AI Integration Services

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.

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 — for example, a request to generate a quiz about a specific topic — and the AI returns the output in a structured JSON format (questions, options, correct answers) that the application consumes directly. The AI behaves as a reliable structured data generator, not just a text output. This approach works wherever your application needs to generate data dynamically based on user input — assessments, recommendations, personalised content, and more.

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 API documentation (OpenAPI/Swagger format), 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. This turns any well-documented API into something a non-technical user can query conversationally — a significant step towards making complex business systems genuinely accessible.