Rag That Scales

RAG · Scalable Retrieval

Build RAG that Scales — fast, accurate, and cost‑aware across your org.

Production‑grade Retrieval‑Augmented Generation with robust ingestion, smart chunking, schema‑rich metadata, high‑recall search, and airtight governance. Ship grounded answers with citations your teams can trust.

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RAG pipeline visualization
Ingestion + Chunking

High‑Fidelity Indexing

Automated pipelines for PDF/HTML/DOCX/MD. Adaptive chunking, dedupe, table extraction, and embeddings tuned for retrieval.

Search + Rerank

High Recall, High Precision

Hybrid keyword + vector search with cross‑encoder rerankers. Filters by metadata, time, and permissions—always with citations.

Governance

Secure by Default

Row‑level security, PII redaction, retention windows, and audit trails. Multi‑tenant isolation for enterprise teams.

Connect Sources

CRMs, wikis, SharePoint, GDrive, S3. Normalize schemas and set least‑privilege connectors.

Ingest & Chunk

Parse PDFs/tables, split semantically, attach metadata (owner, product, region, version), then embed.

Retrieve & Rerank

Hybrid search + filters → cross‑encoder rerank → grounded context window with citations.

Evaluate & Scale

Golden‑set evals, hallucination checks, caching & cost controls, monitoring, and alerts.

  • Support Deflection Trusted, cited answers from product docs and past tickets—right in the help center.
  • Sales Enablement One‑click briefs from pricing, playbooks, and CRM notes—always current.
  • Engineering Search Query code, ADRs, runbooks with repo‑aware retrieval and permissions.
  • Compliance Q&A Policies and controls surfaced with sources and effective dates.
  • Finance Policies Contracts and terms summarized with thresholds and exceptions.
  • Research Assistant Multi‑source literature review with deduping and citation graphs.

Ready to see RAG ship reliable answers?

We’ll map a 1‑week pilot with success metrics and an ingestion plan.

Schedule a call
How do you prevent hallucinations?

Strict grounding via citations, retrieval score thresholds, cross‑encoder rerankers, and answer abstention when confidence is low.

What about permissions and PII?

We enforce row‑level security, scope tokens per connector, redact PII at ingest, and log access for auditability.

Which vector store / LLM do you use?

We’re store/LLM‑agnostic: Pinecone, Qdrant, or pgvector; OpenAI, Anthropic, or local SLMs—picked to fit cost, latency, and privacy.