OpenClaw Consulting
AI workflows that turn business drag into cleaner execution.
Not a demo. Not a chatbot.
I'm Timo Möbes — AI consultant, builder, and former enterprise AI champion at Doctolib. I help service businesses turn messy follow-up, manual handoffs, and recurring operational work into practical OpenClaw and multi-agent workflows with clear human accountability.
The platform
What is OpenClaw?
OpenClaw is an open-source AI agent platform that can work across chat, email, browser sessions, files, APIs, and internal tools. It is not just another chatbot. Configured properly, it becomes an operating layer for research, follow-up, CRM updates, reporting, and recurring workflows — with human approval where judgment matters.
- → Multi-channel control: Telegram, WhatsApp, Slack, Discord, web
- → Tool integrations: email, calendar, CRM, Airtable, Google Workspace, APIs
- → Workflow automation: research, follow-up, reporting, CRM updates
- → Human approval: drafts, checkpoints, and safe escalation rules
- → Production deployment: VPS/Mac mini, security, memory, monitoring
- → Scheduled operations: cron jobs, heartbeat checks, recurring tasks
What I do
OpenClaw Implementation
Beyond deployment, I also design practical AI workflows for service businesses that need better follow-up, less admin drag, and clearer human accountability.
Operational Audit
I map your business processes, identify automation opportunities, and define what gets built — before a single line of code is written.
Architecture & Roadmap
You receive a detailed multi-agent blueprint: which skills, which models, which integrations, and in what order to deploy them.
Production Deployment
Full setup on your VPS or server. OpenClaw connected to your CRM, email, Telegram, and workflows. Production-grade security included.
Handover & Training
1:1 walkthrough of your new system. You own it — no dependency on me. I stay available for 30 days post-launch for refinements.
What this can look like in your business
Practical AI systems, applied to real operations.
Not vague “AI transformation.” Practical systems that remove manual work, speed up decisions, and make revenue-critical operations more reliable. Built around your actual workflows, with clear ownership, measurable outcomes, and implementation that holds up in the real world.
01
Turn inbound leads into qualified opportunities
A multi-agent lead handling system can capture inbound enquiries, enrich the company and contact, score fit, update the CRM, and draft the right follow-up within minutes. Instead of leads sitting in inboxes or getting handled inconsistently, your pipeline moves faster with better context and less admin.
02
Automate delivery operations without losing control
Client onboarding, reporting, handovers, approvals, and recurring delivery tasks can be turned into accountable workflows that route work to the right people and systems automatically. The result is less operational drag, fewer dropped balls, and a delivery engine that scales without adding chaos.
03
Build an internal AI operator for repetitive high-value work
If your team repeats the same research, analysis, QA, documentation, or support tasks every week, those workflows can be broken into specialist agents with clear rules and checkpoints. You keep human oversight where it matters, while the system handles the repeatable work faster, more consistently, and with full traceability.
If you can point to the bottleneck, we can usually design the system around it.
Anonymized proof signals
What this looks like when it works in a real business
These are anonymized examples of the kinds of operational improvements I design for service businesses. The point is not flashy AI output. The point is cleaner execution where the business was previously leaking momentum.
Proof pattern 01
Lead handling moved from inbox chaos to structured follow-up
A founder-led service workflow was redesigned so inbound context could be captured, reviewed, and turned into faster first-pass follow-up instead of sitting in scattered threads.
Outcome direction: less manual triage, cleaner qualification, fewer warm leads cooling down before action.
Proof pattern 02
Post-call execution stopped depending on memory
A service delivery workflow was tightened so calls could produce structured summaries, action items, and follow-up drafts instead of loose notes and delayed next steps.
Outcome direction: faster follow-up, clearer ownership, less execution leakage between conversations and delivery.
Proof pattern 03
AI support added capacity without removing accountability
The workflow used AI for drafting, structuring, and routing while keeping commercial judgment and final approvals with the operator.
Outcome direction: more output and continuity without creating a system the business no longer trusts.
