AI Knowledge Products
Knowledge layers that make AI accurate — in production.
These are reference deployments: engineered knowledge bases that ground every AI response, enforce compliance at the retrieval layer, and refuse to guess outside their boundary. Architecture, not a document dump.
Why reference deployments matter
The knowledge layer is where most AI systems quietly fail.
Teams deploy RAG pipelines, upload documents, generate embeddings, and move on — then wonder why the AI answers inconsistently, misses obvious questions, or hallucinates on the edges. The model is rarely the problem. The knowledge layer was never architected.
The deployments below are the opposite: knowledge bases designed with a chunking strategy, a metadata schema, access control, an update trigger, and an evaluation loop. The result is AI that knows what it's supposed to know — and says "I don't know" when it shouldn't.
Reference deployments
Knowledge bases we've engineered and run.
Anonymized from real production systems across automotive, financial services, and consumer healthcare.
Automotive compliance knowledge base
A curated layer embedding TCPA/FCC/FTC rules, 200+ OEM communication requirements, DNC Registry behavior, and state-by-state regulations. Every agent queries the compliance layer before an outbound action — so violations are caught at retrieval, not at output. Refreshed on regulatory change via automated monitoring.
Financial intelligence knowledge base
A multi-source system for a US investment firm — portfolio data, research, CRM records, and external feeds unified into one queryable layer. Analysts ask in natural language; every answer cites its source document and extraction date, with role-based access to data subsets and a monthly hallucination-rate check against a golden dataset.
Veterinary knowledge base
A domain knowledge base built from veterinary references, breed-specific health guidelines, medication-interaction data, and symptom-to-condition mappings — validated at 91% accuracy against a licensed veterinary knowledge base, with hard guardrails preventing the AI from making clinical diagnoses.
What makes them work
The same engineering under every deployment.
Compliance at retrieval
Gate, don't filterRules are embedded in the knowledge layer and queried before an action executes — so a non-compliant response is prevented upstream, not scrubbed after generation.
Source-cited answers
TraceableEvery response points back to the source document and its extraction date. No anonymous assertions — analysts and auditors can verify the provenance of any answer.
Boundary behavior
Know the unknownHard guardrails define what the system must never output and how it behaves outside its knowledge boundary — it declines rather than hallucinates on the edges.
Continuous evaluation
Stays accurateA golden dataset and automated evaluator track retrieval accuracy and hallucination rate over time, with change-triggered re-ingestion keeping the knowledge current.
What you get