AI · RAG
Retrieval is the ceiling on RAG quality.
Domain-specific pipelines for parsing, chunking, hybrid retrieval, evaluation, and guardrails — engineered for production accuracy, not demo accuracy.
Overview
RAG works when retrieval works. We build the measurement loop first — so chunking, embeddings, and reranking can be tuned against numbers instead of vibes.
What it is
Grounding answers in your own knowledge.
Retrieval-Augmented Generation pairs an LLM with a search system over your documents. Instead of relying on whatever the base model memorized, the application retrieves relevant passages at query time and asks the model to answer using those passages — with citations.
The hard part is not the LLM. It is the parsing, chunking, embedding, retrieval, and reranking that decide which passages the model ever sees. Get those right and the model has a chance. Get them wrong and no prompt can save you.
Workflow
How a RAG pipeline runs.
- Ingest: source documents are parsed, chunked, embedded, and written to the vector index.
- Query: a user query is embedded and used for hybrid retrieval (BM25 plus dense) against the shared index.
- Retrieved passages are reranked, assembled into context, and passed to the LLM.
- The LLM produces a response.
- Evaluation feedback flows from the response back into reranker tuning, closing the loop.
Deliverables
What you walk away with.
- Document parser and chunking strategy tuned to your corpus structure and query patterns.
- Hybrid retrieval (BM25 plus dense) with reranker, evaluated against a labeled gold set.
- Evaluation harness: retrieval recall, answer faithfulness, latency, and cost dashboards.
- Hallucination guardrails: citation enforcement, refusal handling, and confidence thresholds.
- Operational runbook for index refresh, schema migrations, and embedding-model upgrades.
Pitfalls
How we don't do it.
- Shipping a single embedding model with no reranker and calling it "retrieval".
- Chunking by fixed token count with no respect for document structure or semantic boundaries.
- Evaluating only end-to-end answers — masking which stage of the pipeline is actually broken.
- Indexing once and never refreshing, so the system silently rots as the corpus changes.
Engagement
How we work with you.
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01
Discover
Corpus shape, query intents, and the answers users actually need.
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02
Architect
Chunking, embeddings, retrieval, and the eval harness — designed together.
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03
Build
Ingest pipeline, query path, citations, and guardrails wired into your app.
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04
Tune
Closed-loop eval. Iterate on retrieval and reranking against measured gaps.
Want answers your users can trust?
Bring us your corpus and your hardest questions. We'll build the eval harness first, then the pipeline that climbs it.
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