Generative AI for a company's operational work
Digital employees that do the work inside your systems — and answer for the result
BusinessOS turns enterprise AI from an adviser into a governed executor. AI agents take over repetitive knowledge work between 1C, email, Excel and messengers. Risky decisions stay with people; the effect is counted in money.
Review my processSee digital employees
Why AI pilots never reach the numbers on the board agenda
Companies have tried chatbots, copilots and automations. Budgets are spent — and six months later someone says: "nice, but where is the effect". The reason is one: three different things get confused.
Chatbot
Answers in a window and waits for a person to press send. Does not remember the client, does not see commitments, does not answer for the result.
Automation (RPA, n8n, macros)
A rigid "if X then Y" script. Speeds up one step and breaks at the first deviation: an email off-template, a supplier quoting the wrong unit.
AI agent in BusinessOS
Understands the situation in the company's context, chooses the action, performs it in your systems, stops before a risky step and leaves a trace of every decision.
BusinessOS
What BusinessOS is
BusinessOS is an operating layer where a company keeps its objects, events, rules, decisions and action history so that people and AI agents work from one picture. It does not replace CRM, ERP, email or storage — it connects them into one governed process.
Seven layers that make up operational capability
Digital employees
One agent is one employee: with a job description, access, KPIs and a manager. Not "magic AI for everything" but a role with a goal and boundaries.
Quote collection specialist
Sends requests against an item list, gathers replies from emails, Excel and PDF, compiles a comparison with a source behind every price, escalates gaps to a person.
PilotFinanceTax reconciliation controller
Merges 1C data, bank statements and certificates for 20–50 entities into one reconciliation per period. Finds discrepancies before tender documents are filed.
PrototypeProduction planner
Re-plans production on updated stock, demand and line availability — every day, not once a month. Shows what did not fit and why.
Data checkProject archive clerk
Sorts tens of thousands of files into an approved structure, removes duplicates, issues a document register. Deletes nothing.
PilotInvoice and month-end checker
Walks through the closing checklist, reconciles postings with the accounting system, lists errors and proposes fixes.
On requestOwner's morning brief
What changed in the last day, where your decision is needed, what the conclusion rests on, who owns the next step.
On requestStatus is honest: "Pilot" — running at a client; "Prototype" — working, being verified with accountants; "On request" — assembled for your process during a pilot.
Generative AI
Generative AI where it is strong. Rules where precision is needed
The language model reads and understands
Free-form emails, Excel with someone else's structure, PDF specifications, meeting minutes. It extracts data, finds contradictions, asks clarifying questions, explains the result to a person.
Calculations are deterministic
Amounts, matching and statuses follow approved rules, not the model's "reasoning". Any number in a report can be rebuilt from inputs and the methodology version.
Any model, your data
Claude, GPT, DeepSeek or an open model inside your perimeter. Only anonymised fragments reach the model. Company data is never used for training.
Company memory
The agent knows who the counterparty is, what was promised and decided before. One canon, many views — an index failure does not erase knowledge.
Autonomy is not switched on. It is earned
A leader's biggest fear: "AI will start making risky decisions on its own". In BusinessOS this is impossible by design: autonomy is set explicitly, as a ladder, separately for each class of action.
Never become autonomous: money, contracts, legal obligations, hiring, material commercial promises. This is not a setting — it is a limit built into the system.
Every action is stored with its reason, context and owner. If the agent errs, it is not "AI did something" but a concrete review: where the rule broke and how to fix it.
ROI
Where the money effect appears
Not promised ROI — measured
For each scenario, clear indicators are counted: manual hours per run, time to result, share of work closed without a person, share of escalations and approvals, reliability of actions in your systems. Each result is tied to a role and logged: what it created, what it cost. For a CFO this changes the conversation: AI stops being an experiment line and becomes a managed portfolio of operational bets.
Pay for results
Not for hours and not for "configuring a scenario" — for reduced manual time, closed requests, processed items.
The second agent is cheaper than the first
The core is already built: company memory, approval rules, process map. Each next scenario costs less than the previous.
We measure it on ourselves
The ROI we promise a client we first measure in our own operations.
FDE
How we deploy: an engineer inside your process
Forward-deployed engineering: our engineer sits with your team, takes apart the real process with its exceptions and assembles a digital employee on your data. Not a box, not "deploy it yourself".
- Review1 weekOne process, one owner, input and output examples, success criterion, data and access map.
- Pilot4–8 weeksThe digital employee works on your data under supervision. Checkpoints, expert comparison, training of the pilot group.
- Regular operationsubscriptionAgreed scope, support, metrics in the outcome log. Autonomy expands by action class.
- Scaleby resultNext roles on the same core — cheaper and faster. Expansion is decided on measured effect.
What each executive gets
FAQ
Questions asked at the first meeting
What is BusinessOS in one paragraph?
BusinessOS is an operating layer for a company where AI agents ("digital employees") do repetitive knowledge work between 1C, email, spreadsheets and messengers. Each agent has one duty, access, rules and a manager. Risky actions — money, contracts, commitments — require human confirmation. Every decision traces back to its basis; the effect is measured in money.
How is it different from a chatbot, copilot or n8n?
A chatbot answers with text and waits for a person. Automation replays a rigid script and breaks at a deviation. BusinessOS carries an operation to a result in the company's context: remembers the counterparty, commitments and decisions, handles exceptions, stops before a risky step and leaves a trace of every action.
What if the AI makes a mistake?
The error stops being a black box. Every action is stored with reason, context and owner: what the system knew, what it proposed, who approved. The review shows where the rule broke — and the rule is refined. Risky actions wait for a person until then. We do not promise zero errors; we promise governability.
Will you replace our employees?
We take a defined function, not a profession. First we measure how much human involvement remains. The strongest effect is work nobody does today for lack of hands: re-planning every day, reconciling before a tender rather than after.
Which language model is used and where is the data?
Any: Claude, GPT, DeepSeek via API — or an open model inside your perimeter. Only anonymised fragments reach the model. Data is not used for training. The primary deployment option is your infrastructure.
Do we need to migrate to a new system or train people on an interface?
No. The digital employee works where people already work: 1C, email, messenger, a file folder. A separate interface is for the configurator and for reviewing decision history.
Get a review
Start with one process
Not with a "digital transformation" — with one piece of work that has an owner, data and a number you can verify in eight weeks.
A review of one of your processes. 20 minutes, no slides
You show how the work is done today: what comes in, what should come out, where time is lost. We show which digital employee fits, what it will do on its own, where it stops and asks a person, and how we will count the effect in money.