AI agents & LLM platforms
Assistants that do work rather than demo it: tool use, retrieval over your documents, evaluations, guardrails and a cost you can predict.
AI software team
AI products that reach production
A small senior team. We design, build and run AI-backed software — and the ordinary product engineering around it that turns a model into a business.
- 8+
- years in production ▸ FILL IN
- 20+
- products shipped ▸ FILL IN
- 2 weeks
- to a prototype you can click
- 1 day
- to a reply, and an NDA if you need one
What we do
Six directions
E-commerce & marketplaces
Catalogue, cart, payments and the back office that runs them — with search, recommendations and generated content where they pay for themselves.
Image & video generation
Diffusion wired into a product, not a notebook: fine-tunes, render queues, moderation, storage and a cost model that survives real traffic.
Computer vision
Detection, tracking, OCR and quality control on your own footage — trained on your data, deployed to your hardware, measured on a labelled set.
Dashboards & analytics
The read path first: events, aggregates and a warehouse designed before the screen. Then dashboards that answer in one screen, not five.
B2C apps & games
Consumer products where retention is the only honest metric: mobile apps, games, onboarding, and the analytics that say whether it worked.
How we work
Four steps, and you can stop after any of them
Every step ends with something you own and can take elsewhere.
01
Discovery
What the model must do, what data exists, what "working" means in numbers. You get a scope, an architecture sketch and a fixed quote.
3 – 5 days · fixed price
02
Prototype
The riskiest part first, running on a URL, with the evaluation set that says whether the quality is there before anyone commits.
2 – 3 weeks
03
Build
Two-week increments, each deployed behind a flag, with tests, monitoring and the numbers that show it did what it should.
6 – 12 weeks
04
Run or hand over
We keep it running — cost, latency, quality drift — or hand it to your team with docs and a fortnight of pairing.
monthly · cancel any time
Selected work
Shipped, and still running
Most of it sits inside somebody's company under an NDA, so it is described by shape and result rather than by client name. All cases in detail →
RUBTUR Portal — the concierge service’s internal portal
remove the manual assembly of documents for a request — quotes, letters to hotels and stay programmes, in the client’s own templates.
- 5
- documents from one request
- 4 hours
- saved for one manager
AI agents for 
take the routine off the teams — eight jobs somebody did by hand every week, from watching the competition to writing up the meeting, each handed to an agent of its own.
- 8
- agent services in use
- −70%
- less time on research and fact-checking
Agent platforms for the banking sector
give the client a working agent platform rather than a prototype — and turn an agent engine into a product.
- 12
- agents and a builder in the demo
- 1 hour
- for anyone to build a new AI agent
Size data control
find the discrepancies between Lamoda’s size charts and the brands’ own sites — by hand that is 800 thousand rows of comparison.
- 150k
- SKUs under automatic comparison
- 800k
- rows instead of manual checking
The FashionJet → 
/ 
bridge
keep orders, prices and stock in sync between FashionJet and the marketplaces without manual exports.
- 2M+
- items refreshed every day
- 3
- bridges onto two marketplaces
B2B Video Studio
build advertising video scenes of a product with an “AI director”: one world for the whole clip, with prompts editable scene by scene.
- 1
- world across the whole clip
- 4
- stages from photo to video
RUBTUR Portal — the concierge service’s internal portal
remove the manual assembly of documents for a request — quotes, letters to hotels and stay programmes, in the client’s own templates.
- 5
- documents from one request
- 4 hours
- saved for one manager
AI agents for
take the routine off the teams — eight jobs somebody did by hand every week, from watching the competition to writing up the meeting, each handed to an agent of its own.
- 8
- agent services in use
- −70%
- less time on research and fact-checking
Agent platforms for the banking sector
give the client a working agent platform rather than a prototype — and turn an agent engine into a product.
- 12
- agents and a builder in the demo
- 1 hour
- for anyone to build a new AI agent
Size data control
find the discrepancies between Lamoda’s size charts and the brands’ own sites — by hand that is 800 thousand rows of comparison.
- 150k
- SKUs under automatic comparison
- 800k
- rows instead of manual checking
The FashionJet → / bridge
keep orders, prices and stock in sync between FashionJet and the marketplaces without manual exports.
- 2M+
- items refreshed every day
- 3
- bridges onto two marketplaces
B2B Video Studio
build advertising video scenes of a product with an “AI director”: one world for the whole clip, with prompts editable scene by scene.
- 1
- world across the whole clip
- 4
- stages from photo to video
Content for 
and 
fill product cards with additional photos at catalogue scale — in the admin and on the public side — with batch food photography pipelines and img2video.
- 15+
- prompt iterations kept
- 4
- model families in the pipeline
Inspection of oil and gas infrastructure
inspect routes and facilities from a helicopter without processing terabytes of video by hand after the flight.
- a week → 1 h
- to process the data
- 3 → 1
- people in the process
A learning system for a large holding’s support department
bring three disconnected systems into one platform for learning and testing.
- 1500
- employees moved onto it
- up to 100%
- faster onboarding
An analytics system for an AdTech company
bring 20+ advertising platforms into one system — reports by team and profit by campaign.
- 30 → 500
- growth of the department’s headcount
- +300%
- department profit
ozhiv.ai — a meme video from the user’s photo
take the user from the first screen to a paid clip — the whole product.
- 11+
- clip templates in production
- 2
- landing languages: RU and EN
World of Heroes — a mobile RPG
build a hero-collector mobile RPG: MVP in six months, the release version in a year.
- 100 000
- installs across both stores
- 2
- regions on one backend
Content for and
fill product cards with additional photos at catalogue scale — in the admin and on the public side — with batch food photography pipelines and img2video.
- 15+
- prompt iterations kept
- 4
- model families in the pipeline
Inspection of oil and gas infrastructure
inspect routes and facilities from a helicopter without processing terabytes of video by hand after the flight.
- a week → 1 h
- to process the data
- 3 → 1
- people in the process
A learning system for a large holding’s support department
bring three disconnected systems into one platform for learning and testing.
- 1500
- employees moved onto it
- up to 100%
- faster onboarding
An analytics system for an AdTech company
bring 20+ advertising platforms into one system — reports by team and profit by campaign.
- 30 → 500
- growth of the department’s headcount
- +300%
- department profit
ozhiv.ai — a meme video from the user’s photo
take the user from the first screen to a paid clip — the whole product.
- 11+
- clip templates in production
- 2
- landing languages: RU and EN
World of Heroes — a mobile RPG
build a hero-collector mobile RPG: MVP in six months, the release version in a year.
- 100 000
- installs across both stores
- 2
- regions on one backend
FAQ
The questions we always get
How do you charge?
Discovery is a fixed price. After that, a fixed scope for a fixed price or a monthly team rate — whichever fits the risk.
Who owns the code and the models?
You do, from the first commit — repositories, fine-tunes trained on your data, and infrastructure described as code so you can move it without us.
Our data cannot leave the country. Is that a problem?
No. We run open-weight models on your hardware or in your cloud region, and we tell you plainly what that costs in quality and in money.
What if AI is the wrong answer?
Then we say so in discovery and you have spent days instead of a quarter. A search index or a better data model often beats a language model.
Contact
Tell us what you are building
A paragraph is enough. If it is a fit we come back with questions and a first step; if it is not, we say so.