Operational AI and data analysis for Polish companies
AI in business, not in theory.
We connect data from systems, documents, messages and other company sources so teams can understand what is happening, detect deviations and make better decisions faster. Each solution is built around the company’s processes, without replacing its core systems, with human control where outcomes matter.
Multiple sources
one operating picture
systems, documents, messages, tables
Data analysis
signals for action
KPIs, trends, deviations, risk
Operational AI
results in the workflow
alerts, tasks, reports, decision drafts
Control
people decide
at points with real impact
Where AI and data analysis deliver results
We look for workflows where teams lose time gathering information, comparing records by hand or handling exceptions. A solution can start in one area, then expand across more sources and teams.
Sales and customer service
We connect contact history, offers, orders and CRM data so teams can recognize customer needs, delay risk and cases requiring attention sooner.
Operations and quality
We compare process results, inspections, machine data and documentation. Deviations reach the right person with the context needed to decide.
Finance and documents
We read invoices, contracts, reports and correspondence, check data completeness and highlight differences, risks and items that need attention.
IT and engineering
We organize tickets, logs, specifications and change history. AI helps assess impact, find similar cases and prepare the next steps.
One layer above the tools already in place
We do not begin by replacing ERP, CRM or document systems. We connect the sources that matter into a shared model of company data and work. Analysis then leads beyond a chart to an alert, task, report or decision grounded in a familiar workflow.
Connected sources
Business systems, documents, messages, spreadsheets, databases and APIs provide the information the process needs.
Shared context
Data is organized around customers, orders, cases, assets and the other entities that structure real work.
Analysis and reasoning
The layer detects trends, deviations, gaps and relationships, while every result remains traceable to source data.
Controlled action
The output is a report, alert, task or decision draft. People review results at points with real operational impact.
The uruchom.ai layer adapts AI to the company instead of forcing the company into a generic tool.
| From | To | Count |
|---|---|---|
| Business systems | AI layer | 6 |
| Documents | AI layer | 5 |
| Engineering data | AI layer | 4 |
| Vision and audio | AI layer | 3 |
| Industrial signals | AI layer | 4 |
| AI layer | Decisions | 6 |
| AI layer | Drafts | 5 |
| AI layer | Tasks | 5 |
| AI layer | Evidence register | 6 |
Data from different sources enters a shared layer and returns to teams as controlled outputs. Stream widths are illustrative. · uruchom.ai - operational layer diagram
From data to a decision inside a real workflow
The team sees the full context and a proposed next step instead of rebuilding every answer from several systems.
Customer order with margin risk and a missing approval
Waiting for the owner to check the evidence and approve the next step.
Less manual searching. More useful information.
Today: the data is scattered
- A customer, order, invoice and exception live in different systems.
- Teams compare exports manually before they can act.
- Reports explain what happened but do not prepare the next step.
- Decisions depend on memory instead of a visible source trail.
After deployment: the team sees the whole picture
- The layer assembles the relevant sources around the same customer, order or document.
- KPIs, deviations and missing data are visible in context.
- The team receives a report, task, alert or decision draft.
- Sensitive actions stay reviewable before they move forward.
From the first problem to a working solution
We begin with a business outcome people can see and evaluate: shorter cycle time, lower cost, better quality or an EBITDA impact. Then we connect the required sources, build the solution into the workflow and improve it through real use.
- 01
Define the outcome
Outcome defined
We choose a workflow, decision or analysis where better information will create a visible change for the team.
- 02
Connect data and working rules
Sources mapped
We establish where data comes from, what it means, who uses it and which steps require human control.
- 03
Build and embed the solution
Solution running
We create analysis, alerts, tasks or an interface and fit them into the team’s workflow and existing systems.
- 04
Measure, improve and expand
Outcome confirmed
We verify quality and usefulness. Only then do we extend the scope to more data, decisions and teams.
How large organizations use AI in practice
Public examples of AI use; these are not uruchom.ai client results. Each entry links to a source published by the organization described or by the solution provider.
| Organization | Published result | Source and date |
|---|---|---|
| Bank Pekao | The przeczytAI tool recognizes documents, extracts data and enters it into systems. The bank reports more than 1.5 million documents processed each quarter. | Bank Pekao 30 Apr 2025 |
| Vodafone | In Portugal, SuperTOBi increased first-contact resolution from 15% to 60%, while online NPS rose by 14 points to 64. | Vodafone 4 Jul 2024 |
| Santander | Santander reports more than EUR 200 million in savings from AI initiatives in 2024 and a ChatGPT Enterprise rollout to nearly 15,000 employees in two months. | Santander 12 Aug 2025 |
| A&O Shearman / Harvey | Around 2,000 lawyers use ContractMatrix daily. Harvey reports a 30% reduction in contract review time and up to seven hours saved per review. | Harvey / A&O Shearman accessed 10 Jul 2026 |
| PZU | Samoobsługa NEXT uses generative AI for straightforward PZU Dom claims. PZU expects about 12,000 reviews a year and payment in under 24 hours for straightforward cases. | PZU 3 Feb 2025 |
FAQ
-
No. A dashboard may be one view, but the goal is to connect analysis to a real case and an action the team can take.
-
No. We connect the required data above existing systems and fit the result into the current way of working.
-
One workflow where people lose time searching for information, comparing records by hand or handling exceptions.
-
Yes. We can start with one team, then extend the shared model of data and work into adjacent areas.
-
Results with real impact are presented for review and approval, while sources and decision history remain visible.
-
Identify one workflow where the team lacks a complete operating picture or the next step takes too long.
The first step starts with a conversation.
Every company has workflows that AI can make faster and less costly. The first deployment does not need to be large or complicated. Email us or reach out on LinkedIn, and let’s discuss the first step.
One process. A clear outcome. A practical next step.