Accurate, citeable answers from your documents, lakes, and systems of record.
Gensten builds enterprise RAG systems that ground LLMs on your private data. We design ingestion pipelines, data chunking strategies, embedding models, hybrid search, rerankers, citation UX, and evaluation - on Azure AI Search, Fabric/OneLake, or open vector databases - so agents and copilots answer with evidence, not hallucinations.
How RAG fits into a production agentic stack.
Connectors for SharePoint, OneLake, CRM, PDFs, tickets, and APIs.
Semantic/structural splits, metadata, PII handling, ACL propagation.
Embedding models, vector + keyword indexes, refresh schedules.
Query rewrite, hybrid retrieval, cross-encoders, context packing.
Grounded prompts, citations, refusal policies, hallucination checks.
Agents fail when retrieval is weak. We treat retrieval quality as a product: measure recall@k, citation coverage, and groundedness, then feed results back into chunking and embedding choices.
For Microsoft-centric estates we wire RAG to OneLake and Fabric IQ so analytics and unstructured knowledge share one governed lakehouse - with Foundry IQ agents retrieving through certified tools.
Use RAG when facts change often or live in enterprise documents. Fine-tune for style, domain language, or task format. Most enterprises need both: RAG for knowledge + light adaptation for behavior.
RAG retrieves relevant enterprise content at query time and feeds it to an LLM so answers are grounded in your data with citations - reducing hallucinations versus prompting alone.
Common causes: poor chunking, weak embeddings, missing ACLs, stale indexes, no reranking, and no evaluation. We treat retrieval quality as a product metric, not a one-time setup.
Yes. We combine keyword and vector search with query rewrite and reranking so exact terms (IDs, codes, names) and semantic matches both surface correctly.
Retrieved chunks carry source metadata (document, URL, page, ACL). The generation layer is prompted to cite sources, and we evaluate citation coverage in the test harness.
Yes. We propagate ACLs into the index and filter retrieval by user/group identity so agents only see content the requester is allowed to access.
Azure AI Search for Microsoft estates, plus Pinecone, Weaviate, or pgvector when preferred. Choice depends on scale, hybrid search needs, and cloud strategy.
It depends on content volatility. Policies, runbooks, and tickets may need near-real-time or hourly updates; stable corpora can refresh daily with change detection.
RAG is necessary for knowledge grounding but not sufficient alone. Agents also need tools, orchestration, evals, and ADLC operations for production outcomes.
Yes. We design OneLake document and table layouts, then embed and index for Azure AI Search or other stores so Foundry agents retrieve from governed lake content.
The foundation of RAG quality: intelligent data chunking, embedding model strategy, metadata design, and index hygiene.
Build, adapt, and operate LLMs for enterprise workloads - prompt systems, fine-tuning, evaluation, hosting, and cost/performance tradeoffs.
Design, build, and operate enterprise AI agents that plan, use tools, orchestrate workflows, and deliver measurable business outcomes.
OneLake as the enterprise data foundation for Fabric IQ, RAG corpora, and Foundry agents - one lake, many workloads.
Talk to Gensten about ADLC, RAG, LLM building, Foundry IQ, Fabric IQ, and OneLake - scoped to your KPIs and compliance needs.