Production RAG pipelines that ground answers in your data
Retrieval, reranking, and grounded generation designed for the real corpus you have — not a demo dataset. Evaluation harnesses and citations make quality a number you can move.
What a real RAG pipeline needs
Retrieval quality is the whole game. We design every stage — ingestion, chunking, embeddings, retrieval, reranking, and grounding — around measurable answer quality.
Data ingestion pipelines
Batch and streaming ingestion from wikis, ticketing systems, PDFs, and databases, with change-data-capture where it matters.
Chunking & preprocessing
Semantic chunking, structural preservation, and metadata enrichment tuned to your document types.
Embedding strategies
Model selection, dimensionality choices, and per-domain fine-tuning that keep retrieval fast and precise.
Vector database setup
Pinecone, Weaviate, pgvector, or Elasticsearch chosen for your scale, latency budget, and hosting constraints.
Hybrid search & reranking
Dense plus BM25 retrieval and cross-encoder rerankers that lift answer quality on hard queries.
Grounded answer generation
Prompt patterns, citation formats, and low-confidence handling that keep answers tied to retrieved sources.
From messy corpus to grounded answers
Four phases that put retrieval evaluation on the critical path from week one.
Content audit
We inventory sources, structure, freshness, and permissions, and define the questions the system must answer.
Retrieval design
We pick chunking, embedding, and index strategies, and stand up a working retrieval baseline against real data.
Quality tuning
A golden set of questions drives iteration on rerankers, prompts, and grounding rules until quality holds.
Production deploy
We ship behind feature flags, wire tracing and cost dashboards, and set up alerts for drift and regression.
The RAG stack we build on
Retrieval libraries, vector databases, embedding models, and the orchestration and eval tools that keep RAG honest.
Where RAG earns its place
Workflows where the right answer lives in your documents and users need it faster than manual search allows.
Internal docs Q&A
Cross-source retrieval over wikis, playbooks, and design docs with per-user permission enforcement.
Legal & contract review
Question-answering over long contracts, policies, and regulatory filings with citations back to clauses.
Support knowledge base
Agent assist and self-service answers grounded in your help center, past tickets, and product docs.
Sales enablement
Battlecards, competitive intel, and pricing answers delivered fast during live calls.
Compliance research
Grounded Q&A over policy libraries, standards, and internal guidance with an audit trail.
Multi-source enterprise search
A single query surface spanning wikis, CRM, ticketing, and file stores with permission-aware retrieval.
Outcomes tied to answer quality
A well-built RAG pipeline compounds — better retrieval, better answers, more adoption, more feedback.
Answers grounded in your data
Responses cite the documents that produced them, so users can trust and verify what the system says.
Reduced hallucination
Retrieval-first design and low-confidence escalation cut fabricated answers to a measurable level.
Auditable citations
Every answer carries source references, so compliance and QA can reconstruct any response.
Predictable scaling costs
Model routing, caching, and reranking budgets make cost per query a knob you can turn.
RAG in production
A grounded support copilot built end-to-end with a real retrieval and evaluation stack.
AI • RAG
NeuraDesk
A retrieval-augmented support copilot that grounds answers in a company's own knowledge base.
Read Case Study →Why teams choose us for RAG engineering
A senior team that has debugged retrieval quality in production and knows the difference between a demo and a system.
Learn more about us →Retrieval is our craft
Chunking, embeddings, hybrid search, and reranking are core skills, not afterthoughts on top of a chatbot.
Evaluation from day one
A golden question set is defined before we write the first ingestion job, and it drives every design decision.
Honest about limits
We tell you what a RAG system can and cannot answer given your corpus, and where a different approach fits better.
Own the outcome end-to-end
Ingestion, retrieval, generation, monitoring, and cost — one team accountable across the whole pipeline.
Questions teams ask us about RAG
Practical answers on retrieval, evaluation, and how RAG systems actually reach production.
Do we need a vector database?
How do you measure retrieval quality?
How do you deal with permission-sensitive documents?
How do you keep the index fresh?
How do you handle low-confidence answers?
Can we swap the model later?
Ready to design a RAG pipeline that holds up?
Book a working session and we'll map your corpus, questions, and success criteria into a concrete build plan.
Book a Discovery Session →