Operations Engine

Make your workflows AI-ready before you automate them.

We help founder-led teams identify which workflows are ready for AI, which need operational cleanup first, and which should not be automated at all.

See the readiness framework
Why AI projects fail in SMBs

Most AI projects do not fail on the technology.

They fail on the operations underneath it. The same seven gaps show up almost every time.

01
The process is not clearly defined, so the automation guesses at the work.
02
Ownership is unclear, so nobody is accountable when the output is wrong.
03
Exceptions are undocumented, so the edge cases break it in week two.
04
Inputs are messy, so the system automates the mess at speed.
05
Success metrics are missing, so nobody can say whether it is working.
06
Nobody owns maintenance, so it quietly degrades until someone turns it off.
07
The tool arrives before the operating rhythm, so the team works around it.

The tool is rarely the problem. The system around it is. AI works best when the work is already clear.

The automation readiness framework

Seven criteria decide whether a workflow is ready.

We score every workflow against the same checklist before a single tool is discussed. The score, not the hype, decides what happens next.

01
Volume

Does this happen often enough to matter? Automating a monthly task rarely pays back the build.

02
Stability

Is the process consistent enough? If it runs differently every week, there is nothing stable to automate.

03
Ownership

Does someone own the outcome? An automation without an owner is an orphan waiting to fail.

04
Inputs

Is the required data accessible and reliable? Rubbish in still means rubbish out, just faster.

05
Exceptions

Are the edge cases known and documented? The exceptions, not the happy path, are where automations break.

06
Risk

Is the impact of a failure acceptable? Some workflows can tolerate a wrong output. Others cannot.

07
Measurement

Can success be tracked? If you cannot measure the workflow today, you cannot tell whether the automation helped.

Score a workflow against all seven and it lands in one of three verdicts.

Verdict one
Automate now

High volume, stable, owned, measurable. We build it, test it against the edge cases, and hand it over with documentation.

Verdict two
Fix first

Worth automating, but the process, ownership or data is not ready. We clean up the operating layer, then automate on solid ground.

What we build

From audit to handover, in that order.

Every engagement starts with the audit and ends with your team running the result without us.

Workflow audit

We map how your core workflows actually run: steps, handoffs, inputs, owners and the exceptions nobody wrote down.

Automation readiness score

Every workflow scored against the seven criteria, with a clear verdict: automate now, fix first, or do not automate.

Tool recommendations

The right tools for your stack and your team, chosen on fit. We build on what you already use wherever possible.

Implementation roadmap

A sequenced plan with owners, timelines and dependencies, so the quick wins land first and nothing stalls halfway.

Automation build

Where the score says go, we build the automation, test it against the documented edge cases, and monitor the first runs.

Team handover and documentation

Your team learns how it works, how to maintain it, and what to do when it breaks. No dependency on us to keep it running.

We are independent and tool-agnostic. No referral deals, no commissions. Every recommendation is based only on fit for your business.

Built by an operator who shipped production AI systems inside a live business, including a feasibility agent that compressed a multi-week bottleneck into a 2 to 5 minute check.

Read the founder story
When the workflow is not ready

If the audit says "fix first", the fix is usually the operating layer, not a better tool.

Unclear ownership, missing standards and invisible handoffs are Performance Engine problems. We fix those first, then automate on top of a system that holds.

See the Performance Engine