Technological tasks
The Tech-tasks module is tasks related to the development, implementation and improvement of new technologies, products and processes. If you have development, implementation and other needs you can post information below.
The ideal anomaly detector in logs and telemetry
As the number of mobile app users increased, the customer faced an avalanche of logs and metrics. The security team would like to have a model that: • detects all real incidents (attacks, leaks, critical bugs) in log and telemetry streams; • never issues false alarms for benign events; • automatically adapts to any future changes in user behavior and infrastructure; • does not require manual configuration of the rules. What was needed was, in fact, an "ideal anomaly detector" that works like a black box and is guaranteed to distinguish dangerous behavior from safe behavior for any possible input data.
Decision acceptance deadline
22.12.25 (inclusive)
Preferred systems
мобильное приложениеNumber of applications
2
Universal DATABASE Schema and Query Optimizer
The customer uses PostgreSQL 16 as the main storage for mobile event analytics. The load profile changes monthly: new reports are added, and filtering patterns change. The customer formulated an ambitious requirement: to build a system that automatically selects the schema, indexes, and hints to the optimizer so that ** for any current and future set of queries** the average delay is no worse than a certain theoretical minimum (for example, no more than 5% worse than the optimal plan). The system should be universal — not for a specific set of requests, but for any possible workloads.
Decision acceptance deadline
22.12.25 (inclusive)
Preferred systems
Smart Field Information SystemsNumber of applications
2
Development of the Cognitive Multi-Agent Predictive Network
To create an intelligent multi-agent system capable of independently analyzing, predicting and optimizing the behavior of digital counterparts of complex objects and infrastructures. The system should combine cognitive models, neural network predictive algorithms and distributed decision-making mechanisms, forming a single layer of "smart management" on top of physical and virtual environments. The project aims to develop the Cognitive Multi-Agent Predictive Network, a distributed network of autonomous computing agents that interact with each other and with digital counterparts in a simulation environment to solve analysis, forecasting, and management tasks. Each agent in the system must have the following capabilities: Cognitive interpretation of an object's state The agent analyzes the data of the digital twin (geometry, telemetry, events, historical records) and generates a model of the current state. Neural network predictive modeling Based on AI operators, including dynamic models (Neural PDE, Graph Neural Networks), the agent predicts the development of the system ahead in time: loads, risks, bottlenecks, potential accidents. Distributed decision-making Agents coordinate their actions through relational graph structures, ensuring consistent behavior without a central control node. Adaptation and self-study The system corrects internal models based on discrepancies between simulation and real observations, improving the accuracy of the digital twin's behavior. The project provides for the creation of a unified cognitive infrastructure embedded in digital twins, which will allow: to predict events before their actual occurrence; optimize processes and loads in the infrastructure; detect anomalies and threats; coordinate the operation of multiple virtual and physical objects; to form autonomous management strategies. The resulting system represents a new level of digital twins — not only reflecting a physical object, but also having built-in intelligence capable of predicting, analyzing, and interacting.
Decision acceptance deadline
22.12.25 (inclusive)
Preferred systems
Intelligent control systemsNumber of applications
0
Automatic semantic merging of branches without conflicts
The customer has a monorepository with mobile and server components (Kotlin, Swift, Go, TypeScript). The number of parallel feature branches exceeds 50, and manual conflict resolution takes weeks. A requirement was formulated: to design a semantic merge system that: • always generates the correct merged version or explicitly refuses to merge; • in case of a successful merge, guarantees the preservation of behavioral equivalence with respect to both branches (none of the existing functionality breaks); • works for any project in the repository without custom rules. In other words, an "automatic engine without surprises" was expected, which removes the issue of manual conflicts in principle.
Decision acceptance deadline
22.12.25 (inclusive)
Preferred systems
Smart Field Information SystemsNumber of applications
0
Development of a new generation digital solution for the modernization of KTZ's automated transportation process management system
Creation of a new generation digital solution for the modernization of the automated transportation process management system (ASOUM). The new system should include the full functionality of the current version and ensure the implementation of new industry requirements.: improving operational management efficiency, using AI and predictive analytics, integrating with other KTZ digital services, supporting a real-time transportation information model, and improving operational planning. The project involves the involvement of domestic IT companies and experts, the application of the best international practices and modern technologies.
Decision acceptance deadline
18.12.25 (inclusive)
Preferred systems
Technologies in transport and logisticsNumber of applications
7
Development of a project management platform with client access and status and documentation control
The aim of the project is to develop and implement a software solution for automating project management processes in service and consulting companies, including managing completion statuses, storing documentation, timing control, financial monitoring, and providing clients with secure access to up—to-date information on their projects. The solution improves transparency, increases the speed of decision-making, and reduces the risks of errors and data duplication.
Decision acceptance deadline
18.12.25 (inclusive)
Preferred systems
Intelligent control systemsNumber of applications
4
AI assistant for conducting interviews
The company conducts several dozen interviews of IT specialists per month. The cost of conducting a technical interview with a lead is high. The goal is to reduce the cost of hiring and improve quality. Our vision is to create an AI assistant that would be used by an HR recruiter (not a technical expert) for the initial assessment of professional skills of programmers. The AI assistant must work in prompter mode. When conducting an interview, he should suggest which questions to ask and, depending on the answers, suggest the next question. According to the results of the interview, he should give an assessment of the candidate's skills.
Decision acceptance deadline
18.12.25 (inclusive)
Preferred systems
Neurotechnology and artificial IntelligenceNumber of applications
8
Oracle of regressions for arbitrary versions of a mobile application
The customer supports several mobile applications with a common business logic code (Kotlin Multiplatform + Go‑backend). After each release, the QA team spends weeks on regression testing. It was proposed to develop a tool that, according to a pair of versions (old and new), automatically answers the question: "Is there at least one use case (sequence of user actions, background events, network latency, etc.) in which the new version behaves differently from the old one in terms of declared invariants?" Important: the tool must give a **strict** answer (yes/no), without probabilistic assumptions.
Decision acceptance deadline
18.12.25 (inclusive)
Preferred systems
ITNumber of applications
0
Optimizing the QA process for a web product at the MVP stage
Develop and implement an effective testing strategy for web MVP, which will significantly improve the quality of the release, saving the team time and resources. The project aims to create a structured, prioritized and fast QA process for the product at an early stage. The task will cover planning, manual testing, analysis of results and laying the foundation for future automation.
Decision acceptance deadline
18.12.25 (inclusive)
Preferred systems
Other technological solutionsNumber of applications
2
AI is a first-line agent
To develop an AI agent that automatically distributes incoming customer requests to the appropriate groups of managers based on territorial characteristics. The agent must work in WhatsApp: independently clarify the client's city, classify the request and direct the dialogue to the desired regional group of managers, without the participation of an employee.
Decision acceptance deadline
18.12.25 (inclusive)
Preferred systems
Neurotechnology and artificial IntelligenceNumber of applications
10
