Every leader gets more done
The role is unpacked into the system and verified 5/5. Load — 20–40 minutes a day per person, without stopping the business.
For owners with a top-management team
A professional operating systemfor business, people, and agents
Everything in one window. Your top team works inside it and gets more done — and the system fully closes part of the work on its own.
We deploy it in 4–8 weeks. What you do with the freed capacity — reduce payroll or take on more with the same team — is your call.
The system does most of the work itself
Management accounting, a sales-lead role on the client base, tender monitoring, moving 1C to a secured server — powered by AI.
Savings on contractors, outsourcing, and outside expertise
Contractors asked 20,000 ₽/mo or 2,000–3,000 ₽/hour for accounting support. The function is closed inside the perimeter — no need to buy it out.
If you own the company — this is probably you right now:
“I don’t know where the money is. There’s revenue — I don’t see profit. Am I living on credit?”
“Accounting profit isn’t real profit.” Accounting shows taxes, not where the business actually earns.
The business lives in your head: “this information is only in me and with me.” Delegating would take a year.
Several directions don’t fit in a day: you dive into one — the others sag.
Company data is scattered across personal accounts of employees and contractors. Someone leaves — spreadsheets, scripts, and access leave with them.
“We tried neural nets” = everyone has their own ChatGPT on a personal account: context doesn’t accumulate, answers are toy-like, security is zero.
Decisions are made by feel — because pulling a number takes longer than guessing.
Our answer
We take the business out of your head and your team’s heads into the company’s AI contour: accounting, clients, decisions.
Not chatbots — a single working environment: the whole team in a protected contour, data belongs to the company, leadership roles unpacked into the system’s memory layers. On “where is the money?” you get a reliable answer in chat — without opening spreadsheets.
What we lock in the contract
- each stage is delivered against a verifiable artifact — not a self-report
- fund isolation: AI prepares a payment, but does not execute it — any movement of money is confirmed only by an authorized person
- the pilot shows effect on your data before full payment
- effect is measured in your P&L, not in our slides
Anchor pilot — reliable figures from your management accounts in ~2 weeks.
Not ready for a pilot
Start with a 30-minute AI diagnostic. First we isolate 20 improvements that deliver 80% of the yield. From those twenty we take another 20% — 1–3 implementation points: process, owner, hours/rubles now, solution, payback period, and impact on margin. Free, no obligation.
840+
growth projects before AI
a methodology that AI sped up many times over
Everything from A to Z — with AI to help humans.
- Exam preponline school · Russia
- Architecture studioSF · +400% requests
- Motor Cars LA$25M → $40M
- Apotek Hjärtat400M → 2B+ ₽
- Top-5 audit, RussiaNDA · NEURO OS
- Investment-lending · SkolkovoNDA · platform
Deploying an AI ecosystem in the business
The whole top team — function leads with the CEO and owner — works in one AI ecosystem on corporate plans. CRM, 1C, telephony, any agents become data sources. The company is run through AI: some processes it runs fully, some it prepares — a human confirms.
Sectors: consulting, development, transport, energy/tenders, fintech, e-commerce. Names under NDA — cases on the call.
Product price
from 350,000 ₽ · 3,900 USDT
4–8 weeks to checklist acceptance · two tranches 70 / 30, each on stage acceptance · contract in the RF, invoice in rubles, dollars, or euros.
Until 30 September 2026 we take a limited number of deployments: 5–10 sessions of 45 minutes per client.
First step is free
AI diagnostic, 30–90 minutes: maturity map, money priorities, draft roadmap. Before any payment.
What’s included — all of it
- Owner’s personal AI contour
we start here: you personally ×5 faster in the first 2 weeks
- Protected corporate contour
SSO, revoke access in one action. All data, access, and scripts belong to the company
- Anchor pilot
reliable management accounts that answer in chat: profit by line, payment calendar, cash flow — reconciled with the accountant
- AI staff for each key leader
each function is a project in shared context; the role is verified 5/5, a trio of automations taken to 100%
- Data channels
accounting, CRM, telephony, banks — no manual exports
- Team training
5–10 sessions of 45 minutes without stopping production
- AI Club
practice, agent templates, reviews — included. Open the club →
- A Keeper inside your team
plus our engineer: the system grows monthly, context is exported to your store
What the price depends on
- Leaders in the contour: team of 2–3 — 4–6 weeks, 4–5 — 4–8
- Number and state of integrations
- Data contour: zero-retention cloud or a local model in the perimeter
- Whether you need systems that don’t exist ready-made
The ceiling is fixed in the contract after the free diagnostic and is not revised.
