Enterprise RAG
Turn scattered enterprise knowledge into a trustworthy, permission-aware answer engine that cites its sources and never leaks data.
Enterprise RAG grounds large language models in your own documents, wikis, tickets, and databases so answers are accurate, current, and traceable. We build the full retrieval pipeline — ingestion, chunking, hybrid search, reranking, and citation — with document-level access controls that respect who is allowed to see what. Employees and customers get precise, sourced answers instead of hallucinations or hours of searching.
Problems we solve
The operational bottlenecks that hold enterprises back — and where AI delivers measurable impact.
Knowledge is fragmented across dozens of systems
Answers live in SharePoint, Confluence, PDFs, ticket histories, and databases. Employees waste hours hunting, and half the time settle for stale or wrong information.
Off-the-shelf chatbots hallucinate
A raw LLM confidently invents policies, prices, and procedures. In regulated or customer-facing contexts, an unsourced wrong answer is a liability, not a feature.
Naive RAG returns irrelevant chunks
Basic vector search retrieves the wrong passages, buries the answer, or misses it entirely on real enterprise corpora with tables, acronyms, and conflicting versions.
Access control gets ignored
A search assistant that surfaces HR, legal, or customer PII to anyone who asks is a breach waiting to happen. Most quick builds skip permissions entirely.
What we build
Production-grade capabilities, engineered for enterprise scale, security, and reliability.
Multi-source ingestion pipeline
Connectors for SharePoint, Confluence, Google Drive, S3, databases, and ticketing systems keep the index current with incremental sync and change detection.
Hybrid search with reranking
We combine dense vector retrieval with keyword and metadata filtering, then rerank candidates so the model sees the most relevant evidence, not just the nearest embeddings.
Document-level access control
Retrieval respects your existing permission model, so each user only ever sees answers grounded in documents they are authorized to read.
Grounded answers with citations
Every response links to the exact source passages, letting users verify claims and building the trust that drives adoption.
Structure-aware chunking
Tables, sections, and long documents are parsed and chunked intelligently so context is preserved rather than shredded mid-sentence.
Evaluation and drift monitoring
A continuous eval harness scores answer accuracy and retrieval quality, catching regressions when your content or models change.
Why it matters
- 65% less time spent searching for information
- Sourced, verifiable answers on every query
- Hallucination rate cut to near zero on covered topics
- Permission-aware retrieval out of the box
- Faster onboarding for new employees
- Deflects repetitive support and internal-help tickets
Implementation roadmap
Corpus assessment
We inventory your content sources, permission models, and highest-value question sets, then define the evaluation benchmark we will hold the system to.
Pipeline build & tuning
We stand up ingestion, hybrid retrieval, and citation, then iterate against the benchmark until retrieval and answer quality clear the agreed bar.
Production launch
Access controls are wired to your identity provider, the assistant is embedded in Teams, Slack, or your portal, and we ship with monitoring live.
Expand & maintain
We add sources, tune on real usage, and track drift so accuracy holds as your knowledge base and the underlying models evolve.
Common questions
The model is constrained to answer only from retrieved sources and instructed to say when it cannot find an answer. Every claim carries a citation, so users and auditors can verify it. Our eval harness continuously measures how often answers stay grounded.
Yes. Retrieval is filtered by each user identity against your existing access model, so a user can never receive an answer built from a document they are not entitled to see.
The architecture is model-agnostic. We can route to Claude, other hosted frontier models, or open models running in your VPC, and switch as your requirements and budgets change.
Connectors sync incrementally, so new and updated documents are typically searchable within minutes to hours depending on the source. Sync frequency is configurable per source.
Read access to the target content sources and your identity provider, plus a few subject-matter experts to help define and grade the initial question set. We handle the rest.