AI Integration Services
We integrate AI capabilities into web applications — practical, production-ready features built on OpenAI and delivered within your existing systems.
AI is most valuable when it is embedded directly into the tools your business already uses — not sitting in a separate platform that your team has to remember to open. We integrate AI capabilities into web applications, making your existing systems smarter without replacing them.
Our AI work is practical and grounded in real delivered projects. We have integrated AI-powered search, document processing, structured content generation, and conversational interfaces into production applications — all using OpenAI APIs within .NET-based systems. We are not a data science company and we do not build custom machine learning models. What we do is design and implement AI integrations that solve real problems cleanly and reliably.
What We Have Built
AI-Powered Semantic Search
Standard keyword search breaks down when users describe what they want in natural language rather than typing exact product names or codes. AI-powered semantic search understands the intent behind a query — not just the words.
We implemented this for a B2B electronics platform with a catalogue of 450+ specialist RF products. A user can describe what they need in plain language, and the system finds the right products — even when the terminology does not match exactly. The implementation uses an LLM to convert the user's query into optimised search keywords, passed to an OpenAI Vector Store for semantic matching. The result is a search experience that works the way users actually think, not the way a database is structured.
Document Processing & AI Scoring
Manually reading, extracting, and evaluating documents is time-consuming and inconsistent. AI can handle this at scale — reading uploaded files, pulling out the information that matters, and scoring or classifying based on criteria you define.
We built this into a recruitment platform where candidates upload CVs in PDF or Word format. The AI reads each document, extracts the relevant candidate information, and scores the candidate against the role criteria using a prompt-based evaluation. Recruiters get structured, consistent data instantly — rather than reading every CV manually. The same approach applies to any application that receives documents and needs to process, classify, or evaluate their contents.
Structured AI Content Generation
Sometimes AI is most useful not as a search tool but as a generation engine — taking a user's input, combining it with your application's own rules and context, and producing structured output that feeds directly into the next step of a workflow.
We built this pattern into two applications: a quiz platform and a career counselling tool. In both cases, the system combines application-level instructions with a user's input and the AI returns structured JSON — questions, options, correct answers — that the application consumes directly. This approach works wherever your application needs to generate data dynamically based on user input: assessments, recommendations, personalised content, and more.
Conversational Interface for Application APIs
Most applications expose their functionality through APIs — but using those APIs typically requires knowing exactly what to request. A conversational interface changes this: users describe what they want in plain language, and the AI figures out which API calls to make, executes them, and returns the result in a readable response.
We built exactly this — a chat interface that interacts directly with a live application's API. The system reads the application's OpenAPI/Swagger documentation, uses OpenAI's tool call mechanism to identify and execute the right API calls based on the user's question, and returns context-aware responses. It supports multi-step queries where the answer requires calling multiple endpoints in sequence — turning any well-documented API into something a non-technical user can query conversationally.
How We Approach AI Integration
Every AI integration we build follows the same principles — it has to work reliably in production, not just in a demo. We design the integration around your data, your users, and your existing system rather than starting with a technology and working backwards.
We work with OpenAI APIs integrated into .NET applications — the same stack we use for the rest of our web development work. This means AI features are not bolted on as an afterthought; they are part of the application architecture from the start. We handle prompt design, response validation, error handling, and the plumbing that makes AI features feel like a natural part of the product rather than a separate tool.
We are honest about what AI is good at and where it is not the right tool. If your requirement is better served by conventional logic or search, we will tell you. When AI is the right fit, we design an integration that is maintainable, cost-aware, and built to evolve as the models improve.