RAG Systems
AI Engineering

RAG Systems

Retrieval that finds the right chunk — not just a similar-sounding one.

Most RAG systems fail on retrieval, not generation. Dense embeddings alone miss keyword matches. BM25 alone misses semantic context. Chunking strategies that look reasonable on toy datasets collapse on real documents. We build hybrid retrieval stacks with re-ranking, metadata filtering, and citation attribution — tuned on your actual data, benchmarked against your actual queries.

50+
RAG systems shipped
4.1×
Recall improvement
3–8 wk
Typical timeline
68%
Avg cost reduction
Client outcome
4.1× average recall improvement over baseline vector search across 50+ RAG deployments.

Measured across similar ai engineering engagements we've shipped.

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StackPineconeWeaviatepgvectorOpenAI EmbeddingsCohere RerankLangChainLlamaIndexRedis

What we build

01
Hybrid retrieval

Dense vector search combined with BM25 keyword retrieval, merged with Reciprocal Rank Fusion. Semantic context plus exact keyword matching — no more choosing between them.

02
Re-ranking

A cross-encoder re-ranker (Cohere, BGE, or custom) scores the top-k candidates from first-stage retrieval. Recall improves 2–4× over vector search alone on real enterprise queries.

03
Adaptive chunking strategies

Sentence-window, hierarchical, and semantic chunking — selected and tuned per document type. A strategy that works for contracts fails on transcripts; we benchmark before committing.

04
Metadata filtering & faceted search

Structured metadata filters applied pre-retrieval to scope results by date, source, department, or access level — shrinking the search space and eliminating irrelevant candidates before scoring.

05
Citation attribution

Every generated response includes grounded citations to the exact source chunks — with document title, page reference, and confidence score. No hallucinated sources.

06
Eval harness & quality monitoring

RAGAS-based evaluation on precision, recall, faithfulness, and answer relevance — run automatically on every pipeline change, with dashboards tracking quality over time.

How we Deliver

Week 1
Data audit & query analysis
We audit your document corpus, analyze a sample of real user queries, and identify the retrieval failure modes before designing the pipeline. No assumptions about what chunking strategy will work.
Week 2–3
Prototype & benchmark
Two to three retrieval configurations benchmarked on your real data and real queries. You see precision, recall, and latency numbers before we commit to an architecture.
Week 3–6
Production build & indexing
Full pipeline built — ingestion, chunking, embedding, indexing, and the retrieval + re-rank + generation chain — with cost instrumentation from day one.
Week 6+
Monitor & iterate
Quality dashboards live at launch. We review retrieval metrics monthly and tune chunking, re-ranker thresholds, and filter logic as your corpus and query patterns evolve.
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

Which vector database should we use?
Pinecone for managed simplicity at scale, Weaviate for hybrid search and multi-tenancy, pgvector when you want retrieval inside your existing Postgres stack. We benchmark all viable options on your data before recommending.
How do you evaluate RAG quality?
RAGAS framework: context precision, context recall, faithfulness (did the answer stick to the retrieved context?), and answer relevance. We run automated evals on a golden Q&A set before every production deploy.
What document types do you support?
PDF, DOCX, HTML, Markdown, structured JSON, database records, and code. Each format gets a parser and chunking strategy tuned to its structure — not a generic text splitter.
How do you handle access control — users shouldn't see each other's documents?
Metadata-based access filtering applied at query time — each user's retrieval is scoped to documents they're authorized to see. We support row-level security patterns in both Pinecone and pgvector.

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