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.

Request a demo