Find the answer in three seconds. Even in ten thousand documents.
Invoices, delivery notes, contracts and a product catalogue where customers never find what they are looking for. Five modules clean it up: semantic search, auto-suggest, invoice scanning, custom OCR and classification & tagging. Trained on your own documents and your own language.
Try it yourself · fully interactive demo with fictional data
Five modules that turn text and paper into structured data.
Each module connects to your catalogue, your archive or your inbox and is trained on your own documents, so it understands your terminology. Start with one, expand when it makes sense.
Semantic search
The search understands meaning instead of just letters: queries and documents are translated into embeddings, and vector search finds the closest match, even when the customer types "rain jacket for cycling" and the product is called "waterproof cycling jacket". The input is your product catalogue or document archive. The output is fewer zero-result searches and more searches ending in the basket.
Auto-suggest
Suggestions as the user types, ranked by your own search log and sales data instead of a generic dictionary. Fuzzy matching forgives typing errors, so "cyclng jacket" still hits the mark. The result is a shorter path from first letter to the right product, measurable in zero-result searches and in the conversion from search to purchase.
Invoice scanning
Incoming invoices are read automatically: OCR extracts the text, and field extraction finds amounts, dates, VAT numbers, invoice numbers and line items. Each field gets an OCR confidence score, so only the uncertain ones go to manual review while the rest are posted directly to e-conomic, for example. The typical gain is minutes instead of hours per stack of vouchers, and fewer typing errors in the books.
Custom OCR
Off-the-shelf OCR fails on delivery notes, forms, meter readings and handwritten notes. A custom-trained model learns exactly your document types, with confidence thresholds and human-in-the-loop, so edge cases are always seen by a person. The input is a sample of your own scanned documents. The output is a pipeline that turns paper into data with measured accuracy.
Classification & tagging
Documents, emails and products are sorted automatically by your own taxonomy: an NLP model classifies the content, and named entity recognition finds names, dates, amounts and references that become searchable tags. The model is trained on examples you have already sorted, and its accuracy is measured with precision and recall before it is let loose. The result is an archive that files itself, and a catalogue with consistent categories.
Want to see all 24 modules in one place? See the full overview.
From first call to finished module.
Spot
A 30-minute video call. We find the search and document modules that move the most for you, and you get a written report with impact estimates.
Prototype
A working demo on your own documents or your own catalogue, built in 1-4 hours. You see the search and the extraction working before you decide anything.
Build
A production-ready module in your dashboard, delivered in 1-4 weeks. Full documentation, 30-day guarantee, no lock-in.
What could it mean in real money? Try the calculator · or see all solutions.
Search and document AI for small and medium-sized businesses
Much of an SMB's knowledge lives in text that no system understands: invoices in the inbox, delivery notes in binders, contracts on a drive and a product catalogue where the search function only finds exact words. The consequence is the same everywhere: employees spend time looking, bookkeeping types vouchers in by hand, and the webshop's customers leave the site because the search returned zero results for a product that was actually in stock.
Document AI turns that around. Semantic search with embeddings finds meaning instead of letters, so the customer's own words hit your catalogue. OCR and field extraction turn paper and PDFs into structured data with measured confidence per field, so only edge cases need a person. And classification models sort documents and products by your own taxonomy, consistently and without piles that keep growing.
The advantage of custom-trained models over off-the-shelf tools is that they learn your reality: your product names, your forms, your abbreviations. The modules are built on your own documents and handed over with full ownership of code, models and training data. One module at a time, a fixed price agreed in advance, and a free prototype before you decide.
Try the search on your own catalogue.
Get a free demo built on your own data. You see the result before you pay anything.