Specific problems, not AI adoption
These are the problems we solve most often, grouped by function and by industry. Any of them can become a pilot. If yours isn't on the list, that doesn't mean we don't solve it.
What changes in each department
For each function: typical use cases, and the metric we use to measure the result.
Plain-language answers about the business, a daily digest of deviations, tracking of directives, meeting preparation.
Metric · time to answerCounterparty reconciliations, receivables control, consolidated reporting from ERP and accounting, source-document checks, plan vs. actual.
Metric · days to closeOutput plan vs. actual, downtime, quality control, procurement and inventory, on-site video analytics.
Metric · downtime, lossesLead qualification, proposal drafting, channel analytics, monitoring of sales activity in the CRM.
Metric · conversion, cycle timeMultilingual voice and text agents, request routing, response-time tracking, call analysis.
Metric · resolved without an operatorContract review against a checklist, regulatory search, standard document drafting, deadline control.
Metric · time per contract reviewRésumé screening, policy questions from staff, onboarding, a company knowledge base.
Metric · time to hireA single access point to data for analytics, first-line internal support, systems documentation.
Metric · first-line ticket loadTypical use cases by industry
Examples of what clients bring to us. We name clients only with their consent.
Ministries, municipalities, state enterprises
- Citizen request handling and routing between agencies
- Tracking of directives and deadlines
- A legal AI assistant over the regulatory base
- An analytics console for leadership
A corporate AI platform for a holding
- One knowledge base for projects, estimates and procedures
- Schedule and budget control per site
- Tender documentation drafting
- A staff assistant in corporate chat
Banks and insurers
- An in-app agent: payments by voice, text or a photo of a document
- Customer document package checks
- Analysis of requests and calls
- Operation inside the bank's closed perimeter
Chains, dealers, distributors
- Loyalty programs for partners and installers
- Regional marketing and sales analytics
- Receivables and inventory control
- Automated dealer ordering
Plants and industrial groups
- Real-time production plan vs. actual
- Video monitoring of safety compliance
- Procurement and supplier analysis
- A digital assistant for process engineers
Airports, railways, logistics
- Video analytics of passenger flow and zone occupancy
- Multilingual answers for passengers
- Asset maintenance planning
- Consolidated analytics for leadership
Payments, lending, digital services
- Customer onboarding and KYC document checks
- Support agents in apps and messengers
- Signals of suspicious operations for analysts
- Product and channel analytics
Warehouses, carriers, distribution centers
- Stock and movement control
- Receiving and shipping documents read automatically
- Video monitoring of zones and safety
- Capacity and delivery planning
Universities, schools, training centers
- Admissions and student requests in several languages
- A knowledge base of programs and regulations
- AI assistants for teachers and methodologists
- Analytics for university and ministry leadership
What makes a good pilot
The signs we look for during the diagnostic. The more of them a process has, the faster the pilot pays back.
It happens every day
Dozens or hundreds of similar operations a week.
The data already exists
Even if it's spread across systems and spreadsheets.
It's clear how to count
Hours, days, errors, money: something you can compare before and after.
Someone owns it
A person this process frustrates, who wants it changed.
Mistakes are recoverable
The first process shouldn't be one where a single error is expensive.
The result shows
Leadership should be able to see the pilot's impact and decide on scaling.
Don't see your problem here?
Describe it in your request. An engineer will tell you whether it's solvable and where to start.