Good fit when you need
- Internal Q&A over policies, SOPs, or product knowledge
- Cited answers for support, sales, or operations teams
- A retrieval layer that later feeds agentic tools
Track A · AI Products
Domain RAG & Enterprise Search
Hybrid semantic and lexical vector search over proprietary documentation, PDFs, and relational stores.
Overview
Answers that can be traced to a source
Enterprise RAG is not a generic chatbot over a public model. We index the corpora you designate—policies, product docs, tickets, structured records—then retrieve before generate so responses stay attached to evidence.
Access rules travel with retrieval. Users should not see chunks they could not open in the source system. When the corpus has no answer, the product should say so rather than invent one.
Capabilities
What we implement
Grounding first. Generation second.
Ingestion pipelines
Chunk and refresh PDFs, wikis, tickets, and database extracts on a schedule you control.
Hybrid retrieval
Keyword plus vector search, with filters for tenant, role, and document class.
Citations
Show sources next to answers so reviewers can open the original passage.
Access-aware indexes
Respect existing permissions rather than flattening everything into one search space.
Evaluation
Measure groundedness and refusal quality on a held-out question set before go-live.
Product surfaces
Embed retrieval in portals and apps your teams already use—not only a standalone chat page.
Benefits
Why teams choose this path
Practical outcomes for knowledge that already lives in your estate.
- Less hallucination pressure because retrieval is required
- Easier review for legal, QA, and operations stakeholders
- A corpus you can expand without redesigning the product
- Cleaner handoff into agentic workflows later
Process
How We Deliver
01
Corpus map
Decide what is in-scope and who may see it.
02
Index
Ingest, chunk, and apply metadata filters.
03
Ground
Tune retrieval and citation UX on real questions.
04
Evaluate
Score groundedness, refusals, and latency.
05
Operate
Refresh pipelines and monitor drift.
Technologies
Tools We Work With
Retrieval
pgvectorPineconeQdrantChromaDBRedis
Models & cloud
LlamaIndexLangChainAzure OpenAI
Delivery
REST APIsEvaluationAccess filters
FAQ
Common Questions
No system can guarantee that. RAG reduces unsupported answers by requiring retrieval and citations, plus evals and refusals when sources are weak.
Architecture is scoped with you—private indexes, chosen model endpoints, and data-handling rules before any corpus is ingested.
Yes. We prefer a bounded corpus and a question set you already ask, then expand once quality holds.
Need answers tied to your documents?
Bring a corpus and a sample question list—we’ll outline a retrieval approach you can govern.