We deploy it in 6–10 weeks. What to do with the freed capacity — cut payroll or take on more work with the same team — is your call.
The role is unpacked into the system and verified 5 out of 5. The load is 20–40 minutes a day per person, with no pause to the business.
Management accounting, the head-of-sales role on the client base, tender monitoring, moving the accounting system to a secured server — done by AI.
Contractors quoted $250/month or $25–40 an hour just to maintain the books. The function is covered inside the perimeter — nothing had to be bought.
30–90 minutes · AI maturity map and money priorities · before any payment
Run the margin numbers on your own inputs
“I don’t know where the money is. There’s revenue — I don’t see profit. Am I living on credit?”
“Accounting profit is not real profit.” The books show taxes, not where the business actually earns.
The business lives in your head: “this information exists only in me.” Delegating would take a year.
Several business lines don’t fit into your day: dive into one — the others sag.
Company data is scattered across employees’ and contractors’ personal accounts. One departure — and spreadsheets, scripts, access leave with them.
“We tried AI” = everyone has their own ChatGPT in a personal account: no accumulated context, toy answers, zero security.
Decisions are made by gut feel — because getting the number takes longer than guessing.
Not chatbots — a single working environment: the whole team inside a secured perimeter, data owned by the company, executives’ roles unpacked into the system’s memory layers. To “where is the money?” you get a trustworthy answer in chat — without opening a spreadsheet.
The anchor pilot — trustworthy management-accounting numbers in ~2 weeks.
Start with a 30-minute AI diagnostics. You leave with a map of 5–8 implementation points: the process, its owner, hours/money spent today, the solution, payback period and impact on margin. Free and no commitment.
6–10 weeks to checklist sign-off · three tranches 30 / 40 / 30, each on stage acceptance
Until September 30, 2026 we take a limited number of rollouts: the format requires 15–20 joint sessions of 1–2.5 hours per client. After that — a queue for the next window.
AI diagnostics, 30–90 minutes: a maturity map, money priorities, a draft roadmap. Before any payment.
Get your free AI diagnosticsRun the numbers on your own inputs →Your whole executive team — function leaders headed by the CEO and the owner — works inside one AI ecosystem on corporate plans. Everything else — CRM, ERP, telephony, any agents — becomes data sources. The company is managed through AI: some processes it runs end-to-end, some it prepares and a human confirms, critical decisions stay with people.
No tariff grids or “service packages” — because there is one result: a company managed through AI.
Confirmed sectors: transportation (5 cities), energy/tenders, real estate development, fintech, e-commerce. Names under NDA — cases shared on a call.
we start here: you personally ×5 faster within the first 2 weeks
SSO, access revoked in one action. All data, access and scripts owned by the company: reclaimed from contractors and personal accounts
trustworthy management accounting that answers in chat: profit by line, payment calendar, cash flow — checked with the accountant
every business function is a project in one shared context; the role is verified 5 out of 5, a triple of automations driven to 100%
accounting, CRM, telephony, banks — no manual exports
15–20 joint sessions of 1–2.5h without stepping away from operations; between sessions the team works on its own
$1,800 a year standalone — included for you: practice, agent templates, teardowns
plus our engineer: the system evolves monthly, the context is exported into your own storage
The ceiling is fixed in the contract after the free diagnostics and is not revisited afterwards.
A team of five executives runs the whole stack at roughly $200–600 a month. Licences are issued to your company and stay yours — this is not our subscription that can be switched off.
Google Workspace public pricingNeed something that doesn’t exist off the shelf — AI-CRM, voice agents, internal SaaS? We build custom inside the same ecosystem; the estimate comes from the diagnostics process map.
A holding with 5+ legal entities? Same methodology, a custom AI core for your perimeter and an equity option in the industry product — discussed at a strategy session with the owner.
Anton delivered every case below by hand — no AI, through systematic management, personal involvement and months of work. If companies grew 3–10× in 1–5 years without AI, imagine what we do now — when AI takes over 80% of the routine.
Swipe the cards sideways
Built the management vertical from scratch: org chart, KPIs, regular management cadence. The product funnel was rebuilt around customer-retention economics.
Got the owner out of day-to-day operations through delegation and hiring strong deputies. Built a system that runs without his daily involvement.
