Too much manual work
The team fills spreadsheets, reconciles data, forwards reports and answers the same questions. Manual work eats more hours than growth.
AI isn't for the trend. It's for the places where reducing manual load, killing routine and speeding up processes is how the business survives.
The team fills spreadsheets, reconciles data, forwards reports and answers the same questions. Manual work eats more hours than growth.
Every department looks at its own system. The real picture gets assembled by hand at month-end — decisions are made on stale data.
CRM, ERP, Telegram live in isolation. Staff move data by hand, and decisions happen on different versions of the truth.
Every new volume = new hire. The system doesn't scale on its own — it's inflated with people. Growth plans hit the labour market.
Clients in CRM, finances in ERP, leads in email, files in the cloud. No one — neither executive nor team — sees the whole picture.
There were pilots with ChatGPT, but nothing went to production. No methodology, no owner for the metric, no view on ROI.
Scroll — watch scattered requests and tools pull into an orchestrated network.
We work with companies where operational efficiency is a question of survival, not of fashion.
Companies with revenue between $3M and $60M, where every inefficiency already shows up in the numbers and hits the margin directly.
Multi-location business where standards, data and processes have to be held across 20, 60, 180 locations at once — scale meets manageability.
Production, logistics, retail, client service — where processes are numerous and wired across dozens of systems and roles.
The goal: 2—3× in a year without proportionally expanding headcount. It's math, not a slogan — growth without AI hits the hiring ceiling.
Departments work in isolation. Data drowns in chats and spreadsheets. Decisions run on intuition; communication goes through managers.
Artificial intelligence reads the context, connects systems and becomes the central nervous system of the business.
Links are clean, processes pass through agents, decisions happen in seconds — not weeks.
Operating cost drops; speed and quality rise. Growth becomes a consequence, not a target.
→We put the agent where it actually moves the process and changes the outcome.
→Success metrics and expected ROI are locked before development starts.
→We ship into production: real integration, monitoring, SLA.
→After launch we hand over the stack, access and full control of the solution.
Short cycles: we ship an effect you can measure, then embed the solution into your working context and grow it from there.
We dig into your processes, data and current infrastructure. We pinpoint where AI will actually take manual load off, speed things up and deliver a measurable business impact.
We ship the first working solution for a specific task. Straight inside your process and on your data — so the effect is validated in real work, fast.
We integrate into your existing perimeter: CRM, ERP, Telegram, email and internal services. Access set up, team trained, system goes into live operation.
We watch quality, retrain models on new data and extend the solution to new scenarios. The system stays stable and grows as your business does.
Tap a question — the agent answers. This isn't an LLM: just the facts about how we actually work.
↳ What does it cost?
Three ways to solve a task with AI. Pick honestly — we'll tell you when you don't need us.
Open-ended — usually 6+ months of experiments with no predictable outcome.
3—6 months hiring + 3 months to first launch.
6 weeks — written into the contract.
Thousands of $ on API + 2—4 months of your team's time.
~$8k/mo × 6 months = ~$48k before the first launch.
Fixed price, agreed before kick-off. Doesn't move.
On you. ChatGPT doesn't own a business outcome.
On you — you hired, you manage, you own the result.
On us. Metric doesn't move — we refund the fixed fee.
You maintain and grow it yourself.
Team on payroll — fixed cost regardless of load.
Separate SLA contract with clear monthly fee.
Yours from day one.
Yours — you paid for the salaries.
Yours from day one. Stack handover written into the contract.
Only the API provider (OpenAI/Anthropic).
Lock-in to people — turnover = project risk.
None. After launch, work with anyone — no rebuild.
Simple task, you have an AI engineer in-house, fine with 3—6 months of experiments and undefined ROI.
AI is a 2+ year strategy, you need a permanent team, OK with the fixed payroll cost.
You need a predictable 6-week launch: fixed price, money-back guarantee, full stack handover.
Snapshot of the network. Red nodes are agents; white are systems and data they're wired into.
For a 180-location network we deployed a system of 12 AI agents covering sales, support, procurement and marketing — to cut back-office load and bind the processes into a single, manageable model.
−68%
12

−68%
12
6 weeks
3× ROI
We built and shipped an AI system that unifies market and competitor research, audience segmentation, offer crafting, landing-page and creative generation, and performance launches across any acquisition channel. One person runs ×10 the campaigns without losing the brand voice.
×10
−70%

×10
−70%
4 weeks
+240%
We deployed an AI system that unified financial data from multiple sources, automated management reporting and accelerated CFO decision-making. Instead of stitching reports by hand, the team got a single analytical loop with dashboards, deviations and ready-made conclusions.
3×
−82%

3×
−82%
7
5 weeks
We work with companies that want to grow faster, make sharper decisions, and rely on data rather than guesswork.
Мария Гончарова
«We didn't fire anyone. We just stopped hiring. Back office didn't grow for two quarters — and revenue went up 40%.»
Хенрик Йоханссон
«We stopped losing hours on manual asset assembly and approvals. Agents put a campaign together in minutes — we review and ship.»
Юки Танака
«The weekly report now takes half an hour. The board decides on fresh data, not week-old numbers.»
AI doesn't replace people. It frees their time for the work that moves the business. Growth without proportional hiring isn't a slogan — it's a measurable gap in the numbers.
of manual operations after AI systems ship
faster decisions and reports on key processes
average time from contract to first launch
new hires needed to double operational volume
The team stops drowning in routine and goes back to what actually moves the business: growth, product and customers — not endlessly copying data from one spreadsheet to another.
Not abstract promises — concrete shifts visible in the numbers and in how the team works, 4—8 weeks after launch.
The team stops copying data, reconciling sheets and repeating the same operations. Routine goes to agents; people move to hard problems.
Leads, documents, communications flow through the system in seconds, not hours. Clients wait less; the business reacts faster.
One panel across every department. The executive sees where the money is, where the delays are and where the risks are — in real time.
Volumes double — the team stays the same. Hiring turns from a required function of growth into a deliberate choice.
Processes built around AI replicate to new locations, markets and teams without a rebuild — the infrastructure is ready to grow.
Launch in 1—6 weeks, a log on every agent action. You know what you get and at what cost.

Adjust the parameters — we'll show how much capacity an AI system frees up if it takes part of the manual load. This is an estimate based on real projects.

Depends on the task. A small automation — tens of thousands of dollars. A linked set of processes or a custom system with AI inside — into the hundreds. We give the exact range after a free review: we look at your processes, data and systems and ship a quote before the first line of code. If the project doesn't pay back — we say so honestly.
A 30-minute diagnostic, no slide decks, no vague talk. We map your process, estimate the impact, timeline, and the best way to launch.