What you pay later — besides the deployment
- ChatGPT or Claude — $30–100 per user / month; our subscription payment store will help you get it billed
- Google Workspace — $7–22 per user / month
- VPN — your own, on your server: from $10 / month for the whole company
A team of five leaders — about $200–600 / month for the stack. Licenses are issued to your company.
Need something that doesn’t exist ready-made? We build custom inside the same ecosystem. Holding of 5+ legal entities? Same methodology, custom AI core — discussed in a strategy session with the owner.
AI did not replace the methodology. AI sped it up many times over.
If without AI companies grew 3–10× in 1–5 years, imagine what we do now — when AI takes 80% of the routine.
→ Scroll the cards sideways · all use cases
Online school
~1M / mo
- ~1M touches / mo
- Shipped: outreach + follow-up of the audience
- Online EGE school, Russia
How we did it
Approach
Acquisition for an online EGE prep school: competitor customers and their visitors, then follow-up. Short path: message → consult → plan.
What we did
- Built the audience from competitor clients and visitors of their communities and sites
- Ran outreach with the school’s offer into a consult
- Followed up non-responders until they replied or opted out
- Scale: on the order of 1M touches per month
Architecture studio
requests +400%
- Requests +400%
- Shipped: agent quote, deck, priced post
- Public portal → filing straight into work
How we did it
Approach
A fully optimized sales system. Residents file on a government site about a neighbor — roof or house not being repaired. The filing is captured and lands at the studio. An automatic agent writes the person named in the complaint and builds a personalized quote.
What we did
- Capture filings from the public portal: roof and house repair complaints
- The agent builds a quote and a personalized presentation: services and prices that fit that lead
- News of the filing goes to the person named: “A complaint was filed about you. We can offer reconstruction at a low price”
- The sales loop runs without manual lead hunting
Motor Cars LA
in 2.5 years
- Revenue $25M, +30%/year
- Dividends +40% · shipped: 20 tools
- Client returned · AI, 2026
How we did it
Approach
Installed 20 management tools — from financial planning to lead-gen automation. Regular owner reporting.
What we did
- Financial planning: P&L, cashflow, quarterly forecasts
- KPI system across sales and service
- Automated CRM funnel and scripts
- Dividend strategy — a steady 40% rise in owner payouts
Apotek Hjärtat
in 3 years
- Revenue 400M → 2B+ ₽ (×5)
- Margin 7% → 9%
- Shipped: dashboard of client and order flows
How we did it
Approach
We built the process on our system and dashboard: every client flow and every order flow on one screen. Objections are handled in the process; reviews are collected from customers who did not leave them and published on the site — that is how the business is optimized.
What we did
- Dashboard to control client flows and order flows
- Objection-handling process at each step of the funnel
- Collected reviews from customers who had not left one
- Published those reviews on the site and raised business optimization that way
Audit firm
NDA
- Top-5 audit firm in Russia
- Shipped: BPMN Flow, knowledge base, NEURO OS
- Human review on control steps
How we did it
Approach
A top-5 audit firm in the Russian Federation, under NDA. Automation of the audit engagement and the auditor’s report: document intake, reading the accounting policy, validating and adapting working-paper templates. Processes mapped in BPMN Flow with BPMN annotation. Launch points — pinpoint automation with the pair method; human review where a check is required. NEURO OS embedded in the firm’s system.
What we did
- Collected documentation and a project knowledge base
- Auto-read of the accounting policy; validation and adaptation of working-paper templates
- BPMN Flow + annotations, pair-method launch points, human review on control steps
- NEURO OS in the company system: the opinion is assembled from the client’s source documents, the firm’s materials, and extra information — the flow is faster
Crypto exchange
in 3.5 mo
- Net profit $4M → $9M/mo (×2.5)
- Shipped: org, KPI, roles · 120 people
- Recruiting and marketing from zero in 3.5 mo
How we did it
Approach
Fast surgery: 120 people without a clear org → a managed organization with KPI and a weekly rhythm. Profit result in a quarter.