Proof approach
Practical AI workflows. Not automation theater.
I design AI-assisted workflows for service businesses that need fewer missed follow-ups, less manual admin, and stronger execution without piling on complexity.
What this means in practice
- →Reduce manual drag without building a fragile stack
- →Keep commercial judgment and final accountability human-led
- →Make the right next step easier after calls, meetings, and client interactions
Ideal fit
Who this is for
AI will make your team faster. It will also show you how much more is possible. The question is whether your systems can keep up.
- ✓ Businesses spending 10+ hours/week on repetitive tasks
- ✓ Founders who want AI but don't have in-house ML engineers
- ✓ Companies that need GDPR-compliant AI (Doctolib-grade experience)
- ✓ Teams for whom AI has created more work — and who need the right setup to absorb that scale.
Deployed systems for
Implementation guides
The OpenClaw Operator Playbook
Practical pages for founders and operators who need to make the right deployment decisions, avoid common implementation mistakes, and get OpenClaw live without turning the rollout into a cleanup project.
01
DIY vs Expert OpenClaw Implementation
Compare the real tradeoffs in cost, risk, time, and operational quality before deciding how to build.
02
OpenClaw Deployment: VPS vs Mac Mini
Choose the right deployment path based on uptime, privacy, and maintenance reality.
03
Common OpenClaw Implementation Mistakes
See the mistakes that make deployments noisy, fragile, or hard to trust.
04
Read the full playbook
Start with the hub page and follow the chapter that matches your current decision.
Investment
Choose your path.
Choose the path that matches how ready you are to implement.
Hourly Consulting
- 1-on-1 workflow diagnosis
- Security Hardening Review
- Remote
Full Package
- End-to-end workflow setup & deployment
- Custom Skill Integration
- On-site Support (Cyprus Only)
Proof signal 01
Engineer-grade implementation
Built around real workflows, system boundaries, and execution quality — not generic AI fluff.
Proof signal 02
Human accountability stays intact
AI supports drafting, routing, and summaries. Commercial judgment and final decisions stay human-led.
Proof signal 03
Designed to reduce chaos
The goal is fewer missed follow-ups, less admin drag, and clearer next actions after every key interaction.
Example implementations
See what implementation can look like before you book
If you are evaluating fit, these examples show the kind of workflow changes I help design: practical, accountable systems tied to follow-up quality, delivery continuity, and operational clarity.
01 · Inbound workflow
Turn inbound leads into structured follow-up
See how inquiry capture, signal extraction, draft follow-up, and safe CRM preparation can work together without removing human judgment.
02 · Post-call workflow
Turn calls into summaries, tasks, and next steps
See how post-call execution can move faster with structured outputs, cleaner handoffs, and draft-first follow-up instead of memory-based admin.
Choose the next step that matches your intent.
If you already know there is a workflow worth implementing, request the Full Package. If you want a paid diagnostic call first, book the €99 strategy session.
Best fit
- Service businesses with recurring follow-up or handoff problems
- Founders or operators carrying too much workflow context manually
- Teams that want practical implementation, not AI theater
Not the right call if
- You mostly want generic AI brainstorming without an operational bottleneck
- You want full autonomy with no human review on important actions
- You are looking for a cheap chatbot setup rather than workflow design
What you leave the first call with
A clearer diagnosis
We identify where workflow drag, follow-up leakage, or operational friction is actually coming from.
A recommended first workflow
You get clarity on which workflow is worth fixing first instead of trying to automate everything at once.
A realistic implementation path
You leave with a practical next-step direction, including what should stay human-led and what can be supported by AI.
Primary path
Request full implementation scope
Best when you already have a workflow, bottleneck, or OpenClaw rollout that needs practical design and implementation support.
Request Full Implementation →Direct call path
Book a €99 strategy call
Best when you want to diagnose the first workflow, pressure-test fit, or decide whether a full implementation makes sense.
Book the Strategy CallSerious implementation inquiries should start with the Full Package request. Direct strategy calls stay available when you want diagnosis first.