RAG, document intelligence, and conversational AI in production.
GenAI engineering services.
Enterprise RAG
Hybrid retrieval over your private knowledge — Azure AI Search vector + keyword, query rewriting, semantic ranking, and grounding citations.
Document intelligence
Extract, classify, and validate structured data from invoices, contracts, claims, and forms — with Document Intelligence + LLMs working together.
Conversational AI
Customer-facing and internal chatbots with multi-turn memory, persona control, and tool calling — grounded in your business context.
Knowledge pipelines
Ingest, chunk, enrich, and embed at scale. Continuous re-indexing on top of SharePoint, OneDrive, Confluence, S3, and structured sources.
Fine-tuning & customization
Targeted fine-tuning when prompting and RAG aren't enough — for domain language, brand voice, or output structure constraints.
Evaluations & quality gates
Golden datasets, LLM-as-judge, regression tests, and quality dashboards — so non-determinism doesn't ship to your customers.
Where GenAI ships fastest.
- Internal Q&A over policies, contracts, and runbooks
- Customer-facing knowledge assistants
- Invoice and claim extraction with human review
- Sales-enablement chatbots with CRM grounding
- Research assistants across regulatory filings
- Multilingual support automation
From idea to production.
- 01Week 1
Discover
Map data sources, define success metrics, and pick the simplest architecture (prompt vs. RAG vs. fine-tune) that meets the bar.
Output
Architecture decision record
- 02Weeks 2–4
Build
Ingestion pipeline, retrieval logic, prompt engineering, eval harness, and a thin UI for stakeholders to validate.
Output
Working prototype + evals
- 03Weeks 5–6
Harden
Production-ready: caching, rate limits, content safety, PII redaction, observability, and FinOps optimizations.
Output
Production deployment
- 04ongoing
Iterate
Eval-driven improvements based on real usage. We never ship and forget.
Output
Quality dashboard + roadmap
Common questions.
- When is RAG the right choice?
- When the answer lives in your private documents and changes more often than you can afford to fine-tune. Most enterprise use cases land here.
- Do you fine-tune models?
- Sometimes. We fine-tune when prompting + RAG can't hit the quality bar — usually for domain-specific language, structured outputs, or brand voice. We never fine-tune as a default.
- How do you handle hallucinations?
- Defense in depth: grounded retrieval, structured prompts, citation enforcement, eval harnesses, and runtime guardrails. We measure groundedness as a first-class metric.
- What if we don't use Microsoft for our data?
- Our default stack is Azure AI Search + Azure OpenAI, but the patterns work with any vector store and any LLM provider. We optimize for what you already pay for.
Have a knowledge or document use case?
Tell us what you're trying to automate. A discovery brief is enough to know if RAG, document intelligence, or fine-tuning is the right path.