AI agents that
ship your roadmap

The orchestration layer that grounds AI agents in your product backlog, team standards, and codebase knowledge graph — built for engineering teams who need rigour, not magic boxes.

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Review setup

Code review setup is manual

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Repo context, test commands, acceptance criteria, and ownership get rebuilt before AI work is reviewable.

Prompt collaboration

Prompt wins do not compound

Useful instructions stay in chat history and dotfiles instead of becoming shared team standards.

Usage visibility

Usage patterns are invisible

Leads cannot see which workflows save time, where runs fail, or which skills actually get reused.

Team acceleration

Acceleration is uneven

Senior engineers move faster first; new joiners still need context, examples, and a clear path to contribution.

Self-serve guardrails

Self-serve needs guardrails

More people can ship only when standards, permissions, evidence, and rollback paths are built into the flow.

aictrl turns scattered AI practices into shared operating infrastructure.
Four Pillars

The orchestration stack for AI engineering

Skills, Execution, Observability, and Knowledge — the complete orchestration stack for engineering teams using AI agents.

Skills

Your team's playbooks, centrally managed

Internal enterprise skills that encode your team's conventions, architecture decisions, and coding standards. Every AI agent follows the same playbooks — not ad-hoc prompts scattered across individual setups.

13.4% of ClawHub skills flagged for security issues
ctrl.skills ctrl.marketplace

Execution

Multi-step workflows, composed from skills

Compose skills into SDLC workflows — deterministic structure on the outside, fluid AI agent work inside each step. Every run is isolated in its own cloud container and produces inspectable artifacts. Your CI/CD stays in charge of release gates.

Every step cloud-isolated
ctrl.workflows ctrl.tasks ctrl.execution

Observability

Every agent action, traced & measured

Evidence capture, execution traces, and skill usage analytics — full visibility into what your AI agents produced and whether it worked. Every run generates inspectable artifacts, not just logs.

Every run audited end-to-end
ctrl.evidence ctrl.telemetry ctrl.analytics

Knowledge

Your codebase, understood

Knowledge graph and ontology that gives AI agents structural understanding of your code — relationships between files, modules, and APIs. Fewer tokens wasted on context gathering, better results from every interaction.

Significant token reduction
ctrl.knowledge ctrl.stacks
How It Works

From repo to shipped, in three steps

Cloud-native orchestration — no infrastructure to run, no tokens to manage.

Step 01 · Connect

Link your repos & backlog

Sign in with Google and connect your GitHub, GitLab, or Bitbucket repositories. aictrl builds the knowledge graph on its own infrastructure.

Step 02 · Define

Codify your team's standards

Import or author skills — reusable playbooks that encode how your team designs, reviews, and ships. Everyone's agents play by the same rules.

Step 03 · Ship

Agents run in the cloud with evidence

Workflows execute on containerised infrastructure, pass through your quality gates, and land with full audit trails — screenshots, tests, diffs.

Comparison

Not Another Code Generator

We build workflow orchestration, not magic boxes.

Lovable / Bolt Cursor GitHub Copilot aictrl.dev
Target Audience Non-technical Developers Developers Engineering Teams
Verification None None None Evidence-backed
Audit Trail No No Logs only Full evidence
Cloud Execution Local only IDE only IDE only Isolated containers
SDLC Workflows No No No Skill-composed
Team Dashboard No No Basic Real-time
Knowledge Graph No No No Ontology-backed
Skills Governance No Rules files No Enterprise-managed
Built For You

One Platform, Three Perspectives

Whether you lead the org, manage the team, or write the code — aictrl has your back.

Governance, ROI, and Business Alignment

Your board asks about AI ROI. Your compliance team worries about ungoverned agents. aictrl gives you the evidence to answer both — every AI session tied to a business outcome, with full governance and cost visibility.

  • Every AI task run tied to a business outcome
  • Organisational AI consistency at scale
  • Reduced AI context costs via knowledge graph
  • Governed skills instead of ungoverned prompts

Designed for

Prove AI ROI to the board with real productivity data and the governance controls leadership demands.

Standards, Quality, and Team Productivity

You manage 5-50 engineers using AI agents daily. You need to know what's shipping, what's stuck, and whether standards are being met — without reading every PR.

  • Team playbooks applied consistently to every agent
  • New engineers productive on day one
  • Any engineer can understand any repo
  • Evidence trail makes debugging faster when things go wrong

Designed for

Stop worrying about what AI agents are doing unsupervised. Check the dashboard — if runs are clean, move on.

Context, Patterns, and Speed

You're already using Claude Code. aictrl makes your AI sessions smarter — the knowledge graph finds the right files, skills give you proven patterns, and your progress persists across context resets.

  • Context persists across session resets
  • Proven patterns for every common task
  • Agent finds the right files immediately
  • Zero friction — one CLI command and you're connected

Designed for

Add aictrl in 30 seconds and get full codebase context plus your team's best patterns in every Claude session.

Frequently Asked Questions

Quick answers to common questions about aictrl.dev.

What is aictrl.dev?
aictrl.dev is the orchestration layer that grounds AI agents in your team's playbooks, codebase, and engineering process. It is not a code generator — it coordinates AI coding agents through four pillars: Skills (centrally managed team playbooks), Execution (skills composed into SDLC workflows and run in isolated cloud containers), Observability (evidence capture, execution traces, and usage analytics), and Knowledge (structural codebase understanding via knowledge graph). Think of it as the missing coordination layer between your AI agents and your engineering process.
How do the four pillars work together?
Skills encode how your team works into reusable, versioned playbooks. Execution composes those skills into SDLC workflows and runs each step in its own isolated cloud container — deterministic structure outside, fluid AI agent work inside. Observability captures the evidence every run produces plus usage analytics across the fleet. Knowledge sits beneath all three, providing structural understanding of your codebase so every skill, run, and trace is grounded in real code — not token-hungry guesses.
How does aictrl work with Claude Code?
aictrl connects to Claude Code via the Model Context Protocol (MCP). Run one command (claude mcp add --transport http aictrl "https://app.aictrl.dev/mcp"), sign in with Google, and Claude automatically receives codebase context from the knowledge graph, follows your team's skills, and records evidence from every task run. No manual configuration needed — just one CLI command to connect.
Is there a free trial?
We're currently offering early access. Contact us or sign up to get started with full access to all features.
What about security and data privacy?
aictrl captures task run metadata and evidence artifacts — your source code is never stored on our servers. Skills Governance ensures your team's AI playbooks are internal and enterprise-managed, not pulled from unvetted public sources where a ClawHub audit found 13.4% had security issues. All data is encrypted at rest and in transit. See our Privacy Policy for full details.

Ground Your AI Agents in What Matters

Four pillars. One platform. Start shipping your roadmap.

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