OCR & document extraction
Fields, tables and text from scans, PDFs and photos, turned into structured records.
Overview
Your emails, scans and photos are data you aren't using yet
A lot of what a business knows is stuck in emails, contracts, scanned forms, product photos and support messages. NLP and computer vision turn that into structured data you can search, route, check and report on.
We build the practical versions: pulling fields from invoices and forms with OCR, tagging and classifying text, searching by meaning, and recognising or checking things in images. We measure accuracy on your own documents, not a benchmark.
Keyword search gets worse as a catalog or library grows. Someone searching "warm jacket" never sees the product called "insulated coat". We build semantic search on vector embeddings over your existing catalog or CMS, add recommendations that surface genuinely related items, and tune relevance against real queries after launch.
Tools & platforms we use
- Python
- spaCy
- Hugging Face
- OpenCV
- Tesseract
- vector databases
- Algolia
What's included
Usable data from messy inputs
Text processing
Classification, entity extraction, summaries and sentiment for emails, tickets and reviews.
Semantic search & recommendations
Vector search that matches meaning, and recommendations based on real relevance, tuned on actual queries.
Image classification & vision
Recognition, categorising and quality checks on images, trained on your own examples.
Questions
Common questions about NLP and computer vision
5 questions
How accurate is OCR on our documents?
It depends on scan quality and layout. We test a sample of your real documents first, so you see the actual accuracy and where a person still needs to check.
Do we need to train a custom model?
Often not. A pre-trained model with light tuning covers many cases. We only train a custom one if testing shows it's worth the effort.
Can this handle languages other than English?
Yes, with models that support them. Quality varies by language, so we check early against your content.
Is semantic search worth it for a small catalog?
Sometimes not. Under a few hundred items, decent keyword search often works fine. We'll tell you if the extra complexity isn't worth it for you.
How is semantic search different from keyword search?
Keyword search matches exact words. Semantic search matches meaning, so 'cancel my plan' also finds a page called 'ending your subscription'.
Quick question?
Ask us about NLP, Search & Computer Vision
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Ready to get started?
Send us a few sample documents or images.
Share a handful of real examples and what you want from them. We'll test it and send a fixed estimate.
We'll redo the first milestone at no cost if it doesn't match the brief.
NDA signed before we see anything. Delivered under your brand.