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CK/SYSTEMS
AI Workflow Consulting

Governed AI workflows for teams that need systems, not demos.

I help founders and small teams design, ship, and harden AI workflow systems that survive real usage. That includes workflow design, agent integrations, approvals, validation, and the engineering rigor that keeps the whole thing from collapsing in production.

Consulting availability Taking 2 consulting projects in Q2 2026 Best fit: workflow audits, AI automation systems, and governed agent builds.
Your team has an AI pilot that is promising but brittle.
A manual approval, routing, or admin step still blocks throughput.
You need an engineer who can design, build, and harden the system end to end.
You want governed AI workflows, not another vague automation demo.

Engagements

Start with one workflow. The point is to move it from “interesting” to “operational.”

Governed workflow audit

AUD 350-900

A focused review of one workflow: triggers, failure modes, approval boundaries, observability gaps, and the safest path from pilot to production.

Governed workflow starter build

AUD 2,500-7,500

A fixed-scope implementation for one bounded workflow with routing, validation, approval logic, auditability, and handoff notes.

Workflow hardening sprint

AUD 5,000-15,000+

A deeper sprint for teams that already have a pilot and need integration work, policy layers, operator review paths, or production hardening.

The named offer: governed workflow build

This is the client-facing version of the Portarium plus OpenClaw pattern. It is not “an autonomous agent for your whole business.” It is one bounded workflow designed so the system can do useful work inside explicit approval and review boundaries.

The offer is derived from the same pattern I am building on my own stack: OpenClaw as operator, Portarium as governance layer.
I already use that shape for bounded inquiry handling, draft preparation, and audit-trail coverage instead of treating “autonomy” as a marketing claim.
That makes the consulting offer practical: start with one workflow, make it reviewable, and expand only when the first slice becomes boringly reliable.

Typical delivery includes

  • workflow mapping and failure-mode review
  • tool and integration design
  • validation and approval boundaries
  • audit and observability hooks
  • implementation notes and handoff documentation
Read the case study

How I work

The goal is clarity first, then delivery. I do not sell mystery-box AI implementation.

01

Discovery

We define the workflow, constraints, approvals, and failure modes before any implementation work starts.

02

Scope

You get a concrete plan with deliverables, milestones, and the workflow boundaries that matter operationally.

03

Build

I implement the workflow with tests, safety rails, observability, and regular demos instead of vague status updates.

04

Handoff

The result ships with documentation, operational notes, and a clear line between automated actions and human approval.

FAQ

Who is this for?

Founders, operators, and small teams who need practical AI workflow design and implementation, especially when a pilot already exists but is too fragile to trust.

What kinds of systems do you build?

Governed AI workflows, agent systems, automation pipelines, product infrastructure, and the reliability layer around them: validation, approvals, tests, and observability.

Do you only do consulting?

Consulting is the lead offer. Technical tutoring and AI workflow coaching still exist, but they are secondary paths and are scoped differently.

How do discovery calls work right now?

Discovery calls run through a first-party request flow. You send the workflow details and availability, then I confirm the time directly by email if it is a fit.

Start with a concrete workflow

If you have one broken workflow, one unreliable agent pilot, or one manual bottleneck that keeps resurfacing, that is enough to start.

Newsletter

Short notes on building AI agents in production.

One email when something worth sharing ships. No fluff, no daily cadence, no recycled growth-thread noise.

Primary use: consulting updates, governed AI workflow lessons, and major project writeups.