AI Automation & Infrastructure

Most companies have work that a person does every week because nobody ever sat down and automated it. Pulling the same report. Checking the same list. Copying data between two systems that were never introduced. It is not the hard part of the business, and it quietly costs more than anything on the invoice.

This is the other half of what I do. Where my IT consulting work keeps existing systems running, this side builds the parts that run themselves - and puts real checks around them so they stay trustworthy.

Find the Repetitive Work

The first job is not automation. It is finding out where the time actually goes, and being honest about which of it should be automated at all. Some tasks look repetitive and carry a judgment call in the middle. Those are the ones that go wrong quietly when you hand them to a machine.

I map the process as it really runs - not as the documentation describes it - and separate the work into three piles: automate it, leave it alone, and change the process so the work stops existing.

• Process mapping across the systems you already pay for
• Honest scoping - what automation saves, and what it costs to maintain
• Integration between tools that do not talk to each other
• Scheduled jobs that run without anyone remembering to start them
• Reporting that arrives on its own instead of being assembled

Put AI Where It Earns Its Keep

AI is very good at drafting, summarizing, classifying, and extracting - the work that is tedious rather than difficult. It is unreliable in exactly one way that matters: it will produce a confident, well-formatted answer that is wrong, and nothing about the output looks different when that happens.

So the automation is only half the job. The other half is the check. A safety net that verifies the format is correct will pass a false answer every time. The check has to test whether the claim holds up against the underlying data. I build that check first, and I prove it can fail before I trust it to pass.

• Identifying which tasks AI is actually suited to
• Drafting, summarizing, and classification built into existing workflows
• Verification steps that test the answer, not the formatting
• A human approval point wherever the cost of being wrong is high
• Spend limits and usage alerts, so a metered service cannot surprise you

Infrastructure That Ships Safely

Automation is worth nothing if the platform underneath it is fragile. Most of the outages I have dealt with were not caused by an attack or a hardware failure. They were caused by a release nobody verified, a backup that had never been restored, or a safety setting that was switched on and pointed at the wrong thing.

I set up the boring machinery that prevents those: an automatic health check after every release, an automatic rollback when that check fails, backups that get restored on a schedule rather than assumed, and monitoring that watches the thing customers actually touch.

• Automated testing and release checks before anything reaches production
• Automatic rollback when a release fails its health check
• Backups that are proven by restoring them, not by a green checkmark
• Monitoring and alerting that has been tested by making it fire
• Cloud cost control - spend caps, budget alerts, and removing what you stopped using
• Documentation your team can run without calling me

Write-Ups From This Work

These are the decisions and the mistakes behind the list above, written out in full.

See everything on the blog →

How This Usually Starts

Almost always with one process that everybody complains about. I look at that one, automate it end to end with the checks in place, and you get something working before we discuss anything larger. If it does not pay for itself, that is a cheap thing to find out.