AI that knows your business, because it can look it up.
A general model does not know your product, your documentation, your customers, or what happened in your system this morning. Retrieval is what closes that gap: giving the model the right context at the moment it answers, rather than hoping it absorbed something close enough during training.
Most retrieval systems that disappoint are not failing at generation. They are failing at retrieval, and nobody is measuring it. So we measure that first: what came back, whether it was the right thing, and how often. Chunking, embeddings, reranking, and a proper evaluation set before any of it goes near a user.
Then the part people actually see. An interface that shows its sources, latency and cost budgets that are decided rather than discovered, and permissions honoured where the retrieval happens instead of filtered after the fact.
What you get
Vector DB setup, retrieval pipeline, custom UI, eval framework, latency and cost optimization. Built to scale.
How it runs
- 01
Map the sources
What questions the system has to answer, what it must never answer, and where the truth actually lives. Sources, permissions, and freshness requirements written down before anything is indexed.
- 02
Build the eval set
A set of real questions with known good answers, built before the pipeline. This is the only thing that later tells you whether a change helped.
- 03
Build retrieval
Chunking, embeddings, vector store, and reranking, tuned against the eval set and measured on retrieval quality alone before generation is added.
- 04
Build the surface
The interface people use: answers with visible sources, honest uncertainty, and a way to report a bad one that reaches the eval set.
- 05
Optimise and monitor
Latency and cost profiled and tuned, monitoring on retrieval quality in production, and a documented way to add the next source.
This is for you if
- A SaaS product that would be genuinely better with an AI layer over its own data.
- A large internal knowledge base that people cannot search effectively.
- A support team answering the same questions from documents that already contain the answers.
This is not for you if
- The knowledge base is out of date. Retrieval will surface that faster and more publicly than anything else you could buy.
- You want a chatbot on the marketing site. That is a smaller and cheaper job.
- Nobody can say what a correct answer looks like.
Work that proves it
Questions
- What is RAG?
- Retrieval-augmented generation. The model is given the relevant parts of your own documents, data, or product state at the moment it answers, instead of being expected to have memorised them. It is what turns a general model into one that knows your business.
- How is an AI integration priced?
- By source count and how much correctness matters. One source with straightforward retrieval, multi-source retrieval with a custom interface and an evaluation suite, or enterprise retrieval with a managed vector database, monitoring and an SLA. Model and vector storage costs are yours and are estimated before the build.
- Why does our current RAG give bad answers?
- Usually a retrieval problem rather than a model problem. Wrong chunking, no reranking, no evaluation, and nobody looking at what was actually retrieved before the model saw it. We start by measuring retrieval on its own, because a perfect model on the wrong three paragraphs is still wrong.
- What can it retrieve from?
- Documents, wikis, ticket histories, product databases, and live application state. If it has an API or a schema, it can be a source. The work is in deciding what should be retrievable and what should not.
- Can it respect our existing permissions?
- Yes, and it is designed in from the start: source scoping, per-user permissions honoured at retrieval time rather than filtered afterwards, and cost and latency budgets set before the build.
A roadmap, not a pitch.
Skip the agency runaround. Thirty minutes with the people who would actually do the work. You leave with a clear next step, whether or not you hire us.