AI Search & Recommendations
Semantic search and recommendations that help someone find a relevant result.
AI Software
AI Software Development
We build software that uses AI as part of the product: copilots, assistants over your own documents, and applications that can ask a question of the data you already hold.
An Application, Not a Chat Window on Its Own
A useful AI product is usually an application with a model behind one or two of its tasks. Someone opens a screen they already understand, asks for help with a document, a record or a decision, and gets an answer that can be checked against the source.
That is a different job from dropping a general chatbot onto a website. The product has to know which documents or records it may use, who is allowed to see them, and what should happen with the answer. Retrieval, permissions and the surrounding interface are the work. The model is one component.
We build that as software: a copilot inside an application, a knowledge assistant over an agreed set of documents, or a database-aware feature that returns a structured result the rest of the system can store. The broader view of how we bring AI into existing systems is AI Integration & Automation. This page is the specialist service.
What This Service Covers
The shape depends on the product. Not every application needs all of these.
AI copilots
Assistance inside a product the user already works in, scoped to that product's data and tasks.
Retrieval applications
Software that finds relevant passages or records and uses them as the context for an answer.
Document and knowledge assistants
Interfaces for asking questions of an agreed document or knowledge set, with the source available to check.
Database-aware AI
Applications that can query or summarise business data the organisation already stores, within the permissions of the user.
LLM integration
Connecting an established model API into the application, rather than training a foundation model.
Structured outputs
Asking the model for a result the application can validate: a category, a set of fields, or a draft the user can edit.
Application integration
Putting the AI feature into the screens, APIs and permissions of the product, including systems built in .NET or Angular.
Review where it matters
A person checks the result before it is sent, stored as fact, or used to change a record.
Typical Reasons to Build It
- Staff spend time searching documents that should be answerable from an agreed source
- A product needs a copilot for a defined task, not an open-ended chat with the whole company
- An application should draft a response or a record that a person then edits
- Users need to ask questions of a database without being given unrestricted access to it
- An existing .NET or web application needs one AI feature, not a replacement
How We Approach the Product
Smaller features compress this. A copilot inside an existing application still needs the same decisions.
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Define the task
Agree what the software must help with, and what it must not answer.
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Name the sources
Decide which documents, records and permissions the feature may use.
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Build the application
Interface, retrieval, model call and validation as one product, in reviewable increments.
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Check the answers
Review real examples, including the ones the model handles badly.
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Release and watch
Put it in front of users with a way to see failures, not only successes.
Reliability
Production AI Needs Limits
We do not claim that an AI feature will be accurate every time. Models produce plausible text, including when the source does not support it. A production feature has to show its sources where that is possible, refuse questions outside its scope, and keep a person in the path when a wrong answer would be costly.
Validation, logging and a way to turn the feature off are part of the build, not a later compliance exercise. We will say when a conventional search box or a fixed form would be more reliable than a model.
Evaluation is practical: a set of real questions, the answers we expect, and a review of what the system actually returned. It is not a promise of a percentage.
What the Software Usually Connects To
We do not train foundation models. The engineering is the application around an established model, including how it reaches your data.
Product
Retrieval
Model
Engineering
Related work
Where It Meets the Rest of the Work
A retrieval feature often becomes search once people need to browse, not only ask. A copilot that talks to users is closer to conversational AI and should share the same rules about sources and handoff. If the answer has to be written back to another system, that join is an integration, not something the model should invent.
Related Services
The product, the way people find things, and the broader AI capability usually belong in the same conversation.
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 AI Software
Do you train your own language model?
No. We integrate established models and build the application, retrieval and safeguards around them.
Will the assistant only use our documents?
That is a design decision, and it has to be enforced in the software. If a source is not in the agreed set, the feature should not pretend it consulted it.
Can this replace the people who know the subject?
No. It can draft, retrieve and summarise. Someone who understands the work still needs to check anything that will be relied on.
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.