AI systems that go into production and stay there.
Document understanding, private LLM assistants, training simulators and real-time operations dashboards for European small and mid-sized companies. Designed, built and supported by one senior engineer, end to end.
Four directions, one engineering standard.
Each tile is live. Pick the one closest to your problem, the page behind it goes deep.
Document AI
In productionCollect documents from customers by phone, check the photo on the spot, read the fields, route the case. Invoices, contracts, IDs, forms, handwriting. Low-confidence fields go to a human, everything else goes straight into your systems.
Private AI
Prototype · demo on requestA ChatGPT-class assistant over your own documents, running on your hardware or in your EU tenant. Every answer cites its source. Nothing leaves your network.
AI training simulators
Two prototypesRole-play training that adapts to the trainee: crisis response for aviation and travel, customer situations for couriers, onboarding for any front-line role. Scored, repeatable, in any language.
Operations AI
PrototypeComputer vision on the cameras you already have, plus a live dispatcher picture: yards, loading bays, quarries, production lines. Count, identify, flag what is stuck, and show the dispatcher what matters now.
Explore Operations AIWhat we have built so far.
Honest labels. "In production" means a client uses it every day. "Prototype" means it works and we will show it on a call.
Document capture platform
In productionCustomers photograph their documents on the phone, the system checks each shot on the spot, reads the fields, asks for confirmation and hands a clean case to staff. Review queue with a request-changes loop. 10 document types, RU/EN/DE, 100+ cases. Own CPU models: detectors, classifiers, MRZ, key-value.
Crisis Command
PrototypeLLM-driven crisis training simulator for aviation and travel companies. Scenarios branch on the trainee’s decisions and are scored against a playbook.
Delivery Ace
PrototypeInteractive training simulator for delivery couriers: difficult customers, damaged parcels, time pressure. Same engine, different playbook.
Digital Yard
PrototypeDispatcher cockpit for industrial rail yards, replacing Excel, plus wagon-number recognition on the client’s existing cameras.
Private on-premise assistant
Demo on requestGPU RAG assistant over company documents: chat with citations, search, extraction. Runs on a single DGX Spark class box inside your network.
PIX-TAB, CVPR 2026
ResearchPixel-precise table structure recognition with speculative decoding, co-authored. The same table-reading know-how goes into every extraction pipeline we ship.
Not sure which direction your problem is? Bring it to a 30-minute call, we tell you honestly, and whether AI pays back at all.
Book a callFrom first call to running system.
Fixed scope, fixed price for the first slice. You know the saving in hours and euros before we start.
Analysis
Inside your process, not in slides. Savings estimate in hours and euros.
Development
Built for your documents, languages and edge cases. Thin end-to-end slice first.
Integration
CRM, ERP, DMS, email. Your team keeps its tools.
Training
Onboarding for the people who will live with it. Same-day support.
Improvement
Corrections become training signal. Accuracy report you can audit.
“You talk to the person who designs, builds and supports the system. No hand-off, no account manager, no junior team behind the curtain.”
Prefer to see the systems before talking? The demos above open without a signup. The production platform we show on the call.
Back to the work ↑Frequently asked
Is it safe to trust our data to AI?+
In the default setup the data never leaves your infrastructure. Models run on your servers or in your EU tenant. Nothing goes to third-party APIs unless you explicitly choose that trade-off.
Are you an agency or a single person?+
A company of one senior engineer who brings in specialists per project when needed. You always talk to the person who builds the system, and that person has 15 years of shipping ML into production.
Some of your work is labelled "prototype". Why?+
Because it is. Prototypes work and we demonstrate them live, but they have not run at a client for months the way the document platform has. We would rather you know the difference before the first call.
How long does implementation take?+
A first working slice in 3–6 weeks, full rollout in 2–4 months depending on integrations.
How much does it cost and when does it pay off?+
Fixed-scope pilot first; most projects pay back within 2–6 months. The saving is estimated before we start and written into the proposal.
Bring three real documents to the call.
Thirty minutes. You describe the process, we tell you honestly whether AI will pay back, how fast, and which of the four directions it is.
Book a call with Artem