Accelerator

RAG Studio — production retrieval in 10 days

A production-grade retrieval system with hybrid search, re-ranking, and citation attribution.

Get a scoping call

Naive RAG — chunk-embed-retrieve — works in demos and fails in production. Documents get split badly, retrieval misses semantically related content, hallucinations slip through. RAG Studio delivers a retrieval system tuned on your actual data with hybrid search, metadata filtering, re-ranking, and citation provenance tracked end to end.

10
days to production
62%
avg. accuracy lift
5
core deliverables
Client outcome
Average retrieval accuracy improvement over naive baseline: 62%.

Measured across similar accelerator engagements we've shipped.

Get a proposal
StackPinecone / pgvectorOpenAI EmbeddingsCohere RerankLangChainPythonFastAPI

What we build

01
Intelligent chunking

Semantic and structural chunking strategies matched to your document types — not a one-size-fits-all splitter.

02
Hybrid search

BM25 keyword search + dense vector retrieval fused with Reciprocal Rank Fusion for consistently better recall.

03
Re-ranking

Cohere or cross-encoder re-ranker applied on top of retrieval to push the most relevant chunks to the front.

04
Metadata filtering

Date ranges, document types, authors, and custom metadata fields — filtering happens before and after retrieval.

05
Citation attribution

Every generated answer includes exact source references with document name, page, and chunk offset.

How we Deliver

Day 1–2
Data audit & pipeline design
We inventory your documents, assess quality, and design the ingestion pipeline, chunking strategy, and schema.
Day 3–6
Ingestion & indexing
Build the ingestion pipeline, chunk and embed all documents, and stand up the vector store with metadata schema.
Day 7–9
Retrieval tuning
Hybrid search integration, re-ranker calibration, and evaluation against your ground-truth Q&A pairs.
Day 10
API & handover
FastAPI endpoint, integration tests, a retrieval quality dashboard, and full documentation of the pipeline.

Best practices for RAG Studio

  • Build your ground-truth eval set before touching the pipeline

    Without it, you're tuning in the dark. A small set of 50–100 Q&A pairs from real users is worth more than any benchmark.

  • Chunk at semantic boundaries, not character count

    Fixed-size chunking splits sentences and collapses context in ways that consistently degrade retrieval quality.

  • Store rich metadata at ingestion time

    Retrofitting metadata onto already-indexed chunks requires a full re-index. Get the schema right before you ingest a single document.

  • Never skip re-ranking in production

    Hybrid fusion alone leaves significant accuracy on the table. A cross-encoder re-ranker consistently recovers the gap at low latency cost.

Evolve Edge team

From Evolve Edge

“We don't ship AI without an eval harness. Not because clients ask — because it's the only way to know the system is actually working in production.”

FAQ

What document types do you support?
PDF, Word, HTML, Markdown, CSV, PowerPoint, and plain text. Custom parsers for structured formats like XML or JSON schemas are scoped on request.
Which vector store do you recommend?
For most use cases, pgvector if you already run Postgres — it removes operational complexity. Pinecone for very large indexes (10M+ chunks) or sub-10ms SLAs.
How do you evaluate retrieval quality?
We build a ground-truth test set from your domain, then measure recall@k, MRR, and answer faithfulness with RAGAs. You get the eval harness permanently.
Can you connect it to our existing LLM application?
Yes. The retrieval system is exposed as a typed API. We handle the integration with your existing prompt chain or chat UI.

Have Questions? Let's Talk.

Free 30 minute call with a senior engineer, not a salesperson. We have got the answers to your questions.

Book strategy call
contact@evolveedge.co+1 (512) 678-3820