Built by Impactia

Our product stack for implementing AI.

The platforms we built to get AI initiatives running in production.

Why this matters

Together they form one implementation layer: deploy faster, operate with more control, get more done across the organization.

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Process discovery

Atlas

Map how your organization actually works. Before automating anything.

Most automation fails because nobody mapped the real process first. Atlas interviews the people who do the work, captures evidence, reconciles conflicting accounts, and produces structured outputs ready for automation planning. AI-native from the first interview to the final deliverable.

  • AI-guided interviews via chat
  • Evidence capture and annotation
  • Reconciliation across conflicting perspectives
  • Structured outputs ready for automation planning

Orchestration

Arc

One place to manage every agent, workflow, and integration.

Most companies end up with agents scattered across tools, each with its own config and no shared context. Arc is where they all live. Agents, conversations, workflows, integrations, evaluations -- configured once, monitored in one place.

  • Agent routing with model and context protocol support
  • Conversation threads and execution logs
  • Workflow automation across agents
  • Built-in evaluation and experimentation
Work Items
Tech Plan
Execution
✓

Delivery

Wand

Specs drive everything. Not tickets. Not chat threads.

Delivery breaks down when requirements live in Slack messages and half-finished tickets. Wand starts from a functional spec -- written with AI assistance -- and turns it into work items, technical plans, execution runs, and deployments. Every change traces back to the spec that started it.

  • Spec creation through AI-assisted conversation
  • Automatic breakdown into technical plans
  • Kanban execution with full traceability
  • Deploy tracking across environments
$ poi "Connect to Gmail via IMAP"
[search] gmail imap python setup
[exec] import imaplib; m = imaplib.IMAP4_SSL('imap.gmail.com')
[exec] m.login('user', 'password')
✗ AuthenticationError: Less secure apps disabled
[reflect] password auth blocked → need OAuth2
[memory] store: "Gmail requires OAuth2 for IMAP"
[fix] switching to OAuth2 flow...
[exec] creds = flow.run_local_server()
[exec] m.authenticate('XOAUTH2', auth_string)
✓ Connected. 847 messages in INBOX.
semantic · episodic · procedural

Autonomous execution

POI

Software that writes itself. Then rewrites itself until it works.

POI is self-programming software. Give it an objective and it writes code, runs it, reads the errors, figures out what went wrong, and reprograms itself. Not retries -- actual self-modification. It remembers what it learns across sessions: which APIs need what auth, which patterns break under load, which approaches are dead ends. The system running today is not the same one that ran yesterday.

  • Writes and modifies its own code
  • Actual learning, not retry loops
  • Persistent knowledge across sessions
  • Every run produces a better version of itself

You don't need to buy or learn these systems. We use them behind the scenes to deliver your AI implementations faster and run them more reliably.