Specialized RAG Systems
Retrieval-augmented generation tailored to your domain and data.
Why RAG (and why us)
- Reduce hallucinations with grounded answers and citations.
- Keep sensitive content out of models—control what goes to the LLM.
- Ship faster: reference architecture, guardrails and evaluation harnesses.
Capabilities
- Multi-tenant architecture with per-tenant isolation
- Policy-aware chunking & citation controls
- Evaluation harnesses and production monitoring
- PII redaction & access control integration
Reference Architecture
- Ingestion → parsing → policy-aware chunking → embedding
- Hybrid retrieval (dense + keyword) with re-ranking
- Guardrails: allowed sources, section-level filtering, citation enforcement
- Answer assembly with verifiable citations and confidence score
Safety & Compliance
- OWASP LLM threat modeling and abuse prompts filters
- PII detection/redaction and RBAC/ABAC integration
- Audit logs, evidence packs and change control for prompts/models
- PCI DSS scope minimization (no PAN storage; SAQ-A patterns with Stripe where applicable)
Evaluation & Quality
We deliver an evaluation harness (datasets, prompts, scoring) you can run in CI/CD.
- Answer faithfulness / groundedness
- Precision / Recall (top-k) and MRR / nDCG
- Citation coverage & correctness
- Toxicity, privacy and policy violations
Integrations
- Vector stores: Pinecone, pgvector/Postgres, Qdrant
- Orchestrators: LangChain, LlamaIndex, custom
- Clouds: AWS, GCP, Azure; deploy on Vercel for web edge
- SSO, secrets, observability (OpenTelemetry, logging, tracing)
Delivery in 3 Phases
- Discover & Design → Sources, policy, threat model, evaluation plan
- Implement & Validate → Pipelines, retrieval, guardrails, eval harness
- Operate & Improve → Monitoring, drift checks, feedback & iteration
Outcomes
Higher answer accuracy, defensible citations, and safer operations for regulated industries.