Ingestion and processing
Parsers for PDFs, tables, wikis and legacy formats; chunking tuned per document type, not one-size-fits-all.
Featured Solution
Accurate, Secure Enterprise RAG
Production RAG with strong retrieval quality, access control, citations, evaluation, and cost control — not a weekend demo.
- 90%+answer accuracy on golden sets
- <2sp95 retrieval latency at scale
- 100%of answers cited and permission-checked
Capabilities
RAG that survives contact with real data
Build accurate, secure and scalable retrieval-augmented generation systems for enterprise knowledge.
Hybrid retrieval
Vector, keyword and structured search fused with reranking — because embeddings alone miss exact matches.
Grounded generation
Answers constrained to retrieved evidence with citations, freshness checks and refusal on low confidence.
Evaluation and tuning
Retrieval and answer-quality evals on your golden set, run continuously in CI.
How it works
From document chaos to cited answers
Most RAG demos die on real enterprise data — scanned PDFs, permission sprawl, stale copies. We engineer for that reality from the first pipeline.
Ingestion
Your documents, actually understood
Tables extracted as tables, scanned files OCR'd, versions deduplicated, metadata preserved. Chunking strategies are tuned per content type and validated by retrieval evals — not guessed.
- Parsers for PDF, Office, wikis, tickets and email
- Layout-aware extraction for tables and forms
- Incremental sync so the index never goes stale
Ingest→
Chunk→
Embed→
Index→
Retrieve
PDFConfluenceSharePointSQLTicketsEmail
Grounded answer…with citations [1] [2] and permissions applied
Retrieval
Hybrid search that finds the right evidence
Dense vectors for meaning, BM25 for exact terms, metadata filters for scope, rerankers for precision — fused and tuned against your golden questions.
- Hybrid vector + keyword retrieval with reranking
- Permission filters enforced inside the query, not after
- Query rewriting and decomposition for complex questions
DocsCRMWiki
↓
↓
Answer with citationsscoped to the user's permissions [1] [2]
Trustworthy answers
Cited, current and honest about uncertainty
Generation is constrained to retrieved evidence. Every claim links to its source; low-confidence questions get routed to humans instead of hallucinated.
- Inline citations to the exact source passage
- Groundedness evals with regression gates in CI
- Confidence-based refusal and escalation paths
Groundedness
96%
Safety
100%
Task success
93%
Tone & format
91%
CI gate passedrelease promoted to production
Use cases
Where enterprise rag delivers value
Policy and procedure Q&A
Employees get instant, cited answers from HR, compliance and operations documentation.
Customer-facing knowledge
Support portals and in-product help that answer from your latest docs, not last year's.
Contract and legal search
Clause-level retrieval across thousands of agreements with precise citations.
Research repositories
Scientific, market or technical corpora made queryable in plain language.
Engineering knowledge
Runbooks, ADRs and postmortems surfaced in context during incidents.
Regulatory intelligence
Current regulations and internal controls cross-referenced and searchable.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Two vendors had already failed on our document mess — scanned PDFs, six versions of every policy. This build hit 93% on our golden set and, crucially, tells us when it doesn't know.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- Anthropic Claude
- OpenAI
- Cohere
- Pinecone
- Weaviate
- pgvector
- Elasticsearch
- OpenSearch
- Unstructured
- LlamaIndex
- AWS Bedrock
- Azure AI Search
FAQ
Common questions
Our first RAG attempt disappointed. What went wrong?
Usually retrieval, not the model: naive chunking, vector-only search and no eval set. We diagnose with retrieval metrics first, then fix ingestion and search before touching prompts — that's where the accuracy lives.
How do you handle document permissions?
Source-system ACLs are mirrored into the index and enforced as query-time filters. A user's question can only retrieve what that user could open directly. This is designed in from the start — it can't be bolted on.
How do you measure answer quality?
A golden set of real questions with verified answers, scored for retrieval hit-rate, groundedness and correctness. It runs in CI, so any change — prompt, model, chunking — is gated by evidence.
How fresh are the answers?
Incremental sync keeps indexes current — minutes for critical sources, hourly or daily elsewhere. Answers carry the source document's last-updated date so users can judge for themselves.
Can this feed our agents and copilots too?
Yes. The retrieval layer becomes shared infrastructure: the same permission-aware, evaluated pipeline serves search, copilots and autonomous agents.
Next step
Make your knowledge answer back
Bring your hardest documents and ten real questions — we'll show you what production RAG looks like.