The problem
Code, requirements, discussions and checks are scattered across tools. Teams spend time rebuilding context, handing off decisions and verifying outcomes at each stage.
CodeGryphon: the product and its plans
An AI assistant for software teams that works with repositories, tasks, docs and tools. This page covers the market, business model and product plans.
Working productDesktop · Web · TelegramBuilt for engineering teams
Working with team code and tasks
A developer connects the repository, issue and docs. Gryphon uses these materials, changes code and runs checks. Recurring tasks can be scheduled.
Our approach
The project stores team knowledge and rules. Members work in private chats. Desktop performs local actions; Web and Telegram provide access to the discussion from other devices.
MARKET
Why this market
We focus on teams using AI in existing repositories. They need to follow project rules, review changes and keep agreed decisions.
Initial target segment
Small product teams, development studios and technical leads maintaining existing codebases and several parallel projects.
84%
of survey respondents use or plan to use AI in development
51%
of professional developers in the survey use AI daily
The product foundations
Team-wide context
Knowledge, rules, focuses and results stay with the project and are available according to roles.
Execution and control
Code, commands, the browser and integrations form one workflow with results that can be verified.
Task-informed model selection
Our SDLC benchmark compares quality, time and usage to guide model selection for Gryphon modes.
Business model
Usage revenue
Glyphs account for product usage. Users top up their balance and choose a mode for the task.
Organizations
Deployment, infrastructure requirements and support are discussed through team pilots.
Execution economics
Model selection and context management help control task usage; the benchmark measures quality.
What exists today
Links to the apps, feature descriptions and benchmark results.
Three ways to work
Desktop, Web and Telegram share projects and an account.
Team workflows
Context, roles, focuses, Control Center and Project Pulse.
Quality measurement
Benchmark methodology and model comparison across three metrics.
Development history
Completed milestones, current work and future directions.
The next stage of growth
We are testing which tasks bring teams back and help them use the product across several projects.
Acquisition
Engineering task demonstrations, professional communities, partnerships and team pilots.
Retention
Schedules, shared knowledge and repeatable working methods. Measuring the workflows that bring teams back.
Economics
Measure the cost of accepted outcomes, usage and cohort retention. Select models by quality and resource use.
Technology development
Two planned directions: our own model for engineering tasks and predictive analysis of project problems.
Gryphon.Model
Training and developing our own model, evaluated on our SDLC benchmark. The aim is to adapt it to Gryphon tasks and tools and manage the balance between result quality, execution time and cost.
ML forecasting of project problems
A forecasting mechanism based on project history and trends. The aim is to identify quality, delivery and technical debt risks early and help teams prioritize what to investigate through Project Pulse and Control Center.
The product team
Kirill Ivanov
Product Lead & CTO
Sergey Zachesov
Architect
Yulia Malovitsa
Marketing & Communications
Investment and strategic partnerships
We welcome conversations about product development, market expansion and partnerships. The presentation, financial model and potential investment terms are available through a direct conversation with the team.