Deployed 20 management tools — from financial planning to lead-gen automation. Set up regular owner-level reporting.
A large-scale manufacturing transformation: from a family-run plant to a team-managed business with a clear org structure and a strong finance function.
Built the franchise model: from brand book to operating standards. Every one of the 8 franchisees became profitable within their first year.
Rapid surgery: a 120-person team with no clear org structure → a managed organization with KPIs and regular management. Profit impact within a quarter.
Because showing only the finale is showing an edited reality. The market is flooded with “saved 40%” by week three. Here is what actually happens:
The most valuable thing the system finds in the first month isn’t automations. It’s the errors and leaks in your data.
Honestly, what isn’t closed yet: freeing the owner’s time requires his decision on prioritizing the business lines — that’s ahead. The implementation continues.
Every implementation is documented session by session: tracker, acceptance criteria, statuses. We show clients a work journal, not presentations.
60+ business scaling projects before the AI era. The methodology was proven by hand — AI made it many times faster.
All projects are under NDA — we disclose sector and scale; details and result figures are shared on a call under an agreement. This is real demand at the holding level, not promises.
Competitors promise “you’ll save 40%.” We don’t know your numbers — and we won’t invent them. On the diagnostics call we’ll count on your data. Meanwhile — formulas you can estimate with yourself:
We assume a machine covers half the routine — deliberately conservative, and not a promise: the exact share comes from diagnostics on your live processes.
Counts ONLY the payroll saving — the most conservative part; the execution gain sits on top and is not included. And the saving becomes money only once those hours genuinely stop being paid for.
1,645 h a year go into work that moves the business
We do not price that gain: it equals the margin of whatever you put the freed time on, and only you know that.
Implementation — from 3,900 USDT
Get the exact figure on the diagnostics →The goal is not to automate everything and cut as many people as possible. The goal is to reasonably automate the tasks where a machine performs as well as or better than a human, and to let people rebuild how they work through mastering AI — doing more, and better. For those not ready for that, the only way to raise business efficiency is replacement.
This is an order-of-magnitude estimate, not an offer. On the pilot we reconcile it with your real data and lock a range for the project. Honest caveat: cut headcount only AFTER an automation works, not before.
Market context — per McKinsey (2025, n=1993): cost-reduction effects are reported most often in engineering and manufacturing (54–56% of companies), revenue growth — in marketing & sales (67%) and finance (65%). That’s the frequency of effect in the industry, not a guarantee of magnitude for you — yours will be shown by the diagnostics.
Everything the market sells as ten different contractors — inside one ecosystem: one screen, one shared company context. Some roles AI runs fully, some it prepares and a human confirms.
Answers business questions from your data. Builds reports, catches anomalies. Up to 90% of decisions backed by numbers.
Reviews contracts in 5 minutes instead of 2 hours. Drafts claims and standard replies 24/7. Human lawyer routine −60%.
Brand-voice content, ad campaigns across your channels, A/B tests. Campaign launches 3–5× faster.
Qualifies leads 24/7, moves them down the funnel, prepares call briefs. Meeting conversion +30–50%.
Real-time financial model, P&L and cash flow, scenario analysis. Reports in an hour instead of 2–3 days.
Source documents into your accounting system in 30 seconds each. Reconciliations, tax filing drafts. Accounting routine −50%.
Calendar, briefs, meeting notes, research, team follow-ups. Gives the owner back 15–25 hours a week.
Tenders and B2B / B2C / B2B2C sales, content generated and deployed, finance of any complexity — multi-account, multi-bank, several business lines — hiring, the legal function, strategy on live data, operations management with a supervisor: task setting → prioritization → execution → control. And any expert for any task — drawing their own conclusions, producing their own dashboards.
The bottom line for the owner: the team gets more done, processes move faster, tasks execute faster — and all of it is visible on one screen.
revenue up, costs down — a two-sided effect on the P&L
processes mapped, roles unpacked, chaos over
knowledge accumulates in the company and survives any departure
the owner sees everything himself — no intermediaries, no “take my word”
payments confirmed only by a human; data and access stay in the company perimeter
We don’t plug in chatbots. We rebuild the decision-making architecture: where the data lives, who owns access, how the company answers the owner’s questions. AI becomes the fabric of the business, not an add-on over chaos.
