Problem → solution
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.
For business leaders and operations teams whose routine processes eat up staff time: handling incoming requests, document workflow, first-line support, preparing reports, managing deals.
Staff sort email and requests by hand, reconcile spreadsheets and copy data from one system to another. It is expensive, slow and scales badly: to do twice as much, you have to hire twice as many people.
Stages, deadlines and owners rest on verbal agreements and managers' memory. Things get lost, control is only possible by hand, and when a person leaves, part of the process leaves with them.
You want to automate everything, but the budget is limited, and some processes will save nothing once automated. Without an honest estimate of the impact, it is easy to invest in something impressive but useless.
We work out where exactly the time and money go, and pick a process whose impact can be counted. Often half the tasks are solved by automation with no AI at all — and we say so plainly.
We check whether the data for automation exists and what shape it is in. If there is no data, it is better to learn that in week two than in month five.
We build a working loop on a real process with a measurable hypothesis: which metric, by how much, over what period. We test it on the live flow of work, not on demo examples.
The automation lives inside your systems — email, CRM, ERP, messengers — with an action log and access rights. Then we extend it to adjacent processes on the same principle of measurability.
Where routine work calls for understanding text and making decisions, an agent takes it on: it reads what comes in, extracts data, prepares the result and passes it to your system — with scoped permissions and an action log.
We build mandatory stages, roles and access into the system and the database, not into a manual. The process becomes manageable: it cannot be skipped or bypassed through an oversight.
When data cannot leave the company perimeter, we deploy the solution on local models inside your infrastructure — with no calls to external providers.
AI implementation · Process automation · Integration
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
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.
Aviation — aircraft components trading and MRO
PilotAn AI agent reads incoming RFQs, compares supplier quotes and checks trace documents; an operations system moves each deal through mandatory stages.
Distribution · Partner networks
In productionPartners 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.
IT consulting · Lead qualification
In-house productWe replaced the contact form with an AI consultant: it interviews the visitor, returns a pilot plan with KPIs and risks, and sales gets a lead with budget and timeline.
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 AVAWith one process whose impact can be counted. We run a rapid diagnostic, select 1–2 scenarios with measurable savings and start with those. That way the first result comes quickly and justifies the next step.
No. A large share of routine work is handled by ordinary automation and integrations — cheaper and more predictable. We add AI where the task calls for understanding text or making decisions, and where it pays off.
By a metric fixed before the start: processing time, share of manual operations, cost per operation, number of errors. You can estimate the impact on your own figures in advance — with the AVA calculator, before the first conversation.
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.
You have a product idea, a business model or a mock-up — but no working system, or one that cannot cope with growth. We design and build the product with operation in mind: the minimum scope that tests the hypothesis, on an architecture that will survive the second client.
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.
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