AI for business
An AI agent that takes care of the routine.
Processes incoming mail, pulls data into the CRM, drafts replies for support, builds the morning overview. A concrete task, a human approval step, measurable time returned to your team.
Agent right now
What slows the business down today
Four places where time leaks every day.
Most companies do not need an AI strategy. They need to remove the manual routine that repeats day after day and nobody wants to do.
People do routine instead of the work they were hired for
Sales rewrites leads into the CRM, accounting types documents from PDFs, support copies the same replies. The team drowns in work the system should handle.
Manually re-entered data between systems
From e-mail into the CRM, from PDF into accounting, from a form into a spreadsheet. Every re-entry is another mistake and another lost hour.
Inefficient e-mail support
The same questions arrive week after week. The team sorts them, looks up context, writes replies from scratch. Quality varies, the customer waits.
Repeated reporting from several systems
Someone has to pull numbers from ERP, warehouse and projects every morning and glue them into an overview. By the time it is ready, half the day is gone.
The agent at work
A day when the agent handles the routine.
Four real situations from one working day. The agent reads the message, verifies data in your systems and prepares a reply. A human always has the last word.
AI agent onFinance9:42
Incoming message
Jan Novák, ABC s.r.o.9:42
Invoice request
Hello, could you issue an invoice for company ID 12345678? It is for last week's order, ref. 2026-0142. Thank you.
Agent processing
Request typeInvoicing
Checking sources
- CRM
- Invoices
- Contracts
Extracted data
- Company ID
- 12345678
- VAT payer
- Yes
- Due
- 14 days
- Order
- 2026-0142
Draft reply
To: Jan Novák
ApproveEditReject
Sent 9:46 · approved by accounting
- A human always approvesThe agent never sends on its own. The draft waits until your team approves it.
- Data under controlEuropean jurisdiction, contractual protection, no use of your data for training.
- Audit trailEvery step is traceable: what the agent read, where it pulled from, who approved.
The same principle runs 24/7 across dozens of messages a day. That is 3 or more hours of focus work returned to your team.
What is possible
What you can hand over to an agent.
Eight tasks that eat the most time in companies today. Each can be deployed on its own. Start with the one that hurts the most.
Finance
Document extraction
Invoices and delivery notes from e-mail straight into accounting, matched to the order.
Sales
Lead qualification
From a web form to a CRM record with a score and a suggested first outreach.
Support
Draft replies
Triaged requests with a suggested reply and a source link. The team just approves.
Management
Morning reporting
Numbers from ERP, warehouse and projects in one overview. Every morning, no gluing.
Logistics
Stock and deadline watch
Alerts on low stock and slippage before the customer has to deal with them.
Finance
Payment matching
Incoming payments matched to invoices. Discrepancies land on your desk to decide.
Sales
Proposal preparation
Inputs from price lists and order history. Sales polishes instead of retyping.
Operations
Cross-system data checks
Finds differences between CRM, warehouse and accounting before they cause a mistake.
How we proceed
Four steps from idea to a running agent.
We do not start an AI project from a platform, we start from a concrete task. We find where it pays off fastest, build the smallest working version, and decide where to go next based on real operations.
1. Use case identification
We find the task where AI really makes sense. Not where the hype is.
- Map of repeated tasks in the company
- Rough economics: today versus AI cost saving
- Shortlist of 2 to 3 use cases with effort estimates
- Recommendation on what to pilot first
2. Pilot on one process
A working prototype within weeks, tested in real operations.
- Model and provider choice by data sensitivity
- Integration with sources (mail, docs, CRM, warehouse)
- Human approval step where it makes sense
- Accuracy measured on real cases
3. Integration into operations
If the pilot pays off, we move it into the team's daily work.
- Hook-up to CRM, accounting, warehouse, ticketing
- Roles and permissions by company rules
- Audit trail over every agent decision
- Escalation of uncertain cases to a human
4. Measurement and iteration
We keep it running long term, watch the impact and tune based on operations data.
- Accuracy monitoring, deviation alerts
- Logic updates as the data changes
- Periodic review with impact measurement
- Suggestions for next use cases based on real data
Frequently asked
What people ask about AI agents.
- For a company that has a repeated task with a clear pattern, done by several people following the same procedure, and at least basic data structure. If you make 50 proposals a month from the same template, it makes sense. If you are a five-person team where everyone does something different, we wait.
Pojďme to zapnout
Let us find where AI makes sense for you.
Write us which tasks in your business are done by several people following the same procedure. We will come back with an estimate of where the rollout will return fastest.