FIG 1.2 · RAG SYSTEM DEVELOPMENT
Retrieval-augmented pipelines grounded in your data.
Retrieval-augmented generation systems that connect LLMs to your documents, databases, and APIs. Grounded answers, real-time data, no hallucinations.
WHAT IT IS
A RAG system combines an LLM with a retrieval layer. When a user asks a question, we first search your data, retrieve the most relevant passages, and pass them to the model. The model grounds its answer in what it found.
This keeps answers current without retraining, prevents hallucination, and cites sources. It is how you turn a chatbot into a knowledge engine.
We build RAG pipelines for support bots, internal knowledge search, document Q&A tools, and agentic workflows.
WHEN TO USE IT
Knowledge base Q&A
Employees or customers need answers grounded in your documentation, policies, or product specs.
Dynamic data surfaces
Answers depend on data that changes daily — prices, inventory, tickets, schedules.
Source-citing support
Responses must link back to the exact source document or passage for audit trails.
Enterprise search
Full-text search is not enough; users need conversational retrieval across 100k+ documents.
OUR APPROACH
- 01
Ingestion pipeline
Chunk, clean, and embed source documents. Handle PDFs, HTML, databases, S3 buckets, Confluence, Notion.
- 02
Retrieval strategy
Hybrid lexical + semantic search, reranking, metadata filtering. Tuned to your data shape.
- 03
Answer synthesis
LLM prompt chain with citations, fallbacks, and guardrails. Streamed responses.
- 04
Evaluation and iteration
A continuous eval harness surfaces regressions. You own the dataset and the metrics.
TECH STACK
FAQ
About rag system development.
Start with a scoping call.
Thirty minutes. No pitch. We audit what you’re building, tell you what we’d do differently, and let you decide.