AI Practice›RAG Architecture

Your AI answers from your data, not from memory.

General models hallucinate when asked about your proprietary knowledge. We build retrieval pipelines that ground every response in your actual documents — with the precision, recall, and freshness production requires.

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rag pipeline · production

Index: 2.4M documents

last updated 4m ago · 98.2% freshness

Hybrid search active

BM25 + semantic · rerank p@5 0.91

Faithfulness score

0.94 avg · answer relevance 0.89

Incremental updates

12k docs/min throughput · zero downtime

What we deliver

The full retrieval stack, end to end.

Document ingestion

Connectors for S3, Google Drive, Confluence, SharePoint, SQL databases, and custom APIs — normalized and versioned.

LlamaIndex connectors · custom ETL

Embedding pipeline

Semantic chunking, sentence-level normalization, and batch embedding with the model that fits your retrieval pattern.

text-embedding-3 · BGE · Cohere

Vector storage

Right-sized vector DB selection, schema design, filtering metadata, and multi-tenant namespace architecture.

Pinecone · Weaviate · pgvector · Qdrant

Hybrid search

BM25 keyword + dense vector retrieval with cross-encoder reranking — higher precision than either alone.

Cohere Rerank · FlashRank · custom

Retrieval evaluation

Precision@k, recall@k, faithfulness, and answer relevance benchmarks built before you optimize anything.

RAGAS · TruLens · custom eval harness

Index refresh

Real-time event-driven and scheduled batch pipelines that keep your index current without downtime or full rebuilds.

Kafka · Airflow · Change Data Capture

Our approach

Measure before you optimize.

Corpus audit

Map your data sources, assess quality and structure, identify gaps that will hurt retrieval before they do.

Pipeline design

Choose chunking strategy, embedding model, vector store, and query flow — all validated against your content type.

Eval baseline

Build retrieval benchmarks and golden test sets before optimizing. Know your precision@5 before you ship.

Production deploy

Monitoring for freshness, drift, and quality degradation — with reindex pipelines and alert thresholds in place from day one.

Platforms & tools we use

PineconeWeaviatepgvectorQdrantLlamaIndexLangChainCohere RerankRAGASOpenAI Embeddings

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Ready to ground your AI in your own data?

No SDR, no discovery-call gauntlet. A senior AI practitioner personally reviews every submission and replies within one business day.

Document ingestion & connector build

Embedding pipeline & vector store design

Hybrid search & cross-encoder reranking

Retrieval evaluation & freshness monitoring

Direct contact

Use the contact form

(202) 903-9000