AI in product and site
AI in the product means embedding language models into a site, a storefront or an internal system: search that understands questions rather than keywords, an assistant that answers from your own documents, or content generated from structured data. The pattern underneath is usually RAG, where the model retrieves your sources before it answers, and that retrieval step is what keeps answers tied to reality.
How the flow runs
The question
The customer writes in their words, not your keywords.
The retrieval
Vector and keyword search pull out your passages.
The evidence
Only the retrieved passages are passed to the model.
The answer
The model composes it and says where it came from.
The log
Question, evidence and answer are kept so deviations surface.
Search that understands the question
Keyword search matches words. Vector search matches meaning, so a customer typing "what do I do if my parcel never arrived" lands on the delivery terms even when those exact words are absent. For a deep catalogue it is often the single largest conversion gain available.
Assistants that stay on source
An assistant answering freely will invent things. An assistant restricted to retrieved documents, citing where each answer came from, can be trusted. The difference is in how the system is built, not in which model is used.
Cost and control
Model calls are billed per token and costs run away quickly. Flows are built with caching, smaller models for the simple steps, and per-user ceilings, so spend is predictable before traffic grows rather than after.
In this area
In this area
RAG explained: making AI answer from your own documents
How retrieval augmented generation works in practice, and why retrieval decides the quality rather than the model.
Before you build an AI assistant: seven questions to answer
The questions that decide whether the project becomes useful or becomes a demo nobody opens twice.
How to measure whether AI search actually got better
A method for evaluating search quality with numbers instead of opinion, and why hybrid search almost always wins.
Other services
- AI automationRepetitive processes that run themselves, with an audit trail and a human still in the decision.
- Digital developmentSites, storefronts and integrations built to load fast and stay maintainable.
- AI visibilityGetting cited when the answer is written by a language model instead of listed by a search engine.
