aictrl
aictrl.dev is an AI workflow-orchestration platform for engineering teams. It grounds AI agents in product backlogs, team standards, and a codebase knowledge graph, then provides controlled execution, observability, evidence, and governance so teams can ship with confidence.
At a Glance
- Software engineering teams
- Organizations adopting AI coding agents
- Enterprise engineering organizations needing governance, auditability, and security
- Teams using GitHub-based delivery workflows
- +1 more
AI Tools by aictrl
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aictrl
AI Workflow Orchestration for Engineers
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Latest News
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Products & Services
AI workflow orchestration and control plane for engineering teams, combining a grounded backlog, skills governance, knowledge graph, controlled execution, observability, approvals, and evidence capture.
Reviews pull requests in repository and product context, records severity/file/line/lifecycle state for findings, supports triage, and measures actionability, review duration, noisy rules, and PR-funnel movement.
A centrally managed, versioned library of reusable AI-agent playbooks, with local open skills and connected team governance, sharing, and telemetry.
Declarative YAML workflows committed to .aictrl/workflows/, composed from skills and task templates, triggered by GitHub events, and executed asynchronously in isolated cloud containers with artifacts and quality gates.
Market Position
aictrl.dev positions itself as an orchestration and governance layer above individual AI coding agents and code-review bots: its differentiation is the combination of shared skills, repository knowledge graphs, deterministic workflow shells, isolated execution, evidence, approvals, and delivery analytics. Its own comparison material places it alongside GitHub and GitLab for agentic SDLC automation and alongside AI review products such as CodeRabbit, CodeAnt, GitHub Copilot Code Review, Qodo, Cursor, and Greptile. The product emphasizes measurable, reviewable engineering outcomes rather than comment volume or ungoverned individual-agent usage.
Leadership
Founders
Bulat Yapparov
Founder of aictrl.dev; former Director of Data Engineering at HeliosX, where he built a large-scale data platform processing millions of events daily for a UK-regulated online healthcare platform. He has more than 15 years of experience in data engineering, platform architecture, and developer tooling. Earlier roles include HeliosX Group and MADE.COM.
Executive Team
Bulat Yapparov
Founder
Former Director of Data Engineering at HeliosX; previously worked at HeliosX Group and MADE.COM. His background spans data engineering, platform architecture, developer tooling, and large-scale data platforms.
Founding Story
aictrl.dev was started around the belief that AI agents should prove their work rather than merely promise it. Its initial vision was to provide an orchestration, observability, and governance layer that connects agent work to engineering backlogs, reusable team standards, and structural codebase context, with inspectable evidence and human approval rather than blind trust.
Business Model
Revenue Model
Subscription platform fees based on team seats, with execution-based usage charges; Enterprise uses volume pricing and annual contracts with custom execution allowances and negotiated per-execution rates. Customers on the paid Team plan connect their own Anthropic API key for model usage.
Pricing Tiers
20 executions, no credit card required.
50 executions per seat per month, then $1 per additional execution; includes reviews, skills, observability, tool unification, GitHub and Slack integrations, five agent harnesses, 30-day telemetry retention, and email support.
Limited to the first 20 teams; rate locked for the life of the subscription, with the same 50-execution-per-seat allowance and $1 per extra execution.
Annual contract, volume pricing, custom execution allowances, negotiated rates and discounts, unlimited users, custom governance/integrations, dedicated customer success, and custom SLA.
Target Markets
- Software engineering teams
- Organizations adopting AI coding agents
- Enterprise engineering organizations needing governance, auditability, and security
- Teams using GitHub-based delivery workflows
- Engineering leaders measuring AI-assisted delivery quality and throughput
- Automated code review on every pull request
- Implement-and-review loops started from GitHub issues or pull-request events
- Repository-aware codebase Q&A and structural impact analysis
- Standardizing engineering practices through shared skills and playbooks
- Multi-step software-development workflows with approvals and evidence
- Delivery analytics and measurement of AI-assisted engineering quality