What we did
- Org chart of 120 people by function + hierarchy
- KPI system for every role
- Recruiting built from zero — roles filled in 2 weeks
- Marketing rebuilt from chaotic to cohort-based
Investment-lending platform
NDA
- Platform: invest in listed companies + loans to business
- Shipped: credit rating + source-doc intake
- Applicant's form data → auto-lookup in EGRUL and open registries
How we did it
Approach
An investment-lending company — Skolkovo resident. Dual model: investors put money into companies listed on the platform; businesses that need a loan get it through the same platform. We automated the credit rating and the intake of source documents from the borrower. Name and figures under NDA.
What we did
- Application loop: the borrower's source documents go straight into processing
- Auto-assembled credit rating for the lending decision
- Based on the applicant's form data, entity information is automatically pulled from EGRUL and other open registries
- Platform details and scale — on the call under NDA
Live AI cases
We publish cases from mid-deployment.
Showing only the finale is showing edited reality. The most expensive thing the system finds in month one is not automations. Errors and leaks in your data.
- Management accounts showed profit that wasn’t there — millions of rubles in one month. The owner saw a real P&L for the first time.
- Sums frozen in warranty retentions — about a third of drivers’ monthly payroll.
- Hiring funnel leaks: from thousands of replies, only a few percent reached a vehicle.
- In 7 sessions (~3.5 weeks): management accounts reconciled; 1C moved to a protected server by AI; client base turned into a dashboard with AI as head of sales; tender monitoring live.
- Owner and manager roles verified 5/5. A client employee built the dashboard in one day.
- Contractors asked 20,000 ₽/mo for accounting — the function is now inside the contour.
Honest leftover: freeing the owner’s time still needs their call on priorities — that’s ahead.
Every deployment is logged session by session: tracker, acceptance criteria, statuses.
AI projects in progress now
What we are building right now.
All projects under NDA — we share sector and scale, details on the call.
Project-engineering holding
- 23 years of expertise into a corporate AI core
- Norms, profitability calculators
- 6 stages with checklist acceptance
Financial holding
- Treasury visibility: 30 entities · 100 accounts · 30 currencies · 600M+ turnover
- Real-time consolidation instead of manual statements
Development holding
- Claude Enterprise into leadership work
- Role context, corporate organization
- 6 weeks, internal operator turnkey
Fintech
- AI into leadership and operations
- Human-in-the-loop for critical decisions
Knowledge-base memory layer
- Building and optimizing the company knowledge-base memory layer
- Agency context: clients, campaigns, playbooks
- The memory layer stores knowledge and feeds it back into work
Landing and mockup automation
- Building automation for landings and Figma mockups — all complexity levels
- Brand regulations and special rules inside the generation loop
- Self-learning: each mockup makes the next ones stronger
We don’t draw ROI. We count on your numbers.
Competitors promise “save 40%”. We don’t know your numbers — and we won’t invent them. We assume the machine covers half the routine — conservative, not a promise.
5 moPayback on the deployment (from 350,000 ₽) — payroll savings only
871k ₽Payroll saved per year if those hours stop being paid
+15.9%1,645 h/year go into work that moves the business
How to read the effect
The goal is not to automate everything and fire the most people. Cut headcount only after working automation, not before.
McKinsey 2025 (n=1993): cost-down most often in engineering and production (54–56%), revenue-up in marketing and sales (67%). Read the frame in the AI Guide for Companies.
You no longer need to buy bots, agents, and integrators
Everything the market sells as ten contractors sits in one ecosystem: one screen, one shared company context.
The AI layer = growth in contribution margin
↑ Revenue
Content that reads demand. Neuro-sellers 24/7. Retention and found revenue leaks. Decisions an order of magnitude faster.
↓ Costs
Routine goes to agents. Dashboards show where it eats payroll. Overpay removal. Month-close in hours, not weeks.
Data sources
Accounting · CRM · Telephony · Banks · Mail and disk
One company window
Finance · Sales · People · Legal · Ops · dashboard in preview
Runs itself
Agents · Schedules · Routines
Finance
Answers business questions from data. Consolidates reports, catches anomalies. Up to 90% of decisions covered by numbers.
AI lawyer
Reviews contracts in 5 minutes instead of 2 hours. Drafts claims, typical replies 24/7. Live-lawyer routine −60%.
