Process audit and choosing where to apply it
We work out exactly where time and money go, and choose a process whose effect can be calculated. It often turns out that half the tasks are solved by automation with no AI at all — and we say so plainly.
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.
For companies where manual work has become the bottleneck — processing documents and requests, support, recruitment, quality control, planning — and where there is a way to measure the result.
What is included
Data check
We look at whether the data exists, what shape it is in and whether a model can learn from it. This is the most honest stage: if there is no data, there is no going further, and it is better to learn that in week two than in month five.
A pilot with a measurable hypothesis
We frame the hypothesis so that it can be disproved: which metric, by how much, over what period. The pilot runs on a real part of the process, not on demo examples.
Integration into your working systems
AI is useful when it lives inside the systems you already have — 1C, CRM, ERP, email, messengers, internal services. A separate window that people have to open by hand usually stops being opened after a month.
Closed perimeter and security
We deploy solutions on local models when data cannot leave the company perimeter. Access control and action logging are designed together with the system, not added later.
Handover to your team
Documentation, staff training and a clear operating model. A system that only the contractor can maintain is not an asset but a dependency.
How we work
A conversation about the process
Half an hour to understand the task and call things by their names: where the effect is, where the data is, where the constraints are.
Rapid diagnostics
We look at the process and the data, pick 1–2 scenarios with a calculated effect and assess how realistic they are.
Pilot
A working solution on a limited part of the process, with a metric and a comparison against how things were before it.
Production
Integration, load, fault tolerance, running costs, access rights and audit.
Growth
Quality control on real data and extension to neighbouring processes — on the same principle of measurability.
Why us
We build and run our own systems
We have our own products and libraries in production — with knowledge graphs, vector search, telemetry and agents. We know the cost of running them because we pay it ourselves.
A research school
We do our own research and publish the results. That means the method gets tested, and its limits are stated out loud rather than hidden behind the words “neural network”.
An honest stop
If AI will not pay off in your task, we will say so before work starts and offer a cheaper solution. A project that will not reach its effect is of no use to you or to us.
Cases
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
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)
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
Music tech · SaaS
MVP in operation (closed launch)Music releases without missed deadlines
Release ops for independent labels: 9-stage lifecycle, pitching deadlines for 12 stores, AI drafts with human approval. Zero to production-grade in 7 weeks.
- 7 weeks
- from first commit to a production-grade stack with Vault, encrypted backups, SLOs and a move to a Russian data centre (265 commits)
- 16 days
- from project start to an investor presentation with a live product demo
EdTech · Mobile app
In-house productA mobile client for an AI learning platform, built without waiting on the backend
Flutter client for an exam-prep platform: adaptive micro-lessons, a streaming AI assistant, a knowledge map. UI built in parallel with the backend via a mock layer.
- 13 weeks
- from first commit to a working app with auth, goals, lessons and AI chat
- 41 → 81 endpoints
- API coverage doubled from MVP to the extended version — 14 → 31 modules on the same architecture
IT consulting · Lead qualification
In-house productA pilot plan for the visitor, a qualified lead for sales
We 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.
- 1 day
- Working MVP — frontend, backend and deploy configs — built in a single day
- 16 days
- From first commit to production with HTTPS and an issued certificate on its own domain
Frequently asked questions
How much does it cost to implement AI in a company?
The cost is set not by “the model” but by the state of the data, the number of integrations and the perimeter requirements. Cheapest are tasks with ready data and a single integration point; most expensive is work with scattered sources, a closed perimeter and strict quality requirements. We name a range after the rapid diagnostics, not in the first email.
How long does implementation take?
Diagnostics takes weeks. A pilot on a limited part of the process usually takes from a few weeks to a couple of months. Full production use depends on the integrations: most often it is they, not the model, that set the timeline.
What if we have little data?
That is a normal situation, and it does not always block the work: some tasks are solved with ready-made models using your documents and rules, without training on historical data. But if the task needs history and there is none, we will say so plainly and suggest starting with data collection.
Our data must not leave the company. Is that possible?
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 scenario.
How is this different from a chatbot?
A chatbot answers questions. Implementing AI in a process means the system does the work: it parses documents, prepares a decision, passes the result to your system and leaves a trail for audit. The conversational interface is a detail here, not the point.
How do we know the implementation worked?
By the metric we fixed before the start: processing time, share of manual operations, cost per operation, number of errors. If the metric did not move, the hypothesis is disproved — and that is also a result, obtained cheaply and quickly.
Related services
Cost the effect on your own numbers
AVA is a process economics calculator. In a couple of minutes it shows whether this service pays off in your case — before you talk to us, with no commitment.
Estimate the impact with AVATell us about your task
Tell us 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