Engineering, architecture and operations of industrial-grade AI systems for mid-market business.
Not strategy on paper: the anchor pilot in ~2 weeks, the full corporate perimeter in 6–10 weeks, monthly support after.
| Criterion | DNAIengineering | ChatGPT on personal accounts | Off-the-shelf CRM AI modules | Homegrown, duct-taped |
|---|---|---|---|---|
| Where context lives | In the corporate org — owned by the company | In a personal account — leaves with the employee | Inside the vendor’s module | In the developer’s head and code |
| Hallucinations | Entity-grounded: answers from your documents with citations | Generic answers, invents facts | Template scenarios | However you set it up — usually unchecked |
| Link to money | KPIs on the P&L — every automation in revenue/costs | None, “an impression of AI” | Partly — module metrics | Not out of the box |
| Time to result | 6 weeks by checklist with sign-off | Instant, but shallow | Weeks for license and setup | Months, unpredictable |
| Who maintains it | In-house operator, trained end-to-end | The employee themselves | The vendor, for a subscription | Only the code’s author |
Not “time savings” in the abstract. Two concrete sides of the P&L — and the owner’s time back for what can’t be delegated.
People stay at the center — AI amplifies rather than replaces. Freed time goes into client value, not new routine.
A year from now anyone will “build an app” — by voice, in a minute. The value will stay in what cannot be copied. These three assets are where we point AI:
Your data, your niche, your processes. AI doesn’t create it — it amplifies it. The deeper the context, the smarter the AI, and the gap with competitors grows daily.
A product is copied in a day. Client trust takes years and cannot be copied. We point AI at deepening the bond with those who already pay you.
Every element of the system must drive revenue up or costs down. If it doesn’t — it goes. Automation for automation’s sake is false motion.
Behind this — two foundations: 15 years of management systematization (strategic and operational) and an engineering school of working with neural networks — prompt engineering, loop engineering (self-check cycles), graph engineering (the structure of links and memory), context engineering. All of it exists so the AI answers accurately, not plausibly. It’s included in the product — you don’t need to master any of it.
We build on the Claude / Claude Code stack — the only one today with the projects, memory layers and team perimeter our architecture requires. When a client wanted to test an alternative three times cheaper — the “don’t switch” verdict came from the alternative model itself. We hand the system to your Keeper: you get autonomy, not a tie to a contractor.
A short list of what not to come to us for.
88% of companies already use AI. Only 39% see any effect on profit — and for most of them it is under 5%.
McKinsey, The State of AI, November 2025 — 1,993 respondents across 105 countries
The nine typical rollout mistakes all grow from one root: AI gets deployed as a toy add-on instead of the operating system of the business. What follows is the opposite.
This is not a frame on a slide: behind the seven steps sits a working checklist of 46 tasks, each with a written acceptance criterion. At the core — the same approaches global consulting applies in the Fortune 500 (Triple-Lens, AI maturity map), plus 15 years of management systematization and an engineering school of working with neural networks. Open a step: inside — what happens and what you accept. The path looks simple because we’ve already walked the minefield.
Questions, documents, analytics, tasks — all through one perimeter. Every workaround “just quickly the old way” rolls the team’s habit back.
Every task has a written acceptance criterion. Self-assessment does not count as sign-off.
Revenue up and costs down. If a process leads elsewhere — we pivot to another solution, and that is a normal move, not a failure.
Summary, results, decisions, blockers, owners, deadline. One session — one line in the log.
We build the solution in parallel with raising source quality, but the business owns the source.
Answer quality depends on the model directly: we track releases and upgrade.
Pace: a team of 4–5 — 6–10 weeks (~15–20 joint sessions of 1–2.5h); a team of 2–3 — 4–6 weeks. Training happens inside the sessions, without stepping away from operations: daily solo work is 20–40 minutes per executive.
The helix on these screens is real B-DNA: 10.5 base pairs per turn, major and minor grooves, A–T and G–C pairs. And its sequence isn’t random: ATG (the start codon) + GAT·AAT·GCT·ATT — the codons of amino acids D·N·A·I. The geometry follows crystallographic B-form data (Arnott–Hukins fibre diffraction and the Drew–Dickinson dodecamer): phosphorus at a 8.91 Å radius, 3.38 Å rise, −11.4° propeller twist. We write AI into the DNA of a business the way nature writes proteins: by exact rules.
