prioritizing relevance and strictly filtering out-of-bounds data.
Instruct the LLM to synthesize answers explicitly grounded in the provided, verifiable context.
Run continuous CI pipelines that measure response hallucination and citation accuracy.
Monitor retrieval latency, relevance scores, and generation costs in real-world usage.
Ranking and context construction are policy-constrained; evaluation gate scores groundedness before promotion.
Governing retrieval
Governed RAG systems: ingestion, retrieval, context, generation, evaluation, and observation with grounding and access controls — patterns, not guarantees.
Delivery, phase by phase.
Four phases with visible artifacts — the engagement spine applied to this problem.
- 01
Inventory knowledge
Sources, freshness, ownership, and access model.
Ingestion & chunking blueprint - 02
Design retrieval
Index, ranking, and context with policy gates.
Access-controlled index reference - 03
Build generation
Grounded generation with guardrails and evidence.
Context construction policy - 04
Operate
Observe retrieval quality and tune over time.
Evaluation harness (groundedness, relevance)
EVERY PHASE PRODUCES AN ARTIFACT — NO BLACK BOXES
Questions engineering teams ask before production.
Clear answers on delivery, security and operations — no sales theatre.
No. They reduce exposure by grounding answers in governed retrieval and evaluation. Residual risk is managed via observability and guardrails — we do not promise elimination.
At retrieval and context assembly: identity-aware retrieval with data-boundary checks and audit trail, not post-hoc filtering.
Ingestion tracks recency and ownership per document/chunk, with re-index policies and evaluation that flags stale retrievals.
Bring your knowledge. We will map the grounded path.
Tell us about your systems, constraints and goals — we will map the architecture, controls and operating model.