Oxagile AI · AI practice
AI is our native language.
You set the direction. Agents build it at AI speed, and the AI you ship holds up in production.
- What does AI-native mean here?
- Our engineers direct coding agents instead of typing the code. A named person still decides and signs scope, design and release.
- Is that safe for production?
- Checks run inside the agent loop, and a named person signs each release.
- Does it fit our setup?
- Yes. It runs inside your CI and your review.
- Where do we start?
- A readiness review. Fixed price, fixed end date.
Competencies
What we know, and how you can check it
So you can verify the work yourself, at any point.
The demo was never the hard part
Yours ran on a laptop in a week. We help it survive a regulator, a region, a rights contract and a bill that arrives monthly.
How we work- Fix first
Secrets committed to the repository
keys in history, still valid
- Fix
Tests on the payment path
none found
- Measure
Load on your busiest day
not measured yet
- Check
Dependency licences
one with unclear terms
- Holds
Deploys from a clean checkout
reproducible
Yours comes back with a price against each line.
Indexed once, queried thereafter
So your agents stop paying to reread the same code. One that queries an index builds it once and carries a 400-token answer instead of 10,000. Measured on one internal workspace of about 700 nodes.
- A
~400 tokens per architectural question, against ~10,000
- B
Answered in under a third of a second
- C
The answer is a subgraph
- D
Extraction runs locally, at no API cost
Written down while it happened
So you hand over the record instead of rebuilding it. A record rebuilt months later costs several times more and still has gaps an assessor will find.
AI Audits & Compliance- Evidence
Change approved by a named person
sign-off record, linked
- Evidence
Review by a session that did not write the code
review attached to the change
- Evidence
Secret paths refused
refusals logged with reasons
- Gap
Action log retention
owner: your platform team
The assessor reads the record as it was written.
How we help you win
Pick where to start. 10 services, one standard, each on your own pipelines.
All 10 services →
Service 01
AI-native SDLC
We ship at agent speed. A named person of ours owns each decision on the way.
Service 02
From vibe to viable
You shipped it fast. We help you make it last.
Service 03
AI Process Automation
Hand the routine reading, sorting and routing to a model, with a person on the exceptions.
Service 04
AI Product Delivery
Your AI product designed, built, launched and supported by one team, at agent speed.
Service 05
Computer Vision
Vision models built for your live cameras, factory floor and video archive.
Service 06
AI Data Analytics & Forecasting
Ask your data a question in plain English, and see problems coming before your customers do.
Service 07
AI Security
Your agents get write access, and the routes it opens close at the tool boundary.
Service 08
AI Audits & Compliance
Put agents into regulated work and still hand your assessor the evidence.
Service 09
AI Test Automation
Your tests ship with each change, so a deadline cannot cut them.
Service 10
AI Cognitive Testing
See how a model reasons on your cases, capability by capability, before your users do.
Industries
Your market, your regulator
The process stays the same. We fit it to the rules your market sets.
The machine makes the move. A person sets the rules.
AI gives you speed. A named person decides what ships. 4 sign-offs, then a release gate your release manager holds. A sign-off given verbally did not happen.
AI-native SDLC- Signed
Scope agreed in writing
you + our analyst
- Signed
Design before the build
our architect
- Signed
Work split into 30- to 60-minute units
our engineer
- Awaiting
Code with its tests, reviewed in a separate session
engineer + test lead
- Not yet
Release past the production gate
your release manager
On a live engagement each line links to its record.
Work that shipped
3 engagements you can read in full
Each with the result it reported and how much AI it involved.
OTT & Media · Media logistics company
Highlights cut while the match is still running
A model that watches the live feed, finds the moments worth replaying, and cuts the compilation before the second half.
- 72% reduction in video processing time
- 90% accuracy identifying scoring moments and fouls
- 3.2x faster highlight compilation
- 70% increase in footage processing speed
Retail · Promotional data provider
Printed promotions, cropped and read by machine
Computer vision crops each advertisement off the page, reads printed and handwritten text, and classifies the offer, at a volume no manual team could match.
- 99.3% cropping accuracy
- Almost 93% accuracy recognising the offer type
FinTech · Ecommerce company, food box ordering
Card payments moved to a new provider, live
Live card payments moved off one gateway onto another, with 3-D Secure added and payment tokens carried across, while the business kept taking orders.
- 1M+ users’ card payments migrated with zero downtime
- 3 months for the gateway transition
- 50,000+ daily recurring transactions
- $100 million processed a year
People
Who runs the practice
The leads behind every engagement. The full team is on the About page.
Alexey Karankevich
Head of Artificial Intelligence
Stanislav Volzhankin
Chief Delivery Officer
Vladimir Zubkov
Chief Engineering Officer
Oleg Stepanyuk
Head of Presales Engineering
Model
How the practice fits together
Every competency is delivered through one or more services. Each node opens its page.
Next step
Tell us where it breaks.
A model that will not hold a threshold. A compliance deadline. A process that stopped scaling. You get a written answer on what breaks first and what it costs.