Current-state workflow map
Actors, systems, inputs, outputs, delays, rework, decision points, and the manual process that remains available as fallback.
Controlled AI Workflow Automation
Automate one important workflow without losing oversight, evidence, or a safe fallback.
A bounded design-and-implementation engagement for teams that want practical AI assistance, but also need the workflow to remain understandable, testable, reviewable, and maintainable.
Fixed-scope pilotOne bounded workflowHuman review and fallbackPricing confirmed after short inquiry
Why this exists
Useful automation needs more than a model connected to a form.
Many workflow projects begin with a tool and search for a problem. This engagement begins with the work itself: where time is lost, which decisions are repeatable, what information is authoritative, which errors matter, and where a person must remain accountable.
The result may combine deterministic rules, APIs, databases, retrieval, and AI. AI is used only where it adds enough value to justify its uncertainty and operating burden.
Strong fit when
- The workflow is repetitive, document-heavy, or information-heavy
- A knowledgeable person can review outputs and define unacceptable errors
- Current effort, delay, rework, or quality can be measured
- The workflow can retain a manual fallback while the pilot is evaluated
Controlled implementation
A working pilot with an operating record
The engagement is designed to leave the team with a functioning bounded workflow and enough evidence to understand how it should be used.
Automation suitability assessment
Separate steps that belong to deterministic automation, AI assistance, human judgment, or deliberate non-automation.
Target workflow and control design
AI and human responsibilities, approved sources, permissions, review gates, exception states, escalation routes, and recovery behaviour.
Working bounded pilot
A functional implementation using the smallest practical combination of scripts, APIs, databases, workflow tools, retrieval, and models.
Acceptance and failure tests
Expected cases, missing or malformed inputs, conflicting sources, unsupported outputs, duplicate processing, tool failures, overrides, and rollback.
Handover and operating package
System map, operating instructions, configuration record, test results, known limitations, ownership, maintenance needs, and reassessment triggers.
Quality standard
The workflow should leave enough evidence to explain what happened.
Where appropriate, the pilot retains the input or source reference, workflow version, model or rule configuration, output, exceptions, reviewer action, override, and final disposition. The required record is proportional to the decision and risk rather than copied from a generic compliance checklist.
Every pilot should define
- What the automation is allowed to do
- When a human must review, approve, or intervene
- How missing, conflicting, or out-of-scope information is handled
- How the team returns to a manual process when the system is unavailable or uncertain
Self-built demonstration · not client work
Controls before the model, in a demo you can try.
I built the Biotech RAG Assistant to test how a document assistant should behave before a language model is added. It answers only from approved, effective versions of a synthetic document set, cites the exact source section, and refuses when the documents do not support an answer.
Try the obsolete-document trap. The assistant refuses, flags the question for review, and names the retired SOP that the same search would have answered from without its status gate. It narrows and cites. A qualified person still decides.
Scope boundary
A pilot, not an open-ended transformation program
The engagement remains commercially workable by freezing one workflow, a limited system boundary, and explicit acceptance criteria.
Normally included
- One workflow with a defined start and end
- One primary workflow owner and a limited stakeholder group
- A limited set of approved systems and sources
- One pilot implementation and acceptance-test package
- Handover documentation and a defined review period
Excluded unless separately quoted
- Company-wide AI strategy or multi-department transformation
- Large custom applications or extensive user-interface development
- Formal computer-system validation, legal advice, or security certification
- High-risk autonomous decisions without meaningful human control
- Unlimited integrations, revisions, training, or ongoing support
Review or build?
Design sprint first, or build with controls from the start?
The Controlled Automation Readiness Sprint is the paid design sprint for an existing workflow: map how the work actually runs, separate authoritative records from working copies, mark the judgment and exception points, and set the automation boundary. No production build sits inside that fee. Controlled AI Workflow Automation is a separately scoped design-and-implementation pilot for a new or revised bounded workflow, with those controls built in.
When the current process is poorly defined or carries unresolved record-chain risk, the design sprint is usually the correct first step. Both use the same method, Workflow Evidence Hardening. You do not buy it separately.
Inquiry outcomes may include
Proceed with a pilot: the workflow is bounded, valuable, and reviewable.
Clean up the process first: ownership, source data, or exceptions are too unstable to automate responsibly.
Use conventional automation: rules, lookups, scripts, or database changes can solve the problem more reliably than AI.
Do not automate: the risk, ambiguity, or maintenance burden exceeds the likely value.
For implementation partners
Your clients do not buy the demo. They buy what happens after the demo.
You ship the n8n or agent workflow and it works. Then the client asks the questions that decide the renewal: what happens when an input is malformed, when the same record processes twice, when the model returns something the sources do not support, when nobody can say who approved an output. I do the verification layer that answers those questions before the client asks them.
What I take on
- Failure-mode requirements: what breaks, what it breaks, and what the system must do instead
- Acceptance and failure tests: malformed inputs, conflicting sources, duplicate processing, tool failures, overrides, rollback
- Review gates, human handoff points, and fallback behavior, specified before build
- A client-ready evidence package: what the automation may do, what it refused, what a reviewer approved, where the limits are
Strong fit when
- You deliver workflow or AI automation to clients who operate under scrutiny
- Your team builds fast and needs verification owned without slowing delivery
- The engagement would survive a client asking you to prove it
Not a fit when
- You need another builder adding capacity on the happy path
- The client has no reviewer and no appetite for one
One engagement at a time, scoped before work starts. The evidence package carries my name on the verification work, which means I only sign what I actually checked.
If the build is done and the proof is not, send a short note: what was built, who reviews it, and what the client will ask to see. I will tell you whether the verification fits in one engagement.
Bring one repetitive workflow, not a request to “add AI.”
A short inquiry tests fit. Free structured diagnostics stay on the one-workflow evidence path; automation is separately scoped if justified.