Content and campaigns
Brand voice, ads, A/B tests. Campaign launch 3–5× faster.
Sales
Qualifies leads 24/7, runs the funnel, prepares call cards. Meeting conversion +30–50%.
Model and 1C
P&L and cash-flow in real time. Source docs into 1C in 30 seconds. Accounting routine −50%.
Orchestration
Calendar, briefs, minutes, team control. Returns 15–25 hours a week.
For the owner: the team gets more done, processes move faster — and it’s all visible on one screen.
Five things the owner always gets
- Margin ↑
revenue up, costs down — both sides of the P&L
- Systematization
processes mapped, roles unpacked, chaos over
- Context forever
knowledge accumulates in the company and survives any resignation
- Full control
the owner sees everything — no intermediaries, no “trust me”
- Fund isolation
only a human confirms payments; data and access stay in the company contour
Automation is what everyone can do. Architecture is not.
We don’t plug in chatbots. We rebuild the decision architecture: where data lives, who owns access, how the company answers the owner. Anchor pilot in ~2 weeks, full corporate contour in 4–8 weeks.
| Criterion | Pattern Automation | ChatGPT on personal accounts | Ready AI CRM modules | Homegrown |
|---|---|---|---|---|
| Where context lives | In the corporate org — belongs to the company | In a personal account — leaves with the employee | Inside the vendor module | In the developer’s head and code |
| Hallucinations | Answers from your documents with citations | Generic answers, invents facts | Template scenarios | As configured — usually unchecked |
| Tied to money | KPI on P&L | “Impression of AI” | Module metrics | Not out of the box |
| Time to result | 6 weeks on checklists with acceptance | Immediate, but shallow | Weeks for a license | Months, unpredictable |
| Who supports it | Internal operator trained | The employee | Vendor for a fee | Only the author of the code |
| Who owns it | Data, access, and scripts are yours. Export as ordinary files | OpenAI / the employee | The CRM vendor | The freelancer, while they pick up |
| Payments | AI prepares the payment; only an authorized person sends it | No contour | Whatever the module allows | As coded — often no isolation |
| Acceptance | A written artifact per stage. Self-report doesn’t count | None | License = “done” | “Seems to work” |
| Access | SSO. Someone leaves = revoke every access in one action | Personal login, password in chat | Vendor roles | Keys in the repo |
| Scope | The whole top team in one contour: books, CRM, telephony, banks as sources | One person, one chat | One team, one module | One enthusiast |
| After go-live | A Keeper inside your team + monthly evolution | Employee leaves — context gone | Subscription, their roadmap | The author is on another job |
| What you buy | Only the pieces your processes need — LEGO, not AI for its own sake | A chat subscription | A pack of “same as everyone” scenarios | Dev hours without a system |
Three assets you cannot copy
Context
Your data, niche, processes. Deeper context means smarter AI — the gap grows every day.
Relationships
A product is copied in a day. Trust in the client base takes years. We point AI at deepening ties with people who already pay.
Traffic and money
Every system element must raise revenue or cut cost. If it doesn’t — we throw it out.
Why not “I’ll do it myself with ChatGPT”
A personal account is not a system: no ownership contour, no memory layers, no acceptance. In 3 months — a scatter of chats and zero accumulated context.
Why not “I’ll hire a bot integrator”
You get a support-chat bot. Decision DNA doesn’t change, data stays someone else’s, vendor lock grows.
Why not “big consulting”
Same methodology — maturity map, process redesign — but 6 months and tens of millions. We do it in weeks because AI itself is our diagnostic tool.
What we do NOT do
- We don’t write 80-page strategies that go in a drawer. The AI map is 5–7 pages you can start from.
- We don’t sell ChatGPT wrappers on a subscription. You own what we build.
- We don’t take jobs that Make.com finishes in an evening.
- We don’t work with companies under 50M ₽ revenue. Focus is mid-market, where the cost of a mistake is higher.
How companies deploy AI — and lose money
88% of companies already use AI. Profit moved for 39% — and for almost all of them by less than 5%.
McKinsey, The State of AI, November 2025 — 1,993 respondents in 105 countries. Nine typical mistakes grow from one root: AI is installed as a toy overlay, not as the company’s operating system.
