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AI Search

RAG & Enterprise Search

Production retrieval pipelines that actually retrieve. Chunking, embeddings, hybrid lexical + vector search, reranking, and the eval rigs required to keep accuracy from drifting once the knowledge base grows.

Retrieval Pipeline

Eval-driven

01

Source

Docs, tickets, code, wikis

02

Chunk

Semantic + structural

03

Embed

Domain-tuned vectors

04

Index

Vector + BM25 hybrid

05

Retrieve

Multi-pass, filtered

06

Rerank

Cross-encoder + rules

Eval gate

Recall · MRR · groundedness

Production exit

Retrieval that survives the next 10× of content.

Timeline
4-8 weeks
Engagement
Principal-led
Output
Production RAG system
Time to Value
2-4 months

Who It's For

  • Teams with large document/knowledge bases
  • Companies with failed RAG POCs
  • Enterprises needing accurate internal search
  • Products adding AI-powered Q&A

Our Approach

1

Data Audit

Weeks 1-2

Analyze your document corpus, identify data quality issues, and design chunking and embedding strategies tailored to your content types.

2

Pipeline Build

Weeks 3-5

Build the ingestion pipeline, configure hybrid search with semantic and keyword retrieval, and establish the evaluation framework.

3

Optimization

Weeks 6-7

Tune retrieval quality using eval results, optimize for latency and cost, and implement quality monitoring dashboards.

4

Production Launch

Week 8

Deploy to production with monitoring, alerting, and runbooks. Train your team on operations and continuous improvement workflows.

Common Questions

What if our previous RAG POC didn't meet quality expectations?

Most RAG failures stem from poor chunking strategies, missing evaluation frameworks, or naive retrieval approaches. We audit what went wrong and rebuild with production-grade patterns including hybrid search and systematic evaluation.

How do you measure RAG quality?

We build automated evaluation suites that test retrieval relevance, answer accuracy, and faithfulness. These run as regression tests so quality is continuously monitored, not just checked once.

Can this integrate with our existing knowledge base tools?

Yes. We design pipelines that connect to your existing document stores, wikis, and content management systems. The RAG layer sits on top of your current infrastructure.

What You Get

  • Production RAG pipeline
  • Evaluation framework with regression tests
  • Chunking and embedding strategy
  • Hybrid search (semantic + keyword)
  • Quality monitoring dashboard
  • Documentation and runbook

RAG systems that need to actually retrieve.

A 2-week Diagnostic audits your retrieval pipeline — chunking, embeddings, reranking, evals — and returns a 30-day plan to fix the failure modes that are dropping accuracy.