Unclear where AI will actually help
There are plenty of promises around, but it is unclear which task in your company will pay back the investment and which will save nothing afterwards. It is easy to invest in a demo rather than a result.
Problem → solution
Adopt AI
You need AI, but it is unclear where to start and whether it will pay off. AI projects more often fail not at the model but earlier — the chosen process cannot be counted in money, or the data for it does not exist. We start with the process and a data check, and decide on the technology last.
For companies that want to use AI where it matters: take routine off people, get value from accumulated data, speed up decisions — but are not prepared to pay for a fashionable project with no measurable result.
What people come with
The data is there, but brings no value
The company has been collecting data for years, but it sits there as dead weight: scattered, unlabelled, spread across systems. Getting a forecast or a decision out of it in-house does not work.
The data cannot leave the company
Off-the-shelf cloud services are ruled out: personal data, trade secrets and security requirements do not allow sending data to an external provider.
How we solve it
Pick the task for its impact
We look for a process where AI pays off and check whether the data holds a signal. If there is no signal, we will say so before you spend the budget on a model.
Build a baseline and a pilot
First the simplest solution as the bar, then a pilot on a real part of the business with a measurable hypothesis. If the complex one does not beat the simple one, it is not needed.
Take it to production
The model or agent lives inside your systems: a data pipeline, quality and drift monitoring, running costs, access rights and audit.
Monitor and expand
Forecasts and quality degrade over time — this has to be watched. We check against real data and carry the approach over to adjacent tasks.
What it is built on
Agents and assistants
Where the work needs understanding text and seeing a task through to a result, an agent takes it on — with access to your systems, company knowledge and scoped permissions.
Predictive analytics
Forecasting demand, equipment failures, churn and risks — with measured accuracy, a comparison against a naive baseline and clear behaviour when the data fails.
Company knowledge and a closed perimeter
Documents and procedures become the system's memory through semantic search and knowledge graphs. When data cannot leave the perimeter, everything runs on local models inside your infrastructure.
What we do for it
AI implementation · Process automation · Integration
AI implementation for business: we start with the process, not the model
An AI implementation project does not fail on the model; it fails earlier. The chosen process cannot be measured in money, or the data for it does not exist. So we start with the process and a check of the data, and decide on the technology last.
AI agents · Assistants · Task automation
AI agent development: autonomy you can trust with the work
An agent differs from a chatbot in that it acts: it chooses its own steps, calls your systems and carries the task through to a result. So the main question is not “which model” but what authority it has, how its work is checked and what happens when it makes a mistake.
Forecasting · ML · Data analytics
Predictive analytics: solutions that calculate rather than guess
A forecast is useful exactly as far as it can be trusted when a decision is made. We build models whose accuracy is measured, whose limits of applicability are known and whose behaviour when the data fails is understood.
What it looked like in practice
Marketplace e-commerce · Sales analytics
In productionOne profit per SKU across three marketplaces, reconciled to 0.00%
Five seller accounts on WB, Ozon and Yandex Market merged into one SKU-level P&L: 0.00% variance vs the client's manual benchmark on a control SKU for one reporting period.
- 0.00%
- variance vs the client's manual benchmark across units, revenue, COGS, profit and margin for a control SKU over a month; threshold was 1%
- 1.18M rows
- of sales across 5 accounts and 12 months processed; 10.7% were ordered and settled in different months
Distribution · Partner networks
In productionA sales and support platform for a distributed partner network
Partners ask who on their team is inactive and what blocks the next level — and get numbers for their own part of the network only, enforced at the SQL level.
- 20 of 21
- UAT checks passed on the client's live back-office database (reconciliation protocol); one partial — no last-activity date in the data
- 10 of 10
- question categories from the assistant behaviour spec (~25 scenarios) implemented; one sub-item outstanding (checklist count)
Aviation — aircraft components trading and MRO
PilotAircraft parts procurement: from inbox chaos to a managed pipeline
An AI agent reads incoming RFQs, compares supplier quotes and checks trace documents; an operations system moves each deal through mandatory stages.
- Full loop
- full AI loop on a live procurement process — from incoming RFQ to team notification; confirmed by the owner
- 3
- types of trace documents checked by the agent: FAA 8130-3, EASA Form 1, COC; counted from the owner's brief
Cost the effect on your own numbers
AVA is a process economics calculator. In a couple of minutes it shows whether automating your case is worth it — before you talk to us, with no commitment.
Estimate the impact with AVAFrequently asked questions
Where should AI adoption start?
With the process and the data, not with choosing a model. We run a rapid diagnostic: where the impact can be counted in money, and whether there is data to learn from. Its results show whether it is worth going further at all.
What if we have little data?
That is common and not always a blocker: some tasks are solved with off-the-shelf models plus your documents and rules, with no training on history. If the task does need history and there is none, we will say so plainly and suggest starting with data collection.
Can we adopt AI without sending data outside?
Yes. We deploy solutions on local models inside your perimeter — with no calls to external providers. It costs more in infrastructure and needs attention to performance, but it is a fully workable setup.
Other tasks
Automate a business process
Manual work has become the bottleneck: people sort through requests and documents, move data between systems and answer the same questions over and over. Automation does not pay off everywhere — so we start with where exactly the time and money go, and automate what can be counted in money.
Crack a hard problem
A problem that standard design cannot solve: there is no ready algorithm, the data behaves unpredictably, and the outcome depends on accuracy. We work where the subject is the method itself: we frame a testable hypothesis, research it and take it to a working prototype.
Build the infrastructure
The product is growing, but the infrastructure holds it back: releases are scary to ship, clients are the first to report failures, and the backup is first tested when it is already needed. We make deployment a boring operation, failures visible in advance and recovery a tested procedure.
Tell us about your task
Describe what needs solving. If it cannot be solved or will not pay off, we will say so straight away, before any work starts.
or email us directly: hello@xteam.pro