Seven steps — each delivered against a verifiable artifact
Behind the seven steps is a working checklist of 46 tasks, each with a written acceptance criterion. Open a step: what happens, and what you accept.
0AI diagnostic · 30–90 min · free
We break the business down: Triple-Lens (efficiency / growth / innovation) × AI maturity map — with AI in real time. Priorities by money.
You accept: maturity map, pain points, priorities, draft roadmap. Before any payment.
1Protected contour · ~1 week · 14 tasks
Corporate domain, SSO, access policy. An employee leaving = revoke all access in one action.
You accept: the whole team inside the contour, security on.
2Data ownership and context · ~1–3 weeks · 8 tasks
All files, sheets, scripts move to company ownership. Each role is unpacked into memory layers.
You accept: each role verified 5/5.
3Anchor pilot · ~2 weeks · 6 tasks
The owner’s most painful job — usually management accounts: sources → P&L by line → payment calendar → cash flow.
You accept: “what’s net profit?” in chat — a reliable answer without spreadsheets, reconciled with the accountant.
4Automations by role · ~3–5 weeks · 9 tasks
Each leader gets a trio of automations taken to 100%. Not ten at 30% — one to the end, then the next.
You accept: an independent AI-auditor report per role + KPI panels tied to profit.
5Data channels · parallel to 3–4 · 5 tasks
Accounting, CRM, telephony, banks — data flows into the contour on a schedule.
You accept: zero manual exports.
6Keeper · finale · 4 tasks
A Keeper of the AI infrastructure is grown inside your team. Then monthly support and an export of context into your store as ordinary files.
You accept: the team clears a new blocker without us. You are autonomous.
Principles
- One window
Questions, documents, analytics, tasks — all through one contour.
- Done — only on a fact
Each task has a written acceptance criterion. Self-score is not acceptance.
- The goal is money, not automations
Revenue up, cost down. If a process doesn’t lead there — we turn it.
- Tracker after every session
Summary, results, decisions, blockers, owners, date.
- Data reliability
We build the solution in parallel with source quality, but the business owns the source.
- Top model
Answer quality tracks the model: we watch releases and upgrade.
Pace: team of 4–5 — 4–8 weeks (~5–10 sessions of 45 minutes); 2–3 people — 4–6 weeks. Daily solo work — 20–40 minutes per leader.
Scenes from a live deployment — no gloss
A production-trade company, 3 lines. Working moments from the project tracker.
AI took the sales-lead role
“It writes possible actions and what results they might have. It looks at everyone unused: abandoned, delayed, hanging clients.” First run — dozens of broken communications.
A manager built the dashboard. Himself. In a day
An employee with no technical background: all clients, auto-refresh, CRM links, a report per manager. The skill stayed inside the company.
AI stood up a server and moved accounting
“I created a new server with it. I exported 1C, moved it — it tuned everything. The accountant checked — it fully matched.”
The “we’re relaxing” moment
“Don’t enter anything, don’t open sheets. I say: file it — it files. I just watch it work.” Target owner state: operates meanings, not files.
The competitor’s verdict came from the competitor
The client wanted a model at a third of the price. “Don’t switch” came from the alternative model itself: no projects, memory layers, or team contour.
A VPN collapse lasted a day
~70% of VPN protocols dropped in the region. Our own WireGuard and a backup route were already in the contour — work restored the same day.
FAQ
We don’t have time for a deployment
Daily work — 20–40 minutes per leader, sessions 1–2 times a week. The rest of the time the system gives back. A team of 3 reached working management accounts in 7 sessions without stopping the core work.
Is our data safe?
Corporate contour: SSO, revoke access in one action, data and scripts owned by the company. AI prepares payments, it does not execute them. Zero-retention cloud or a local model in the perimeter.
AI hallucinates — we can’t trust the numbers
Answers are built from your documents and an accountant reconcile. The anchor pilot is accepted when the chat figure matches the source.
What happens when you leave?
A Keeper is grown inside the team. Context is exported monthly as ordinary files. Claude and Codex licenses are yours. You are autonomous.
What if the team won’t adopt it?
Training inside sessions, on live work. Employees build dashboards and roles themselves. Load is 20–40 minutes a day — not another project on top.
Why not wait a year until it’s easier?
