Agents handle multi-step work across your systems, and a person approves every irreversible action.
Assessment at a fixed price — credited toward your pilot.
Read the email and picked out the items
Found the customer and their terms
Drafted the invoice
Create the invoice in the business system
Waiting for the accountant’s approval
Invoice created · logged
An AI agent does more than answer questions. It works through several steps on its own. It reads a request, pulls data from your CRM, drafts a reply or a report and hands it to a person for review. We build agents apart from your live systems and put them to work only after they pass acceptance on your own examples.
A manager copies a request from email into the CRM, then into a spreadsheet, then into a reply. Same steps every day, same mistakes too.
Someone spends part of every week exporting data from several systems into one spreadsheet. By the time the report is done, some of the numbers are out of date.
Contracts, invoices and investment proposals are skimmed because nobody has time for a full check. Errors in the numbers surface after the decision is made.
A script runs fine on clean data and stops at the first unusual case. An agent can work through that case or pass it to a person with an explanation.
40%+
of agentic AI projects will be canceled by the end of 2027, Gartner predicts, due to escalating costs, unclear business value or inadequate risk controls.
The agent sorts requests from your website, email or messengers, logs them in the CRM and answers routine questions using an approved script. Anything unusual goes straight to a manager.
The agent pulls data from your ERP, CRM and spreadsheets, compiles it to a template and flags discrepancies. Where data is missing, the report shows a marked empty cell.
The agent checks the figures in a contract, invoice or proposal against their sources and recalculates key metrics. Every comment links to the exact place in the document.
Collecting, selecting and digitizing material, plus drafting copy for your website or newsletter. A person reviews everything before it goes out.
We look at which tasks take up most of your team’s time and pick out candidates for an agent.
There is a process where an agent can deliver a measurable effect.
We map the process steps, systems and access rights. For each action we set an autonomy level and a task category, and record baseline metrics and a test set of examples.
Metrics and the test set are agreed before the pilot starts. The assessment may honestly conclude that an agent isn’t needed here.
We build the agent for your process and give it a dry run with no real changes. Then it goes to work with a person approving its actions.
The pilot passes acceptance against the agreed metrics, including the trap examples.
We extend the agent to related processes, or raise the autonomy level for actions where the log shows consistent results.
Every expansion goes through its own acceptance.
We monitor quality, cost and the action log, keep the agent’s knowledge up to date and rerun the tests after every change.
The regression tests pass after every update.
We look at which tasks take up most of your team’s time and pick out candidates for an agent.
There is a process where an agent can deliver a measurable effect.
We map the process steps, systems and access rights. For each action we set an autonomy level and a task category, and record baseline metrics and a test set of examples.
Metrics and the test set are agreed before the pilot starts. The assessment may honestly conclude that an agent isn’t needed here.
We build the agent for your process and give it a dry run with no real changes. Then it goes to work with a person approving its actions.
The pilot passes acceptance against the agreed metrics, including the trap examples.
We extend the agent to related processes, or raise the autonomy level for actions where the log shows consistent results.
Every expansion goes through its own acceptance.
We monitor quality, cost and the action log, keep the agent’s knowledge up to date and rerun the tests after every change.
The regression tests pass after every update.
Each agent has a monthly spending limit for AI models. When it hits the limit, it stops and notifies the person in charge.
Every step is recorded: what the agent read, what it did and which source it relied on. The person in charge can stop it in one click.
Before launch, the agent runs through the process without making real changes, and we compare the output with what we expect. The same test set runs again after every update.
Agents never live on your production server; we develop and test them separately. Payments, documents sent to customers and changes to records in your business systems happen only after a person approves them.
The level depends on the consequences of an action, not on how complex it is. Compiling a summary from five systems is complex but safe. Sending a payment is simple but can’t be undone.
Reads data, summarizes it and flags anything unusual. Changes nothing in your systems.
Example
Every morning, lists new requests and highlights those left unanswered for more than a day.
Drafts an email, a report or a record for your business system. A person makes the decision.
Example
Drafts a quote for a customer, which a manager edits and sends.
Takes an action in the system after the responsible person approves it. Every action goes into the log.
Example
Creates an invoice in your business system once the accountant has checked and approved the details.
Reversible, low-stakes actions: sorting a request, compiling a summary, answering a routine question from an approved script.
Anything that touches money, customers or records in your business systems. The agent prepares, a person approves.
Decisions a person is accountable for, negotiations, and tasks with no verified data behind them. We leave these to people and say so in the assessment.
For every action the agent takes: its autonomy level, who approves it and what counts as an error.
Set up for your data and process, with access limited to what the task actually needs.
A record of every run: what the agent read, what it did and which source it relied on.
Results on the test set, including the trap examples, compared with the baseline metrics.
Budget cap, limits, stop button and approval rules, documented and handed over to you.
How to check the agent’s work, how to stop it and what to do if it gets something wrong.
In a free 60-minute consultation, we’ll find 2–3 candidates for an agent and show you what AI can handle alone and what stays with your people.
Book a consultationA chatbot answers questions, and an automation script repeats fixed steps. An agent decides on the next step itself, works across several systems and handles unusual cases or passes them to a person.
It doesn’t take irreversible actions without human approval, so the review step catches the error. Every step is logged, the agent can be stopped in one click, and we add the case to the regression tests.
We set a budget cap before launch. When the limit is reached, the agent stops and notifies the person in charge. On our own content pipeline, the cap is $100 a month and actual spend is about $9.
Your ERP, CRM and other business systems, including customized and legacy setups where the logic lives in configuration rather than code. Also email, messengers, spreadsheets and documents. Access is limited to what each task needs.
Development and testing happen on isolated copies. In live systems the agent has read-only access, and changes go through only after a person approves them.
The readiness assessment takes about 2–3 weeks, and a pilot on one process about 4–6 weeks. The timeline depends most on access to your systems and the state of your data.
Yes, step by step. Once the log shows consistent results on a type of action, that action can move up a level after a separate acceptance. Actions involving money and customer data keep human approval.
Send a message to our team to find out how we can help you
Andrii
Head of AI for Business
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