RAG Applications
A retrieval-augmented generation pipeline built as a component — retrieving the exact passages from your content before generating a reply, and delivered as an API you embed inside your own product or internal tool.
What a RAG Application Is
Retrieval-augmented generation means retrieving the exact relevant passages from your content first, then generating a reply from those passages rather than from general training alone. This cuts down invented answers and shows where each reply came from. Built here as a component, not a finished front end — meant to be embedded inside something you already run.
Where This Is Used
Adding a Question-Answer Feature to Your Own Product
Give your own software an "ask a question" capability grounded in its own content.
Powering an Answer Feature Inside Your SaaS or App
A retrieval layer your product calls when a user needs an answer sourced from your data.
Grounding an Internal Tool
An internal tool that reads and answers from your own datasets rather than general knowledge.
Combining Multiple Content Types
PDFs, database records and web pages brought together into one answer source.
What Goes Into Building One
Content Ingestion and Chunking
Your content is broken into retrievable pieces sized for accurate matching.
Retrieval Index Build
An index built so the right passages surface for a given question.
Answer Generation With Citation
Replies generated from retrieved passages, with the source attached.
API for Your Application to Call
A defined interface your own application or tool calls to get an answer.
How Accuracy Is Checked
- A test set of real questions matched against the correct source passages.
- Retrieval accuracy measured separately from the quality of the generated answer.
- Re-tested whenever the underlying content is updated.
When You Just Need a Ready Assistant
If you need a finished assistant on your website or for your staff, order that directly instead. This service is for teams who want the retrieval pipeline built as a component inside something they are already building.
Frequently asked questions
How is this different from a chatbot?
A chatbot is a finished front end for customers or staff to talk to. A RAG application is the retrieval engine that can sit behind a chatbot, your own app, or an API call.
What drives the cost of a RAG pipeline?
The volume and variety of content to ingest, and how many content types need combining into one retrieval source.
What drives a RAG build's timeline?
How ready your content is to ingest, and how well-defined the interface point in your application is.
Can this connect to whatever we're already building?
Yes, it is delivered as an API, built to be called from any application or stack you already have.
Who owns the retrieval index and code?
You do — the ingestion pipeline, index and API are yours to run, move or extend.
What do we need from your engineering team?
The content set to retrieve from, and a defined point in your application where the API call will be made.
Tell us what you need.
Send a short brief and one of our engineers will come back to you — usually the same day.
- No obligation
- We reply the same working day
- Your details stay private