88% already use AI; profit moved for 39%. Whoever accumulates context now opens a gap you won’t close later by “turning on ChatGPT”.
How many people do we need?
Owner + the top team. 2–3 leaders — 4–6 weeks, 4–5 — 4–8 weeks. The format needs 5–10 sessions of 45 minutes.
Isn’t this expensive?
From 350,000 ₽ · 3,900 USDT. Two tranches 70/30 on stage acceptance. The pilot shows effect before full payment. Licenses after that are yours, about $200–600/mo for a team of five.
What if sanctions / cut-off / models leave?
The stack can change; context stays in your files and contour. Backup access (your WireGuard) is laid in on step 1. The architecture is not tied to one chat.
152-FZ: may we send data to Claude and Workspace?
Yes — with the right contour: corporate accounts, access policy, consent, and a data list. On the diagnostic we split what can go to the cloud and what must stay in the perimeter. The legal frame is fixed in the contract.
How are the contract and payment structured?
Contract in the RF, invoice in rubles, dollars, or euros. Two tranches 70/30 against a verifiable artifact. The price ceiling is fixed after the diagnostic and is not revised.
Principles — in the manifesto. Book a slot in the discovery calendar.
30 minutes + an AI map of your business. You pay only if we go further.
We don’t sell on the call. You get a clear read: where the bottlenecks are, which AI closes first, and what that is worth in ₽/hour of the owner.
Request
A calendar slot or a Telegram message. In working hours we reply within an hour.
Brief
3–5 questions on the business and bottlenecks so the call is on substance.
Call with AI
Daniil + AI in real time. You see how AI looks at operations.
AI map
A PDF of priorities: 2–3 nodes with max return, a scheme, a pilot quote, ₽/hour of the owner.
What you hold in 48 hours
An AI map — 5–7 pages that read like a spec. The map is yours even if we don’t continue.
- I. Operations map — processes, data, decision contours
- II. 3–5 high-return points
- III. Architecture: agents, integrations, data flow
- IV. First-pilot quote
- V. Return in ₽/hour of the owner
Daniil Biryukov (Daniel Dreames) founded Pattern Automation after years in e-commerce and content — and brought the same product mindset to building AI for real businesses. A global audience of 500K+ follows his work. Together with Devakee Nandan (CTO — LLM, agent architect), the team runs projects worldwide — B2B automation, agents, and systems that keep shipping long after the launch. You can find more about us here.
Pattern Automation & Neuro OS
Pattern Automation is an international technology ScaleUp specializing in AI automation, AI agents, multi-agent systems, business process automation, and enterprise AI — plus IT consulting and ESG automation.
We are the company behind Neuro OS, an AI operating system that orchestrates autonomous agents, multi-agent workflows, and business processes in one governed environment.
We help mid-market and enterprise B2B companies in 12 countries (Russia, Kazakhstan, United States, European Union, and worldwide) automate sales, finance, operations, customer support, and ESG — reducing manual work and scaling with AI. Full company definition.
Let’s look at your business through AI-architecture optics
30–90 minutes. Free. Slots are limited. You leave with an AI map and a development plan — whether we work together or not.
“I say: file it — it files. I don’t go into the sheets anymore.”
Next step
Pick a slot — about 15 minutes. Or write on Telegram.
Partner program
Bring a client — receive 10% of every payment they make
Not a one-off bonus on the first deal. Ten percent of everything that client pays us — for as long as they stay.
What you get
- 10% of every payment, not only the first
- Attribution before talks start
- Zero work after the intro
- Visibility of stages and payments
Honest limits
If the diagnostic shows the economics don’t close, we say so. The client stays attributed to you.
How it works
- 01You write and name the client
Before they reach us. Name, company, what they do.
- 02They are attributed to you
90 days to the zoom, then for the whole engagement.
- 03You show the gist
The site or the channel. You don’t need the tech.
- 04You set a zoom
We sell and deploy from there.
Approach
- 05From demo to pilot
In days — built on your processes, not a demo that stalls after launch.
- 06Orchestration and handoffs
Between agents and your people: reporting, handoffs, control matrices. Humans keep control; agents take the routine.
- 07Priced like LEGO
You buy only the pieces you need — they close processes and raise efficiency, not AI for its own sake.
If the diagnostic shows the economics don’t close, we say so. The client stays attributed to you.