Testing the approach on one work stream
One team tests the approach on real tasks. Scaling depends on the results and on other teams’ readiness.
Shared rules for how the team works with AI agents: a project knowledge base, a risk register and stack-specific skills. Adoption starts with one work stream; results are validated before scaling.
The software development market is reshaping around AI
Kazakhstan. Demand for “pure” programmers fell by a tenth in 2025, while demand for data engineers and analysts doubled: teams are shifting from writing code to setting tasks and reviewing results, and without a shared standard for working with AI, outcomes depend on each individual engineer. Uzbekistan. The fintech strategy plans to train 5,000 specialists, but ready-made AI practices in banking software development are scarce on the market, so the standard has to come with the team.
Rocket AI Framework is a shared standard for how a team works with AI agents: project knowledge, rules and result review
Project research builds a knowledge base in a separate private repository. The team applies it to real tasks, updates the rules and reviews agent output.
One team tests the approach on real tasks. Scaling depends on the results and on other teams’ readiness.
Rules and skills are stored in Git. The team uses a shared version and reviews changes to it.
The knowledge base and risk register persist between tasks. They are kept up to date as the project changes.
Choosing the work stream, participants, goals and metrics; embedding the approach in the team’s processes.
Analysis of approved repositories, architecture and scenarios. Findings come with code references.
Project structure, component connections and key scenarios as an entry point to the knowledge base.
Risks, severity and code references; checks on changes affecting flagged areas.
Change priorities and pre-handover checks, all in a shared knowledge base.
Skills for review, debugging, development, API, testing and security, following project rules.
Selects development, review, debugging or release mode with the right skills.
Fetches the task card, documents and mockups from approved systems with read-only access.
Rules and knowledge are kept apart from client code. Subsequent teams reuse them once applicability is confirmed.
A report on what was done, verified and not verified; the acceptance decision stays with the engineer. AI in development
The map, risk register and change plan are a standalone research deliverable. Fixed price.
Rolling out the framework in one area and testing it on the team’s tasks. The timeline is agreed per project.
Keeping knowledge, risks, rules and skills up to date as the project changes — by subscription.
Adapting the knowledge base and skills to the next teams, with an applicability check. AI in development
“The model is smart, but it knows nothing about your project. Rocket AI Framework records the project’s rules, risks and skills so that the agent reads them on its own — and the team works with agents to one standard, not each in their own way.”
Oleg SolabayevCTO
No. Engineers set tasks, review results, accept changes and run releases. The framework sets shared rules and preserves context.
One team tests the approach on real tasks. Once the results are assessed, the practice is adapted for the rest.
During research, the code is read without changes. Knowledge is stored separately. At the development stage, code changes follow the team’s rules; access to context systems remains read-only.
Claude Code and Codex; shared knowledge and skills are stored in Git. Integration is verified for the selected tools and versions.
Rocket Crew is a tool for agent task execution; Rocket AI Framework is a service that introduces a shared standard for how the team works with agents. The two can be combined.
The research and rollout timeline depends on the project and is fixed in the contract. Tool integration and full ramp-up on the project are estimated separately.
The report lists changes, checks and remaining limitations. Acceptance and the merge decision rest with the reviewer; the report itself does not replace review.
Research is fixed-price, work with the team is priced by team composition and duration, support is by subscription.
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