AI Automation Consultant: Add AI Without Breaking What Works

Most AI automation pitches skip the part where they tell you what can go wrong. That’s the part I start with.

I’ve managed $11M+ in ad spend across 200+ accounts. I run ads every day. And the reason I’m careful about AI touching live campaigns isn’t that AI is bad — it’s that I’ve seen what happens when something goes wrong inside an ad account, and the recovery cost is real. Banned accounts. Campaigns that ran off a cliff because a setting changed at the wrong time. Budget that evaporated overnight. The platforms are unforgiving. You don’t get a warning before the damage is done.

So when someone asks me to automate something with AI, the first question I ask isn’t “can we build this?” It’s “should we?”

The Guardrails Thesis

There’s a clean line between automation that’s safe and automation that’s reckless, and it mostly comes down to read vs. write access.

Safe to automate: reading, querying, reporting, scoring. Pull your data. Summarize it. Alert you when something looks off. Give you a natural-language interface to your own ad account so you can ask “how did Meta perform last week compared to the week before” and get a straight answer instead of digging through dashboards. That’s genuinely useful, and if something in that layer breaks, the worst case is a bad report.

Reckless: hooking an AI directly to your ad account with write access — letting it change bids, pause campaigns, edit ad copy, adjust budgets on live accounts. You can get banned. You can feed it the wrong context and it adjusts a setting you didn’t mean to touch. The model might behave differently than you expected and you won’t know until you check the account the next morning. Mature businesses understand this instinctively: “new things are cool, but we want to protect what we’ve already built and add interesting layers.” That’s exactly the right framing.

The consultant’s job — my job — is knowing which layer you’re in and what the real exposure is before anything gets built.

What I’ve Actually Built

The proof is in the builds, not the pitch. Here’s what’s already shipped:

Cross-account Slack bot — a client with three ad accounts (two Google, one Meta) can now query all of them in plain English from Slack. “What’s my cost per lead this week broken out by campaign?” answered in seconds. No dashboards, no exports, no waiting for a report. Read-only access throughout — the accounts are never touched.

Automated Monday reporting — reads the account data, writes the weekly email, sends it to the client. Which keywords are working, what the trend looks like, what to watch. Sounds like a human wrote it because it’s built around the media buyer’s actual voice.

Account scoring system — reads campaign and keyword data across accounts, scores everything, surfaces which accounts need attention. The practical effect: a media buyer can manage significantly more accounts without manual daily checks on every one of them.

These aren’t demos. They’re in production. The same infrastructure is what I’d be building for you.

Who This Is For

You’re spending $100+/day on ads — probably more, especially if you’re in lead gen where the data gets dense fast. You want a pulse on what’s happening without learning to dig through Google Ads and Meta interfaces every morning. You may have an agency or you may be running things yourself, but either way you want leverage — more signal, less noise, faster answers.

And critically: you’re not trying to take shortcuts that could blow up the account. You want automation that’s genuinely useful, not automation that introduces new risk.

If that’s where you are, this is what I do.

What an Engagement Looks Like

It starts with a conversation. Tell me your stack — what platforms you’re on, what tools you’re using, what you wish was automated. I’ll tell you what’s actually worth building, what’s risky, and what’s a waste of money. No obligation in that first conversation.

If something’s worth building, we build it. Build fees run $500–$2,000 depending on scope. Hosting and support after that is $100–$300/month — that covers keeping the infrastructure running and email support when something needs attention.

For context: the Slack bot that queries three ad accounts in plain English landed at the lower end of that range. More complex builds with CRM integrations, custom reporting logic, or multi-account scoring land toward the top.

If you’d rather have the plan than the build — if your team wants to take it in-house or you just want a second opinion on what’s safe — I can do that too. We map out what’s worth building, what the risks are, and hand you a clear spec.

Why an Ads-Specific Consultant vs. a General AI Shop

I’m not a software architect. I’m not building customer service chatbots or enterprise dev tools. What I do is narrow: AI that touches ads data, marketing data, leads, KPIs. That scope limit is the point.

A general AI automation agency can build you a lot of things. But if you’re spending real money on ads and want automation built by someone who understands the account structures, the platform rules, the ways accounts get flagged, and the data models well enough to build useful tooling on top of them — that’s a different conversation than a typical AI implementation services engagement.

The risk profile of ads automation is specific. The data structures are specific. What “useful” looks like when you’re managing campaigns is different from what it looks like in a CRM workflow. I build for that context because I live in it.

If you want custom AI agents that interface with your ad data, you want someone who can say “don’t hook it to your live account that way” and actually explain why — not someone who’ll build whatever you spec and hand it back.

That’s the difference.

Want AI on your ads data — without risking your accounts?

Custom builds from $500. Hosting + support from $100/month. Based in Albuquerque, working with businesses nationwide.

→ Tell me what you want to build