A manufacturing-and-trade company, 3 business lines. These aren’t curated success stories — they’re working moments from the project tracker.
A manager exported the client base from the CRM and gave AI the role of head of sales. In his words: “It writes me possible actions and likely outcomes. It looks at everyone left unattended: digs up abandoned, postponed, stuck clients with no communication at all.” The first run surfaced dozens of clients with broken-off communication.
AI took a management role no human was filling.
An employee with no technical background built a dashboard in one working day: all clients, auto-refresh, clickable links into CRM cards, a report per manager.
The team didn’t “adopt a tool” — the team started building. The competence stayed inside the company.
The owner: “With its help I created a new server — it did practically everything for me. I exported the accounting system, moved it over — it tuned everything inside, checked all the numbers. The accountant checked — everything matched completely. Today we fully moved from the old server that sat on a home computer into the cloud with properly built security.”
AI doesn’t “answer questions” — it delivers an infrastructure project you’d normally buy from an integrator.
The owner, week three: “We’re relaxing: nothing to enter, no spreadsheets to open. I don’t even know where my spreadsheets are. I say: log it — it sorts it all somewhere, creates the tables, but I don’t go into them. I just watch it work.”
The owner’s target state: operating in meanings, not files.
The client wanted to test an alternative — a model three times cheaper. We tested it honestly, inside his own perimeter. The “don’t switch” verdict was delivered by the alternative model itself: no projects, no memory layers, no team perimeter.
We don’t lock clients into a stack — the architecture proves itself.
In July ~70% of VPN protocols went down in the region. The perimeter already had its own WireGuard on a separate server and a backup route — work was restored the same day.
“We switch over in hours, not days” — not a promise, a lived fact.
Swipe sideways →
60+ business scaling cases. Strategy, diagnostics, AI architecture.
Case videos →
AI systems architect, 15+ years in IT. Owns architecture, AI solution delivery and scaling of productized offerings.
Back-end and front-end development, AI model integrations. 15+ years in the field.
We don’t sell on the call. You get a transparent assessment: where the bottlenecks are, which ones AI closes first, and what that returns in $/hour of the owner’s time.
Fill in the form below. Within an hour we message you in Telegram with confirmation and questions.
3–5 questions about your business and current bottlenecks, so the call goes straight to substance.
Anton + AI dissect your business in real time. You watch AI look at your operations and find the levers.
A PDF with priorities: which 2–3 nodes deliver the most, an architecture diagram, a pilot estimate, the return in $/hour of the owner’s time.
Not an abstract 80-slide strategy. A concrete document you can start implementing from — with us or on your own.
The map is yours even if we don’t continue together.
If the map makes you want to work together — we discuss a pilot. If not — you keep the map and implement it yourself. It’s free either way.
30–90 minutes. Free. Anton runs the diagnostics personally, with AI in real time — slots are limited, requests are handled in order. You leave with an AI business map and a growth plan — whether or not we end up working together.
“I say: log it — it sorts it all. I don’t go into spreadsheets anymore.”
During working hours we reply within an hour, otherwise the next working morning. No selling on the call — we take your business apart with AI and hand you the map. The map is yours even if we don’t continue.
Not a one-off bonus on the first deal. Ten percent of everything that client pays us, for as long as they stay with us.
An owner of a company with revenue from $1M a year. They have a team and an ambition to grow and put things in order. They make decisions themselves. If someone like that is in your contacts, you have something to bring us. Our deal starts at 3,900 USDT — count your ten percent.
We do not take everyone. If the free diagnostics shows the economics do not work at their scale, we tell them so directly. That client stays reserved for you — they will come back when they grow into it.
Before they reach out to us. One message is enough — name, company, what they do.
They stay yours even if they come to us on their own without mentioning you. The reservation holds for 90 days up to the call, and extends for the whole engagement once the call happens.
Whichever fits: this site, the video cases or the channel. You do not need to understand the technology — they need to understand the point.
Through the same chat. From there we sell and deliver; nothing else is required from you.
Anton’s personal contact. Name the client and I will reserve them for you.