Retrieval-Augmented Generation
End-to-end RAG pipelines from ingestion to retrieval to answer generation, built for accuracy and cost control.
Ingest, embed, retrieve, generate — reliably
We build RAG systems that survive contact with real data: messy PDFs, changing sources, permissioned content, and long documents. Chunking strategy, hybrid retrieval, reranking, caching, and eval-driven iteration — we tune each layer against your data.
Outcomes
Our approach.
Corpus audit
We profile your data: doc types, sizes, churn rate, permissions, language. The shape of the corpus dictates the shape of the pipeline.
Chunk & embed
We pick chunking per content type (fixed, semantic, layout-aware), choose embeddings, and version both so you can A/B upgrades safely.
Hybrid retrieval + rerank
BM25 and vector search combined, then a reranker on top. We measure hit rate @ k against a gold set — if it's not measured, it's not tuned.
Generation, cache, iterate
Prompts versioned, generations cached where safe, faithfulness scored continuously. Every change goes through eval gates.
What you get.
Production-shaped, from day one.
// Hybrid retrieval with rerank + permission filter
const chunks = await retriever.search({
query,
topK: 40,
filters: { tenant: user.tenant, acl: user.groups },
hybrid: { semantic: 0.7, bm25: 0.3 },
})
const reranked = await rerank(query, chunks, { topK: 8 })
const answer = await generate({
query,
context: reranked,
citeSources: true,
})
// -> { text, citations, hit_rate, faithfulness }A proven shape for this solution.
We adapt it to your cloud, data, and compliance requirements. Nothing here is boilerplate — every layer is justified by the numbers.
Where this shows up.
- Policy and compliance Q&A over regulated documents
- Engineering knowledge search over code, tickets, and docs
- Healthcare data lookup with permission-scoped retrieval
- Sales enablement over contracts, decks, and call notes
What we use.
We’re not religious about tools. We pick what fits your constraints and team.
Shipped examples.
Healthcare patient data mapping & health information chat
Mapped and normalized patient data to power a grounded chat experience where patients can ask questions about their own health information — safely.
What teams usually ask.
Which vector database should we use?
+
If you already run Postgres, start with pgvector. If you need massive scale or hosted ops, Pinecone. If you want open-source control, Weaviate or Qdrant. We decide on the numbers, not the brand.
How do you handle documents that change over time?
+
Ingestion pipelines track source versions and re-embed only what changed. We keep an index of doc versions so old answers can still cite the exact version they saw.
How is retrieval quality measured?
+
Labeled gold sets for hit rate @ k, adversarial sets for edge cases, and production samples scored with an LLM-as-judge faithfulness metric. All versioned in the repo.
Related solutions.
Conversational AI & Chat Lookup
Production-grade chat systems that answer from your sources with citations, guardrails, and session memory.
Document Intelligence
Pipelines that turn unstructured documents into structured data your systems can use.
Cloud AI Infrastructure
We stand up the platform layer so your AI systems are secure, observable, scalable, and cost-governed from day one.
Ready to accelerate your tech growth?
Schedule your free consultation today and let's discuss how we can help your business scale efficiently.
