The Kazakhstani developer tested a multi-agent system on its own operations. The company now offers corporate process audits based on its use cases.
What happened
Rocket Tech handed over a significant portion of its operational tasks to a multi-agent AI system. According to a case study published on its corporate blog on July 15, 2026, neural networks took over up to 80% of routine work in marketing, research, and management.
Instead of integrating fragmented external services, the vendor built a unified digital environment. According to the company’s own data, this freed up senior specialists’ time for complex architectural and strategic tasks.
How the internal AI ecosystem works
The company detailed the mechanics of five key products that now support its team’s operations.
- Development: Unit, an autonomous AI engineer, functions as a mid-level developer. The system integrates into the codebase and task trackers: it requests access, pulls tasks from the backlog, writes production code, and conducts initial reviews.
- Corporate memory: The Tacit Knowledge Management platform automatically analyzes work communications in chats and calls, saving key decisions in a single database. The vendor estimates this solves the problem of lost agreements and speeds up new employee onboarding.
- Hiring: The Rocket Hunt Telegram bot collects applications, and built-in AI instantly conducts initial candidate scoring. Recruiters receive a filtered shortlist.
- Research: The Product Insight Lab pipeline automates user data collection. The system selects the research format, transcribes interviews, and generates reports using a database of AI personas.
- Content: Material production is handled by a full-cycle AI editorial team. A multi-agent system manages the process from finding news hooks to publication, reducing the human role to editor-in-chief functions.
Country and market
Rocket Tech has operated in the market since 2011, specializing in business application development, with a presence in Kazakhstan and Uzbekistan. Publishing a detailed internal case study signals a shift in the company’s positioning.
Finteqstan previously reported that fear of technology prevents Kazakhstanis from trusting AI assistants. At the corporate level, IT companies are trying to overcome this barrier through measurable efficiency and demonstrating their own working tools.
The vendor is directly offering businesses process audits and the development of similar solutions tailored to specific client needs.
What’s next
For the Central Asian market, publishing such case studies marks a transition from testing external chatbots to creating integrated corporate systems. Companies are starting to look for solutions capable of securely handling closed codebases, internal documents, and task trackers.
IT vendors are beginning to use internal AI experiments as a showcase to sell deep corporate process automation services.
The future development of this trend will depend on how applicable these ready-made AI ecosystems, built by developers for themselves, prove to be for companies in the traditional real sector.
