Generative AI · LLM · RAG
Generative AI grounded in your data
Assistants, search and document intelligence that answer from your permissioned enterprise knowledge, with citations, evaluation and the security controls your CISO expects.
Why it matters
Accuracy is a data and engineering problem
Most generative AI disappointments trace back to retrieval, not the model: the wrong document, an outdated version, a table the parser mangled, or content the user shouldn't see. Retrieval-augmented generation (RAG) done well is serious data engineering.
We build RAG and LLM applications with production disciplines: document processing that preserves structure, hybrid search and re-ranking, permission-aware retrieval, citations for every answer, and automated evaluation that catches regressions before users do.
Typical solutions
- Employee and customer assistants grounded in policies, products and procedures
- Enterprise search across SharePoint, Confluence, Google Drive, ticketing and CRM
- Document intelligence for contracts, claims, invoices and medical or regulatory documents
- Content generation workflows with brand, legal and compliance checks
- Analytics copilots that query governed data with natural language
What we deliver
Capabilities
Delivered by senior architects and specialist engineers, with security and governance designed in.
Document processing
Parsing, OCR, table extraction and structure-aware chunking for complex documents.
Retrieval engineering
Hybrid search, embeddings, re-ranking, metadata filters and permission trimming.
LLM app development
Prompt and context design, model routing, streaming UX and integration into existing apps.
Quality evaluation
Golden question sets, groundedness and citation checks, and regression testing.
Security & privacy
PII redaction, data residency, access controls and enterprise model agreements.
Cost & latency optimization
Caching, smaller models where possible, batching and cost-per-query dashboards.
Approach
How the engagement runs
Clear phases, clear exit criteria, and a working result at every stage.
Assess
Value-sized use cases, data and platform readiness, risk assessment and a prioritized roadmap.
Prove
A working pilot on real data with an evaluation suite, cost per task and security review.
Productionize
Integrations, guardrails, observability, human-in-the-loop design and change management.
Scale & operate
Shared AI platform components, new use cases and managed operations with SLAs.
Typical starting engagement
GenAI / RAG Pilot
Fixed scope, fixed fee, and a clear decision at the end. Final pricing is confirmed after a short scoping call.
Scope this engagement →Starting at
- Use case and content source assessment
- Production-ready RAG pipeline on your data
- Permission-aware retrieval with citations
- Evaluation suite with accuracy targets
- Deployment and handover runbook
FAQ
Frequently asked questions
Straight answers to the questions CIOs, CTOs and procurement ask us first.
What is retrieval-augmented generation (RAG)?
RAG is an architecture where an AI model retrieves relevant, permissioned content from your knowledge sources at query time and uses it to generate an answer, typically with citations, rather than relying only on what the model learned in training.
RAG or fine-tuning?
Use RAG when answers depend on information that changes or must be cited and permissioned. Consider fine-tuning to adjust tone, format or domain behavior, or to make smaller models cheaper to run. Many production systems combine both.
How accurate will it be?
We set accuracy targets per use case, measure them against a golden dataset before launch, and monitor continuously. Where accuracy requirements exceed what's achievable, we design human review into the workflow.
Can it respect our existing document permissions?
Yes. Retrieval is filtered by the user's existing access rights so the assistant never surfaces content they couldn't open directly.
Start here
Get a senior architect's view of your fastest path to value.
A 45-minute working session, not a sales pitch. Bring your toughest AI, data or cloud problem; leave with a point of view, a rough plan and a realistic budget range.
- Senior architects onlyNo account managers in the first meeting.
- Fixed-fee optionsClear scope, price and outcomes up front.
- Mutual NDA on requestBefore any detailed discussion.