AI that solves a specific task, not just a buzzword.

AI can be built directly into an existing website, webshop or internal platform – for example a feature that analyzes information, generates content or helps an employee with a task. Businesses with large amounts of data can use AI to create first drafts of product descriptions, FAQs, metadata, category copy, comparisons or summaries.

The interesting part is often not the text itself but the system around it: where does the data come from, how is the output validated, how do we avoid duplicates, and how does it get published? Those are the kinds of processes I can help build.

AI can also work with documents – a system can read a collection of documents and make the information searchable or help users find the relevant parts. Instead of an AI model having to know everything in advance, embeddings and RAG give it access to the business’s own data to use as context for its answers. That can power internal knowledge bases, support or advanced search features.

An AI model can also be part of larger workflows: receiving data, analyzing and classifying it, calling an API, generating a result, saving it to the database and passing the information on. Here the AI becomes one element in a larger automated system.

AI isn’t just about text. Models can also be used for object recognition, categorization, product analysis, image descriptions, quality control and automatic tagging.

It isn’t always necessary to send everything through an external AI API. I also work with open-source models that in some cases can be hosted on your own server or GPU infrastructure – relevant if there are requirements around cost, privacy, control, large data volumes or specialization. In other cases a hosted AI service makes far more sense, with models integrated via API and scaled as needed. I choose the solution based on the task rather than being locked into one particular model or vendor.

AI doesn’t necessarily have to work live in front of the user. If, for example, 100,000 products need to be analyzed, the work can run in batches in the background, which can cut both cost and complexity considerably.

If AI isn’t just a feature but the product itself, I also help with the architecture around it: user login, credits, subscriptions, API usage, rate limits, databases, job queues, model selection, admin panel and logging.

Just because AI can be used for a task doesn’t mean it should be. Sometimes a simple rule or an ordinary script can solve the problem faster, cheaper and more reliably. So I try to find the solution that actually makes sense – not just the one that sounds most advanced.