RAG pipelines
Retrieval-Augmented Generation done right is mostly about the retrieval, not the model. We get the chunking, embedding strategy, and vector retrieval correct so the system answers from your documents — with measurable reductions in wrong answers — rather than hallucinating confidently.
What you get
- Document ingestion, chunking, and embedding strategy
- Vector retrieval tuned for precision over recall noise
- Source citations and answer-grounding
- Eval set so you can measure accuracy, not vibe-check it
How we deliver.
A structured engagement from discovery to deployment — no surprises, no scope fog.
Document audit & chunking strategy
We analyze your corpus — file types, structure, average doc length, and domain vocabulary — then design a chunking approach (semantic, recursive, or hybrid) that preserves context.
Embedding model selection & indexing
We benchmark multiple embedding models (OpenAI, Cohere, open-source) against your data and build a vector index optimized for your query patterns.
Retrieval pipeline & re-ranking
We wire up the full retrieval chain: query preprocessing, vector search, optional keyword fallback, cross-encoder re-ranking, and context window assembly.
Response generation & citation layer
We configure the generation model to synthesize answers from retrieved chunks, attach inline source citations, and apply confidence filtering.
Evaluation harness & production deploy
We create a golden eval set, run accuracy and latency benchmarks, then deploy with monitoring for retrieval quality, response grounding, and cost.
Problems we solve.
Your chatbot confidently makes up answers
We implement retrieval-first architecture with source-grounded responses. Every answer is traced back to a specific document chunk, and confidence thresholds prevent the model from guessing when evidence is weak.
Search results return irrelevant noise
We tune chunking strategies (semantic vs. fixed-size vs. hierarchical), experiment with embedding models, and apply re-ranking so the top results are genuinely relevant — not just keyword-adjacent.
Document updates don't appear in answers
We build incremental ingestion pipelines that detect changes, re-embed updated documents, and invalidate stale vectors — so the assistant always reflects your latest content.
You can't measure if it's actually accurate
We create evaluation datasets with known question-answer pairs from your domain and run automated accuracy benchmarks — so improvement is data-driven, not subjective.
Technical depth.
Hybrid search (vector + keyword)
Combines dense vector retrieval with sparse keyword matching (BM25) for queries that need both semantic understanding and exact-match precision.
Hierarchical chunking
Documents are split into parent-child chunks: large context windows for the model, small granular chunks for precise retrieval — giving you the best of both worlds.
Cross-encoder re-ranking
After initial vector retrieval, a cross-encoder model re-scores results by analyzing query-document pairs together — dramatically improving top-5 precision.
Incremental ingestion pipelines
File watchers and webhook-triggered pipelines detect document changes, re-embed only what's new, and handle deletions — keeping the index fresh without full re-indexing.
Common questions.
Q.What file types can you ingest?
PDFs, Word docs, HTML, Markdown, Confluence, Notion exports, Google Docs, plain text, and structured data (CSV/JSON). We also handle scanned documents via OCR preprocessing.
Q.How large can the document corpus be?
We've built RAG systems over 100K+ documents. The architecture scales — we use sharded indexes, batched ingestion, and incremental updates for large and growing corpora.
Q.Can users get answers with source links?
Yes. Every response includes inline citations linking back to the specific document and section the answer was derived from. Users can verify and read further.
Q.What vector database do you recommend?
It depends on scale and budget. pgvector is ideal for teams already on PostgreSQL. Pinecone for managed simplicity. Chroma or Qdrant for self-hosted control. We benchmark and recommend.
What we've built.
What our partners say.
“Our staff spent hours searching files, and our early AI bot just hallucinated answers. Lesscode rebuilt our RAG pipeline with precision embedding and citations. Accuracy went to 99%, and data leaks are zero. Stellar work.”
Building something ambitious, or fixing something that's gone sideways?
Tell us where you are and where you're trying to get to. We'll tell you honestly whether — and how — we can help.