Why classic analytics is no longer enough
Dashboards show what happened. But businesses need to understand:
- why it happened
- what will happen next
- where risks and opportunities are hidden
- which actions will deliver the best result
As data grows, manual analysis becomes slow and expensive. AI agents can continuously analyze data and search for anomalies, deviations and new signals several times a day. You don’t need to wait for a weekly or monthly report to spot a problem and make a decision.
AI that works with real business data: automates analysis, explains changes in metrics, helps identify risks, and speeds up the path from data to decisions.
“South” retail chain: deviation from plan over the last 9 days
Reconciling stock and orders across 42 points of sale
Shower gel out of stock at the warehouse for 9 days in a row
Restore the supply, recalculate the SKU forecast
From data to recommendations
We build an intelligent layer that unites corporate data, analytical models and AI technologies into a single decision-support system.
The AI connects to the company’s existing IT landscape — CRM, ERP, 1C, IBP, DWH, BI systems and operational databases — and uses them as a single information environment for analysis.
AI can:
- analyze large volumes of corporate data
- build forecasts and scenarios
- identify anomalies, risks and growth opportunities
- explain the causes of changes in metrics
- cross-reference data from different systems
- answer questions in natural language
- generate recommendations and next steps
Several specialized AI components can analyze one task from different angles: sales, inventory, forecast, pricing, promotions and other factors. The results are then cross-checked and combined into a single conclusion. This is how AI moves from a simple answer to a full investigation of the business situation.
AI agents connected to your systems:
An AI council for complex tasks
Several specialized AI components can work on one task, analyzing it from different angles and using data from various sources: 1C, CRM, ERP, PostgreSQL, pgvector, Excel, PDF and other corporate systems.
One AI component can analyze sales, another inventory and forecast, a third pricing and promotions. The results are cross-checked and combined into a single analytical conclusion.
AI doesn’t just answer the question — it tests hypotheses, finds root causes and forms a well-founded conclusion.
In the interface
AI that explains the numbers itself
Not just a chat layered on top of a dashboard. Our technology turns AI into an analytical researcher that works with the company’s data.
The assistant sees a sales decline, investigates data from connected systems, checks possible causes, and presents the result with specific figures.
- A natural-language dialogue
- Analysis of relationships between data and metrics
- Moving from an answer to a detailed investigation
- Testing hypotheses and cross-checking results
- A research log with the analysis progress, calculations and SQL queries
What the client gets
An AI layer that constantly works with the company’s data and helps find what needs attention
Historical data, current metrics and forecasting models are combined to assess future scenarios.
AI independently finds deviations and signals that might go unnoticed in regular analysis.
Instead of manually searching for data and preparing calculations: a ready-made analytical conclusion and recommendations.
AI takes on a significant share of repetitive analytical tasks.
Answers to questions, explanations, calculations and recommendations in natural language.
The AI layer can gradually be extended to new processes and data sources.
We develop our own AI technologies for working with corporate data — from analytical agents to systems capable of conducting complex research on their own.
Want an AI assistant for your data?
We’ll show you what it could look like on your own systems and data, with no obligation.