Retrieval-Augmented Generation (RAG) Development
RAG systems that actually retrieve the right context — real chunking and retrieval strategy, not just dumping documents into a vector database and hoping.
Retrieval-Augmented Generation (RAG) Development
- req/s, zero deadlocks
- 15Kreq/s, zero deadlocks
- double-charges in production
- 0double-charges in production
- production systems shipped
- 10+production systems shipped
- years building for clients
- 7+years building for clients
Signs you need this now.
RAG is easy to prototype and hard to get genuinely accurate. Poor chunking strategy, weak retrieval relevance, and no evaluation process are why so many RAG systems work fine in a demo and then quietly give wrong or incomplete answers once real, varied queries hit them.
The RAG system retrieves irrelevant or incomplete context
Naive document chunking (splitting by fixed character count regardless of content structure) breaks context in ways that hurt retrieval quality, so the model answers based on incomplete information.
No way to know if the RAG system is actually accurate
Without a real evaluation process, quality issues only surface when a user notices a wrong answer, which is the most expensive way to find out.
The system struggles with questions that need information from multiple documents
Basic single-pass retrieval often fails on questions that genuinely require synthesizing across several sources, which is a common real-world query pattern, not an edge case.
What you get.
A chunking strategy matched to your actual content
Structure-aware chunking (respecting document sections, tables, code blocks) instead of naive fixed-size splitting.
Retrieval tuned for relevance, not just similarity
Hybrid search (semantic plus keyword) and re-ranking where it measurably improves result quality, not added by default.
An evaluation process to actually measure accuracy
A real test set of representative questions with known-correct answers, so retrieval quality is measured, not assumed.
Multi-document synthesis where the use case needs it
Retrieval and prompting designed to handle questions that require pulling from more than one source.
Source citation in responses
Answers that reference where the information came from, so users (and you) can verify accuracy rather than trusting blindly.
Four steps, no mystery.
Quick scoping call
A short call (or async over WhatsApp) to understand what you're working with and what "done" actually looks like for you.
Fixed scope, no surprises
A clear written plan of what's included and how long it takes, before any work starts.
The actual work
Progress you can see, not a black box. You get updates as milestones land, not just a status report at the end.
Handover
Everything documented and handed over cleanly, with a walkthrough so your team isn't stuck waiting on me for routine changes.
Frequently asked.
Whichever fits your scale and existing infrastructure — Pinecone, pgvector, Weaviate, or others, recommended based on your actual requirements rather than a default preference.
Tell me what you're dealing with.
Send a message and get a real reply within 24 hours, not an automated sequence.
Or WhatsApp directly, same link as above