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years of experience
IT solutions
IT experts
What is Smart Data Intelligence?
For business
Managers, financial analysts, and HR specialists no longer have to wait for analysts to process their findings. All they have to do is ask a question such as:
- "Why did the margin fall in 08/2025?"
- "Show HC as of September 30 by division"
- "Which orders are behind schedule according to the SLA?"
The system responds in an understandable form – it adds a graph, table, or overview, while respecting role-based access permissions.
Result: Faster decision-making, lower workload for analytical teams, and higher data accuracy.
For technical teams
The AI layer understands the intent of the question and can find the necessary data directly in databases, data lakes, or BI layers—without the need for additional programming of queries, thanks to which the system:
- selects the correct data and processes it,
- add context,
- performs the interpretation.
Result: Fast and reliable responses without the need for manual validation – with the option to audit every step.
Try Smart Data Intelligence with AWS support
See how it can speed up decision-making, reduce analysts’ workload, and increase data reliability. With AWS support, we’ll prepare a Proof of Concept tailored to your needs — free of charge.
Key benefits of Smart Data Intelligence
Instant information
Natural language into the database via controlled access
No hunting data
semantic search across databases
Consistent reports
Mapping the single source of truth
Root cause faster
Segment analysis and driving forces
Trend clarity
On‑demand what‑changed comparisons
Menej chýb
Generovanie dotazov s ohľadom na schému
Traditional Approach vs. Smart Data Intelligence
- Waiting days for basic answers
- Inconsistent metric definitions across teams
- Ad‑hoc BI requests overwhelm analysts
- Exports violate access restrictions sometimes
- Decisions lack proof and traceability
- Changes in trends go unnoticed
- Deliver cited responses instantly in chat.
- Central dictionary explains KPIs with links.
- Smart self‑service with row-level-security data access.
- RLS‑safe CSV/Excel with full audit trail.
- Include citations, lineage, and reason codes.
- What‑changed lens surfaces key segments automatically.
What problems does Smart Digitization solve, and for whom?
Business leaders (HR, Sales, Operations, Finance)
ISSUE: need quick, consistent answers without waiting for analysts.
EXAMPLES: "What was the HC as of September 30 by division and year-on-year change?", "Why did the margin decline last month?", "Which orders are past their delivery date and in which regions?"
Analysts/BI and Data Stewards
ISSUE: spend time servicing ad hoc questions, explaining metrics, and exports.
EXAMPLES: "Explain the definition of Qualified Lead with reference to the data dictionary," "Generate RLS-safe CSV from the selected view."
Operation / Maintenance / Quality
ISSUE: They need to find the right procedure in the manuals, see the trend of errors, and link it to specific changes or processes.
EXAMPLES: "Show the last 7 days of faults on line L-12 and what has changed in the process/maintenance," "Which CAPAs are overdue and what evidence is missing?"
Success stories of our clients
Our AI experts have successfully completed numerous AI projects of varying scope. Our most significant AI solutions include:
Quality control based on artificial intelligence - the future of troubleshooting
Every industry, from microchips to smartphones to automobiles and food, must produce...
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AI Chatbot speeds up work with HR data
The implementation of an AI chatbot for HR reporting in the Azure environment provides fast and secure access to...
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AI Smart Assistant on AWS: Personalized Shopping Experience
A leading regional retailer of bathroom and kitchen equipment transformed the digital customer experience with AI Smart Assistant...
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Automation of the processing of the product documentation
This case study describes the implementation of a serverless AWS solution for automated processing of technical documents (data sheets, catalogs, etc.).
Read More ⮕FAQ
In AWS/Azure; in Azure also within your own tenant. The data will not be used to train public models.
Server‑side NL→SQL/DAX, RLS/RBAC, audit a RLS‑safe exporty.
PoC with MVP (2–6 weeks) with clear KPIs and scaling plan.
It does not shift the burden onto the user – it understands definitions and citations and suggests the next step or export.