AI solutions that do real work

A regular store search looks for words. When the shopper types “a gift for a 10-year-old who loves drawing” or makes a typo, the results come back empty and the sale is lost.

AI-powered search looks for meaning. It understands synonyms, typos and needs described in natural language, and matches them with product attributes. Results can also be ranked by stock, margin or campaigns, so search serves both the shopper and your sales.

Read the article on AI search

Supplier data is rarely consistent: one gives dimensions in centimetres, another in inches, a third does not state the material at all. Incomplete product cards mean the product does not show up in filters and the shopper does not get an answer to their question.

AI reads supplier descriptions, spreadsheets and product images and fills in the missing attributes from them. It standardises units and values, puts the product in the right category and flags doubtful data for review. A person approves, and AI does the heavy lifting.

PIM integration

With thousands of products and several languages, nobody has time to write a good description for each one. Often the description ends up being the supplier's text or just the product name.

AI writes the product description from the product's attributes: material, dimensions, use and benefits become readable text in your brand's voice and in every language of the store. The text updates when the attributes change, and an editor reviews only what needs reviewing.

Every product image should have an alt text that describes the image to a screen reader user. With thousands of images this usually goes undone, even though accessibility requirements already apply to online stores.

AI looks at the image and the product data and writes a meaningful alt text in every language of the store. At the same time it tags the images: colour, material, style and image type. The tags add to the product attributes, improve filters and search, and help find faulty or missing images.

Accessibility

More and more shoppers ask an AI assistant for a recommendation instead of a search engine. The assistant recommends the stores whose product information it can read reliably: price, availability, attributes and delivery terms.

We make the store's output machine-readable: structured data on every product, clean and semantic HTML, product feeds, and pages that load fast even without a browser. The same work improves accessibility and regular search visibility.

Read Margus Eha's article

The next step after reading is acting. An AI agent does not stop at a recommendation: it searches for the product, compares, checks stock and adds the product to the cart on the shopper's behalf.

MCP (Model Context Protocol) is an open standard through which an AI assistant can use your store's functions: search, stock, prices and order status. WebMCP brings the same to the browser, where the web page itself tells the agent what can be done on it. We build both so that the agent sees only what you allow.

Much of customer support is the same questions again and again: where is my order, will this product fit, how do I return it. The answers exist, but the shopper has to wait for them and the team has to write them over and over.

We integrate an AI chatbot with the online store that answers from your product catalogue, terms and order data, not from general knowledge. The bot finds the order status, recommends products and guides the shopper through a return. It hands a harder question over to your support team together with a summary of the conversation.

A campaign in every language, a newsletter for every segment and product feeds for every ad channel take the team weeks. Much of it is reformatting the same information.

AI drafts campaign texts and newsletters from the product catalogue and shopper behaviour, splits customers into segments and optimises product feed titles for ad channels. The marketer decides and approves, AI prepares.

Rewriting an old system is expensive mainly because nobody remembers everything it does any more. The business logic is hidden in thousands of lines of code that have not been documented for years.

AI analyses the existing code, maps the business logic and writes a first version of it on the new platform. A developer reviews, fixes and tests it. We compare the behaviour of the old and new system with automated tests, so nothing important gets lost. This way an online store or internal system moves to a new platform much faster than with a manual rewrite.

Over the years, different people write content by different rules. The result is inconsistent formatting, broken links, duplicates and texts that no longer fit the new design or content model.

AI brings the content into one form: it standardises headings, formatting and terms, removes duplicates and splits free text into structured fields. This is especially useful during a platform migration, when thousands of pages have to move into a new system.

Opening a new market means translating the whole website, the product information and the user guides. Machine translation without context does not know your terms or your brand's voice.

AI translates content according to your glossary and style guide and keeps terms consistent across languages. Existing text can be rewritten the same way: shorter, in plain language, for another audience or for search engines. A translator or editor reviews the result but does not have to start from a blank page.

Writing and maintaining tests takes a large share of development, and under time pressure it is the first thing to be skipped. Bugs then reach the users.

We use AI to create tests from the requirements and the existing code, in code review and in accessibility checks. AI also suggests edge cases a person might not think of, and helps find bugs faster. Every test and change goes through a developer, so a person remains responsible for quality.

A general AI assistant knows nothing about your customers, orders or documents. The value comes when the assistant can use your systems securely.

We build an MCP server for your systems, through which an AI assistant can search documents, look up data and carry out agreed actions, such as drafting content in the CMS or preparing a request. Every action has permissions and a log, and an important change goes to a person before it is published.

See the Neurisium project