AI Software Development
Copilots, retrieval applications and software that can work with documents and data.
Search & Recommendations
AI Search & Recommendations
We help people find the right thing: by asking in their own words, and by seeing suggestions that are relevant to what they are already doing.
In plain terms
Finding, Rather Than Knowing the Exact Words
Ordinary search works when the user knows the label. It struggles when they describe the thing instead: a problem, a near-synonym, or a question that does not match the title of the page.
Semantic search is the practical answer to that. The system looks for meaning, not only for the same words, and returns the passages, products or records that are actually about the question. It is still search. The user can see results, open them, and decide. It is not a hidden ranking that cannot be inspected.
Recommendations are the other half. Once you know what someone is looking at, or what they have used before, you can suggest a next document, product or article. That suggestion has to be explainable enough to trust, and it has to respect what that person is allowed to see.
What We Put in the Product
Semantic search
Results based on meaning, so a question still finds the relevant item when the wording differs.
Natural-language questions
A search box that accepts a sentence, not only a keyword, and still returns items the user can open.
Knowledge search
Finding the right internal page, policy or document, with the matching passage visible.
Product and content discovery
Helping someone browse a catalogue or a library without knowing the exact name.
Personalised recommendations
Suggestions based on what this user, or users in a similar situation, have already found useful.
Relevance you can review
A way to see why something ranked, and to correct a result that should not have appeared.
Search inside an application
The box lives in the product, and the results respect that product's permissions.
Search and Suggestions Are Not the Same Feature
Search answers a question the user just asked. A recommendation appears because of context: the page they are on, the items they have used, or a gap in what they have seen. Mixing the two without saying which is which makes both harder to trust.
We keep them distinct in the interface. A result list shows what matched the question. A recommendation surface shows what might be useful next, and it can be ignored. Neither should leak an item the user is not allowed to open.
Behind the scenes, this often uses embeddings: a way of placing text or products so that similar things sit near each other, then retrieving the nearest ones. That is an implementation detail. The business question is whether the person found what they needed, and whether the suggestion was appropriate.
When the result should be a written answer rather than a list, the feature is closer to AI software or a conversational assistant. Search still supplies the sources those answers are allowed to use.
A Good Fit When
- People cannot find a document unless they remember its title
- A catalogue is large enough that keyword search returns the wrong neighbourhood of results
- Support or sales staff re-answer questions that are already written down
- A product should suggest a next item without hiding how that suggestion was chosen
- Different users must not see each other's content in the results
What It Connects To
Vector retrieval is used where meaning matters more than an exact word. It is not a reason to throw away filters, permissions or a result the user can open.
Content
Retrieval
Product
Application
Related Services
Search is often the source layer for a copilot or a conversational assistant.
Conversational AI & Voice Assistants
Chat and voice assistants with retrieval, escalation and a path to a person.
AI Integration & Automation
Practical AI capabilities and automation inside existing workflows.
Questions About Search and Recommendations
Is this the same as a chatbot?
No. Search returns items the user can open. A chatbot writes an answer. They can share a source, but they are different interfaces.
Will recommendations use personal data?
Only the context the product is already allowed to use, and only to suggest something that person may see. We do not build a hidden profile for its own sake.
Can we tell why a result appeared?
Yes. A result that cannot be explained or corrected is not a result we would ship as the primary way to find something.
Have a Software Project in Mind?
Whether you're building something new, modernising an existing application or need specialist development support, let's discuss what you need.