01
Workflow mapping
We identify the loops where time is lost: reporting, content adaptation, research, QA, support triage, data cleanup, documentation, production handoff.
Service / AI & automation
Agents that turn repeated work
into running systems.
We design and deploy AI agents — built on Claude, OpenClaw, Hermes and custom frameworks — that do real work in your operation, continuously, with humans in control.
What changes
FIG. 01A prompt is useful. A system is better.
Most companies use AI as a chat window: someone asks, the machine answers, the value evaporates. The real leverage appears when AI becomes infrastructure — connected to your files, tools, inboxes, calendars and rules, working on a schedule instead of on demand.
That is what agents are. They research, draft, code, check, transform, summarize and prepare work continuously. New agent platforms — OpenClaw for personal and operational automation, our own Hermes framework, Claude-based custom agents — make this practical today, not someday.
Humans stay in control of judgement and decisions. The system removes the repetitive 80% around them.
How we work
FIG. 02From isolated AI experiments to a working operation.
02
Agent design
Roles, tools, guardrails, approval steps and escalation rules — so agents do useful work without creating chaos, and every action is traceable.
03
Deploy and iterate
We connect real tools, run the system on real work, measure what it saves and tighten it week after week. Automation is a practice, not a purchase.
What you get
FIG. 03A smaller team with a larger nervous system.
- Claude
- Claude Code
- OpenClaw
- Hermes
- n8n
- Zapier
- MCP
- AI workflow audit and opportunity map of your operation
- Turnkey agent systems on Hermes, OpenClaw or n8n — integrations, plugins, Skills, MCP tools and scripts included
- Dashboards and control apps: pause, approve, inspect what your agents do
- Custom agents for research, reporting, support and operations
- Email, calendar and document automation with approval gates
- Multi-agent pipelines and swarms producing artifacts at volume — reports, banners, emails, product descriptions, summaries, videos
- Knowledge systems: your documents, searchable and answerable
- Monitoring agents that watch metrics, competitors or inboxes and alert you
Old model vs. Ex Machina
FIG. 04| Traditional agency | Ex Machina | |
|---|---|---|
| Weekly reporting | Half a day of someone’s time | Runs itself, human approves |
| Research briefs | Days of junior work | Minutes, reviewed by a senior |
| Support triage | First-in, first-out inbox pain | Sorted, drafted, escalated |
| Cost structure | Headcount scales with volume | Volume scales without headcount |
Questions we actually get
How do we prevent an agent from doing something wrong?
Guardrails and approval gates. Agents propose, log everything and only act autonomously within limits you set. The riskier the action, the more human sign-off it requires. You expand autonomy only as trust builds.
Is our data used to train AI models?
No. We use enterprise-grade APIs where your data is not used for training, and we can keep sensitive processing inside your own infrastructure.
Where should a company start?
With the audit. One workshop plus a week of analysis produces a map of your workflows ranked by automation value. You will know exactly which three systems to build first and what they save.
Next step
Ready to talk about ai & automation?
We'll tell you if we should work together.
A short conversation. No theatre. No deck. No delay.