AI service · RAG & Knowledge AI
Enterprise RAG & AI Knowledge Assistants
Your company's answers are buried in SharePoint folders, Confluence pages, PDFs, tickets, and tribal memory. We build RAG systems that make that knowledge queryable — assistants that answer in seconds with citations to the source, respect your permission model, and stay current as the underlying content changes.
Free scoping call · Clear ROI plan before any commitment
The challenge
Employees lose hours hunting through drives and wikis, or interrupting the one person who knows. Naive fixes fail predictably: enterprise search returns documents instead of answers, and a raw chatbot pointed at your files hallucinates, surfaces stale policy versions, or leaks content the asker shouldn't see. Trust breaks on the first confidently wrong answer.
How we solve it
Production RAG is a retrieval engineering problem, and we treat it that way: document-aware chunking, hybrid semantic-plus-keyword search, reranking, freshness handling, and permission filtering applied at query time so users only ever see what they're entitled to. Answers cite their sources so users can verify, and a retrieval evaluation harness measures answer quality continuously — because 'it seems good' isn't a metric.
Capabilities
What we deliver
The building blocks of a production-grade rag & knowledge ai engagement.
Internal knowledge assistants
Ask-anything assistants over policies, procedures, engineering docs, and project history — grounded, cited, and available in Slack, Teams, or the browser.
Customer-facing answer engines
Support and product Q&A grounded strictly in your documentation, so customers self-serve accurate answers instead of opening tickets.
Retrieval pipeline engineering
Chunking tuned to your document types, hybrid search, reranking, and metadata filtering — the retrieval quality work that separates useful RAG from plausible nonsense.
Permission-aware access control
Your existing entitlements (SSO groups, document ACLs) enforced at retrieval time, so the assistant never becomes a side door around your security model.
Live-sync connectors
Continuous ingestion from SharePoint, Google Drive, Confluence, Notion, Zendesk, and databases, so answers reflect today's documents — not last quarter's snapshot.
Answer quality evaluation
Retrieval and faithfulness metrics tracked on a golden question set, so you know the accuracy rate before rollout and catch regressions after.
How we work
A clear path to production
Five stages, each with visible output — you're never waiting on a black box.
Discovery & scoping
We map the problem, success metrics, constraints, and existing systems before writing code. You leave with a clear scope, timeline, and a fixed view of what 'done' means.
Architecture & design
We design the system end to end — data model, integrations, security, and a path to scale — and validate it against your real workloads, not a demo.
Iterative delivery
We ship in short, reviewable increments. You see working software every sprint, give feedback early, and never wait months to find out it missed the mark.
Hardening & launch
Testing, observability, performance, and security are built in, not bolted on. We launch with monitoring in place and a rollback plan ready.
Support & iteration
After launch we stay on — measuring outcomes, fixing fast, and iterating on what the data tells us actually moves the metric.
Representative stack
- Claude (Anthropic) / OpenAI
- pgvector / Pinecone
- Hybrid search + rerankers (Cohere)
- LlamaIndex / custom pipelines
- SSO / entitlement integration
- Python / TypeScript
Where it applies
RAG & Knowledge AI in the industries we serve
Vertical context changes what good looks like — see how this capability lands in your space.
Healthcare
HIPAA-aware platforms, patient-facing apps, and clinical automation built for security and reliability.
Explore HealthcareFintech
Payments, lending, and financial platforms built for security, reliability, and compliance.
Explore FintechManufacturing
MES, IoT, and predictive AI that improve throughput, quality, and uptime on the floor.
Explore ManufacturingAnswers
Frequently asked questions
How is RAG different from just using ChatGPT with our files?
Uploading files to a chatbot works for one-off questions on a handful of documents. RAG is the production version: thousands of documents continuously synced, retrieval tuned for your content types, permissions enforced per user, answers cited to sources, and quality measured — the difference between a toy and a system a company relies on.
Will the assistant expose documents users shouldn't see?
Not in our builds. We enforce your existing permission model at query time — retrieval only searches documents the asking user is entitled to. Access rules stay in sync with your identity provider, and every query is logged for audit.
How accurate are RAG answers, really?
With well-engineered retrieval and grounded generation, we typically see strong faithfulness on questions the corpus can answer — and, just as important, the system says 'I don't have that' rather than guessing when it can't. We quantify accuracy on a golden set built from your real questions before launch, so the number is yours, not a brochure claim.
What content sources can you connect?
SharePoint, Google Workspace, Confluence, Notion, Slack, Zendesk, file shares, and SQL databases are common; anything with an API or export path is connectable. Scanned PDFs and images are handled via OCR and vision models during ingestion.
Can this run in our own cloud for compliance?
Yes. The full pipeline — ingestion, vector store, and inference via open-weight models or your negotiated API agreements — can run inside your VPC, which is a common requirement for our healthcare and financial clients.
Ready to build with RAG & Knowledge AI?
Book a free consultation and we'll map the fastest path to a working system — with the metric that proves it.