I've made 350k+ in AI Consulting and the money isn't in agents. It's largely in data plumbing.

Hey All - I wanted to share my experience over the past few years of doing automation consulting part time (while still having a 9-5). To start, I come from a cloud / platform engineering and development background working with Fortune 500 financial services companies, so when I saw the multiplier that things like Claude Code (when it first came out) paired with true best practices and principles could do, I was totally hooked.

To be clear, everything I'm about to go over is just my experience and what I focus on.

I started out like a lot of folks do targeting small businesses trying to help them automate their processes and procedures. It was painful. Most only loosely knew what automation was and what automations were all about. This was around 2023 so it was still earlier days for things like ChatGPT, etc. but I found that for smaller businesses they either:

didn't have the money to spend on the monthly retainer I was charging at the time (3k/month) or

we're not organized enough to even articulate what they needed automated or understood what was "automatable".

It made me realize there's probably a good reason why these businesses were small and were staying small. They didn't have systems in place to help them grow / scale and while that's something I could offer, if they didn't want to do it on their own fruition it's not something I am going to be able to force through. TL;DR - don't force systems on people who weren't already building them for themselves.

Over time I found that I continued to land larger, yet still medium sized clients (5M+ / year revenue). Think household names, but in smaller spaces / industries. Every single one of them had the same problem. They wanted to use AI but found the results either to be really generic and not specific to their business, or that the key use cases required a lot of tools that didn't talk with each other well. Basically, they had no data footprint (ingestion, storage, governance, transformation, etc.)

I've now worked with 10+ businesses in multiple industries to build out an AI data platform of sorts. Think connectors via API to all their core tools and services they use that regularly ingest that data, normalize it, and write it to AWS S3 that has a data catalog of all the data being ingested on a regular basis.

It's not sexy work, but it created the foundation for every other automation use case that we've worked on together. Getting familiar with their data, their core services, etc. allowed me to build out automations exponentially faster as I never had to build out a custom integrations to get the data, figure out where I was going to land it, etc.

Questions around churn, ad spend, CAC, etc. that were previously a pain in the ass to answer became super easy when you could just point Codex and Claude Code at the data lake in AWS and let it loose. In turn, this also has lead to rapid and ongoing development of dashboards because they're so quick and easy to build once all the data is accessible.

Just now after months of getting all the foundational platform tooling and infra setup are we getting into more truly agentic work. The real kicker though is that people think they need agents and agentic work, when in reality all they are looking for as a starting point is a better understanding of what is happening in their business at any given time and how to connect the dots between those data points.

The tech stack I use is AWS based, but it doesn't need to be. I use AWS because I've used it for years and have gotten quite good at using it in a cost effective way. It also allows me to use pretty much one platform for all my work which has a ton of integration upsides.

At a high level here's what I use:

Lambdas / ECS tasks for data ingestion

S3 for Data Lake Storage

Athena for data querying

Glue for data cataloging

Cognito for Auth + Google Workspace Auth

CloudWatch for Logging + Alerting

Secrets Manager for API keys

CloudFront for hosting custom web frontends for dashboards and driving consumption

Github Actions + Cloudformation / CDK for deploying

I'm super big on making sure that this is enterprise level infra and configuration because an automation you can't trust is just AI slop at the end of the day.

For clients I've been doing this work with, most are paying between $7.5k - $15k a month for my services and paying for the infrastructure under their own AWS account separately. The work isn't necessarily "easy" for someone who doesn't have cloud experience so that is one of the caveats in this whole thing, but it's consistent and sticky as hell. People keep you around because they constantly want new reports / functionality and because you need to be there when things break.

I've largely automated a lot of this with automatic recon of failed jobs, reporting dashboards to track and trigger alerts when failures occur, and runbooks to keep things moving along but find this to be a pretty easily repeatable model.

The best part about this offering is that it truly does need to be custom. There aren't any clean off the shelf solutions that companies can use to do this. The connectors for various services to do ingestions are repeatable too.

Anyway, that's my pitch for selling the less sexy stuff like the AI data pipelines and what my experience is with it all. The need for this will not go away and any pushback around "just use MCPs" also shows a lack in understanding of when you need deterministic workloads and what underlying infra is needed to run such workloads.

Hopefully this was helpful to folks and is a breath of fresh for folks that feel like the only way to make money in this space is AI phone agents for $300 a month (not knocking this, just what I'm seeing a lot of).

I'm happy to answer any questions around this all and also regularly put out education content on the concepts I'm building / deploying for clients.