LLM and RAG integration
YORD connects large language models to your product, your data and your systems. The team builds retrieval-augmented generation (RAG) for answers grounded in your documents, and AI chatbots with clear limits. Each integration is tested against your own questions before launch. One team of software and AI engineers builds the integration and the product around it.
What we build
- A scope of the questions the system must answer and the sources it may use
- A retrieval pipeline: document import, splitting, search and source references
- A chatbot interface for your website, app or internal tools, where in scope
- Model selection, tested on your own example questions
- Handoff to a person when the system cannot answer with confidence
- An evaluation set and checks for wrong or unsupported answers
- Cost and response time checks before launch
Who this is for
- Your support team answers the same questions from long documents
- Your staff cannot find answers in internal wikis, files or tickets
- You want a chatbot on your site that answers from your own content
- An existing AI feature gives answers that are wrong or have no source
How it runs
Idea
We define the questions, the sources and the answers that count as correct.
Design
We design the retrieval approach, the chatbot flow and the rules for handing a question to a person.
Build
We build the pipeline and the interface, then test each answer against your example questions and the sources it cites.
Launch
We open the system to a small group, then check the answers and the sources they cite.
Grow
We update the sources and tune retrieval based on the questions users ask.
Questions about this service
What is RAG?
Retrieval-augmented generation (RAG) searches your documents for the text that answers a question, then gives that text to a language model with the question. The answer is grounded in your content, and the system can show the source it used.
When should we use RAG instead of fine-tuning?
RAG suits answers that depend on documents that change. Fine-tuning changes how a model writes and responds, so it is a poor place to store facts that change often. The guide at /insights/rag-fine-tuning-or-prompting/ compares the options in more detail. Decide each part on test results, not on the name of the technique.
Can the chatbot show its sources?
Yes. A RAG system can list the documents or passages behind each answer. The scope states which sources the chatbot may use and how the sources appear to users. When the source is not in your documents, the chatbot says so instead of guessing.
How do you protect our data?
Before any data reaches a model, the team agrees which data the model may see, where it is stored and which model provider processes it. Your data goes to a provider only after you have reviewed that provider's terms. Access rules limit each user to the documents they may read.
Can we change the model later?
Yes. The model layer is kept apart from the rest of the product code, so a new model can be run against the same example questions before it replaces the current one. The scope names the model in use at launch.
How do you reduce wrong or unsupported answers?
No model is free of errors, so YORD builds checks around it. The evaluation set includes questions the sources cannot answer, and the chatbot must say so instead of guessing. Answers are checked against the passages they cite, and failures are reviewed before each release.