---
title: "RAG Development Services | Retrieval-Augmented Generation | Agility"
url: https://agilitytech.ai/rag-development-services
description: "RAG development for answers grounded in your own documents: ingestion, retrieval tuning, citations and evaluation, on cloud or on-premises."
publisher: Agility (agilitytech.ai)
---

Service · RAG

# RAG development services

Retrieval-augmented generation lets a language model answer from your documents instead of from memory. The model part is the easy bit.

- Senior engineers on every project
- Evaluation before launch, not after
- You own the code and the models

48-hour turnaround · free · no obligation

### Talk to us about RAG

Tell us what you want to build. A senior engineer sends back a scope and a fixed price within two business days, no obligation.

In short

## The quick answer

Most of the work is getting the right passage in front of it every time, and proving that it does. That is where we spend our effort.

What we build

## What we build with RAG

### Ingestion that respects your documents

Parsing PDFs, tables, scanned files and wikis so headings, tables and page numbers survive, because bad parsing causes most bad answers.

### Retrieval tuning

Hybrid keyword and vector search, reranking and metadata filters, tested against real questions from your team.

### Answers with citations

Every answer links to the passage it came from, so people can check it in seconds.

### Private deployment

Open models and self-hosted vector databases in your cloud or on your own servers when documents cannot leave.

Fit

## Is RAG the right choice?

### When it is a good fit

- People spend time hunting through manuals, policies or past tickets.
- Answers need to be traceable to a source.
- Content changes often, so fine-tuning would go stale.

### When we would suggest something else

- The knowledge fits in one prompt. Just include it.
- You need the model to learn a new style or format rather than look things up. That is a fine-tuning problem.

Process

## How a project usually runs

- 01 ### Collect real questions We ask the people who will use it for the questions they actually ask, along with the right answers.
- 02 ### Fix ingestion first We inspect how documents are parsed before touching prompts.
- 03 ### Tune retrieval against the test set We measure whether the right passage is retrieved, separately from whether the answer is good.
- 04 ### Launch and keep measuring Feedback buttons, logging and a regular review of failed questions.

Pitfalls

## What usually goes wrong

We have seen these enough times to plan around them from the start.

- Judging quality on a handful of demo questions.
- Tables and scanned pages turned into garbage text during ingestion.
- No plan for keeping the index in sync when documents change.

Next step

Want to see similar work? Browse our [case studies](https://agilitytech.ai/case-studies) or [tell us what you are working on](https://agilitytech.ai/contact).

FAQ

## RAG: common questions

[Ask us something else](https://agilitytech.ai/contact)

**Why does our RAG chatbot give wrong answers?+**

Usually because the right passage was never retrieved, often due to poor parsing or chunking, not because the model is weak. We test retrieval and generation separately to find out which one is failing.

**RAG or fine-tuning?+**

RAG when the answer depends on facts in documents that change. Fine-tuning when you need a consistent style, format or narrow skill. Many systems use RAG alone, and some use both.

**Can RAG run on-premises?+**

Yes. We deploy open models and vector databases on your own hardware or private cloud when data cannot leave.

**How do you measure quality?+**

With a test set of real questions and correct answers, scored on every change: was the right passage retrieved, and was the answer correct and supported by it?

Keep reading

## Related pricing and decision guides

[Hire RAG developers](https://agilitytech.ai/hire-rag-developers)[LLM fine-tuning services](https://agilitytech.ai/llm-fine-tuning-services)[Hire AI developers](https://agilitytech.ai/hire-ai-developers)[AI agent development](https://agilitytech.ai/ai-agent-development)[LLM development services](https://agilitytech.ai/llm-development-services)

## Get your exact number with a free 48-hour audit

Indicative ranges only get you so far. Tell us the specifics and get a scope and a fixed price in two business days.

Get my fixed estimate