Custom RAG & AI Integration
Retrieval-augmented generation (RAG) connects a language model to your own documents, tickets and product data, so answers come from your information rather than the model's guesses. We design these systems and build them into real applications.
What this covers
Support and knowledge assistants
Assistants that answer from your documentation, help-centre articles and ticket history, and point to where the answer came from.
Questions over your own data
Search and question-answering over contracts, manuals, catalogs or internal wikis that are too large to read by hand.
AI features inside existing products
Chat, summarisation or classification added to an application you already run, using the Gemini API and a vector store such as pgvector.
Testing before launch
We check answers against a set of real questions before release, so you can see where the system is strong and where it is not.
When this is the right fit
- A general chatbot keeps inventing answers that are not in your documentation.
- Your team spends hours answering the same questions from the same documents.
- You want AI in your product but need its answers to stay tied to your own data.
How we approach it
Start with the data
We look at what your documents contain, how often they change and who is allowed to see what, before choosing any tools.
Get retrieval right before generation
If the right passages are not retrieved, no model can answer well. We tune and test retrieval first.
Let the system say it does not know
The model is instructed to answer only from retrieved sources and to say so when the answer is not there, which reduces confident but wrong answers.
Technologies we use for this
- Gemini API
- pgvector
- PostgreSQL
- LangChain
- Next.js
- TypeScript
Frequently asked questions
- What is RAG?
- Retrieval-augmented generation looks up the most relevant passages from your own content at question time and gives them to the language model along with the question. The answer is then based on those passages instead of only on what the model learned in training.
- Is RAG better than fine-tuning a model?
- For answers that must reflect company documents that change, RAG is usually the simpler and easier-to-maintain starting point, because you update the content rather than retrain a model. Fine-tuning solves a different problem, such as consistent style or format.
- Will it still make things up?
- No system is perfectly immune. We reduce the risk by restricting answers to retrieved sources, instructing the model to say when it does not know, and testing with real questions before launch.
- Where does our data go?
- Prompts sent to the Gemini API are processed by Google under Google's terms. We discuss how sensitive your data is before anything is built, and we scope what is and is not sent.
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