Do you always trust your data?
Almaz Monitoring — self-learning quality monitoring and anomaly detection in corporate storages, real-time data streams, operational and industrial processes.
Almaz monitoring takes quality under control!
The system uses innovative self-learning algorithms for identifying significant deviations in operational KPIs of business processes, financial and operational reporting in high-load technological flows and in Big Data lakes.
Solution diagram
Data from heterogeneous
Autonomous artificial intelligence system for machine analysis of data in real time and detection of significant deviations
Mobile notifications and incident management
Solutions used in
Fuel and energy
Usage scenario
Task: Real-time decision offering should constantly and effectively deliver target offers through inbound and outbound channels. Distribution should be under constant monitoring in order to prevent downtimes and incorrect targeting.
Monitoring KPIs: Hit rate, taker rate, response rate by target offer, region, channel (CRM inbound, IVR, SMS, self-service, mobile app, and more).
Result: More than $600k additional revenue generated due to prevention of loss or consistency in target offering performance.
Bank. Data Science
Task: Predictive modeling must be based on verified and trusted data. Credit scoring, spending segmentation and other data models sometimes give questionable results due to undetected irregularities in temporary malfunctioning data sources.
Monitoring KPIs: Validation by 30,000+ independent models covers more than 500KPI of customer profiles from 12 data sources.
Result: 15 percent increase in effectiveness of data science department due to reduced spending on model data verification and bugfixing.
Task: Automated monitoring of complete technological process to detect overall anomalies and to prevent internal fraud.
Monitoring KPIs: Resource consumption dynamics (coil, electricity) on each technology stage and final product values.
Result: Detection of resource misusage and losses. Comparative analytics of equipment effectiveness.
Task: Product managers should be informed about anomaly spikes of user tickers.
Monitoring KPIs: Average minute quantification of tickets over reasons for call. Size of waiting queue by each request topic. Fraction of tickets on each stage of Kanban process.
Result: Implemented a self-learning monitoring of anomaly spikes of user activity. Product owners and support managers now start being timely informed of 2nd and 3rd level support anomaly spikes and about dangerous deviations in the resolving cycle.
Web applications monitoring
Task: Unnoticed web application malfunctions and slow web user interface response undermining the best user experience.
Monitoring KPIs: More than 50 different system performance metrics in Java logs, Oracle log tables, response timings, Kafka and IBM MQ queue sizes.
Result: More than 30 percent of systems malfunctions start being detected and suppressed before getting the ticket from users. Team start monitoring more than 5000 system services and starts reacting on changes of service response time quality, leading to superior user experience.
Anti-fraud in digital retail
Task: Unscrupulous content provider forces users to activate paid services by deception.
Monitoring KPIs: Dynamics of activation by paid services.
Result: Immediate reaction on unreasonable spikes of fraud activations and reduction in customer claims.
DWH / DataLake
Task: Financial and operational reports contain inconsistencies due to undetected temporary data corruption in some data sources.
Monitoring KPIs: Data sources data, ODS, DDS, Datamarts (more than 1500 entities).
Result: Implemented improved data verification process in each stage, preventing further data transformation and usage of non-cleansed or non-verified data. Significantly reduced the number of report recalculation after user claims on report inconsistencies.
Task: Some items cause difficulties during the self-service process due to incorrect product data. This leads to a longer service time and lower user experience.
Monitoring KPIs: Average processing time per item correlated to other items. Cases of self-service cancellation.
Result: Reduced self-service time and improved success rate. Better customers experience.
Event Based Marketing
Task: Abnormal events trigger behavior, leading to less targeted campaigns and revenue losses.
Monitoring KPIs: Event frequency and event queue dynamics.
Result: Revenue increase in sensitive event-based target offers due to improved control of offer availability.
Task: Effectively deal with high water cut well problem in mature oil field.
Monitoring KPIs: Sensor data (water cut, pump intake pressure, flowline pressure, flowing bottom hole pressures, static bottomhole pressure, etc.).
Result: Increased ultimate oil recovery.
Retail. Loyalty
Task: Prevent incorrect awards.
Monitoring KPIs: Amount of loyalty points per action.
Result: Implemented loyalty points verification system. Prevented cases of significant losses caused by incorrect points calculation and rewarding.
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Let us help you to write your own success story!

Permanent, qualitative data of financial and operational reporting, production aggregates and views are necessary.
Real-time monitoring of technological, economic and operational KPIs based on big streaming data is necessary.
Timely response to any emergency situation before it makes a significant impact
A continuous stand-alone quality monitoring of reporting and streaming data.
An immediate notification about statistically significant deviations (mobile app notifications).
A self-learning system that uses existing data, adjusts to user behavior, and doesn’t require expert knowledge.
The introduction of an automated monitoring system and control of the information infrastructure is able to improve the quality of its operation using the rapid detection and elimination of failures and problems, as well as to prevent their emergence in the future, first of all, for the most critical services for the company's business.
We present you our product - the “ALMAZ MONITORING”.
It’s a self-learning data quality monitoring for business and production processes.
Partnership program

We’re enormously greatful to our partners for advancing and implementing end-to-end customer solutions based on our products.

We are happy to create deep motivation for our partners to collaborate. Together we help customers to benefit from new era of business and production digitalization.

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Flexible approach and focus on quality
Technical and marketing support
Profitable terms of cooperation
Education and certification
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The cost of software is calculated individually