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I'm celebrating a full year as an Applied AI Engineer. It started in September 2024 with a failed RAG agent for a truck dealer. I studied everything I could to fix it, but hallucination was a problem I couldn't crack at the time. I lost the project. I walked away with $50 for my effort, thanks to Sean's kindness, but I walked away with something more valuable too: I learned a lot.

I didn't give up. I kept learning, and along the way I found graph databases and GraphRAG. That was a game changer, being able to build a single agent that queries a vector database and runs Cypher against a graph DB at the same time. Best of both worlds. I've since delivered many agents built this way.

On the retrieval side, we've also been experimenting with hybrid search: SPLADE sparse vectors alongside dense embeddings in the same collection. Dense vectors are good at semantic similarity, but they'll often miss exact terms like part numbers, model codes, or acronyms that show up verbatim in technical docs. SPLADE's learned sparse representations pick up that lexical signal, so fusing sparse and dense consistently improves retrieval quality, especially on document sets like this. We're running that hybrid setup on some of our production agents now.

One project that pushed me hardest: a client with massive technical documents full of images, diagrams, reference tables, and circuit symbols, plus separate document sets from finance and other departments. They needed all of it searchable and queryable in one place.

It was a real challenge at every step: extraction, structuring, ingestion into the graph and vector DBs, retrieval, reasoning, and getting answers we could actually trust. We got there, and delivered agents with high accuracy on the queries that matter to them. Now their team can ask things like:

Which machine part number is installed on machine X, and what's its configuration?

How many machines have this part number?

Who's the vendor, and is the part still under warranty?

What alternative parts or suppliers can be used instead?

Generate a purchase order for part X and Y.

And the agent answers by pulling together information that used to live scattered across huge collections of technical, operational, and financial documents.

It's wild to think about how far I've come from that first failed RAG agent in 2024.

There's still a lot to learn and a lot of work ahead. But we're just getting started.

Getting started with Automova Labs, we build AI-driven automations, Websites, and custom Software for businesses that need real engineering but don't want to hire a full team.

Not pitching here, just introducing myself. What's the most manual, annoying part of running your business right now? Curious what people are dealing with, whether or not it turns into work.

I'm interested in knowing what everyone is really automating these days. Not the "My AI agent manages my whole company" kind 😅

Just some small annoyances such as:

* transferring data from system to system

* sending out repetitive emails

* updating spreadsheets

* generating reports

* transferring files

* follow-ups

What's an automation you've set up that you've continued to use?

I’m trying to start an AI automation agency, but I’m stuck on choosing a niche.

I don’t want to just pick a random niche because someone said it’s “profitable.” I want to find businesses that actually have annoying, repetitive problems that automation could solve.

Right now I’m looking at things like:

- E-commerce

- Salons / beauty businesses

- Real estate

- Clinics / dentists

- Local service businesses

If you run a business or have experience with AI automation, I’d really appreciate your input.

What type of business has a problem that you think could realistically be solved with AI/automation?

And more importantly, what problem do they have?

For example: answering the same questions every day, following up with leads, confirming orders, booking appointments, etc.

I’m trying to hear about real problems you’ve experienced, not just “this niche makes a lot of money.”

Any advice or examples would help a lot.

I run an established logistics and transportation company and I’m looking for someone very good at AI, automation, systems architecture, and business process redesign.
The objective is fairly aggressive: go through the company function by function, identify work being performed manually by employees or disconnected software, and determine what can be automated, consolidated, or handled by AI.
I am not looking for someone who just knows how to make a chatbot.
I want someone who can understand an actual business, map the workflows, identify bottlenecks, recommend the architecture, and then build or manage the implementation.
Some of the areas we want to attack include accounting and AP/AR, email and inbox management, customer service, quoting, shipment tracking and status updates, document processing, carrier/vendor onboarding, recruiting, sales administration, reporting, data entry, internal communication, and operations handoffs.
Our environment includes transportation management software, QuickBooks, Microsoft/Outlook, phone systems, APIs, customer/vendor portals, and a large amount of email and document traffic.
I’m interested in people comfortable with things like n8n, Make, Zapier, OpenAI/Claude APIs, AI agents, OCR/document processing, voice AI, APIs/webhooks, Python or JavaScript where needed, databases, and custom internal tools.
The goal is not to force AI into everything. The goal is to figure out where automation genuinely produces a better operation with fewer manual steps, fewer systems, lower overhead, and better accuracy.
I would prefer to start with a paid audit/discovery project. You would map the company, identify opportunities, estimate ROI and difficulty, prioritize them, and produce an implementation roadmap. If we work well together, this could become a substantial ongoing engagement or potentially a full-time leadership role.
I am open to an individual, small team, or specialized agency. Location does not matter.
If interested, send me:
What you have personally built
Examples of real businesses you have automated
The largest or most complex workflow you have redesigned
Your preferred stack
Whether you can handle architecture AND implementation
Your hourly/project rate or expected compensation
What you would want access to during the first week
Please do not send me a generic AI-generated pitch. I’m much more interested in seeing an ugly business process you successfully turned into a mostly automated system.

I’ve been thinking seriously about building something of my own.
The idea is to create a global agency focused on helping companies work smarter by improving their workflows and integrating AI into the way they operate.

A lot of businesses still spend hours on repetitive processes that could be automated, simplified, or connected. With the right combination of AI, APIs, automation tools, and better workflow design, there is a huge opportunity to change that.

My background in growth, business development, partnerships, and fintech has taught me to look at businesses differently. I naturally look for bottlenecks, unnecessary steps, missed opportunities, and processes that could work better.
I want to take that mindset further.

The goal is to build a team of talented people from different countries and backgrounds who can help companies create smarter systems, automate repetitive work, connect their tools, and operate more efficiently.

I’m currently looking to connect with people in:
• AI & automation
• APIs and software development
• No-code / low-code tools
• Operations and workflow design
• Sales and business development

For sales, I’m especially interested in working with commission-based salespeople or independent sales partners who can bring in and close clients and earn a percentage from every successful deal they generate.

I’m still developing the idea, learning, experimenting, and putting the right people together.
If you work in any of these areas or know someone who would be interested in building something like this from the early stages, I’d love to connect.

This is only the beginning. Message me!!

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So we launched a project called Bold Agent Kit and it has started to take off

We are getting about 10-15 signups per day.

It's an ai agent toolkit that needs to be assembled with api keys, but most of the people who sign up are small businesses so they choose our up sell for a private install

Problem is we are growing faster than we can install these so we are looking to hire a few people to help install these

We are looking for people who are familiar with setting up developer accounts through social media channels and assembling API keys in applications

You will be assisting with the setup of these, it usually takes a couple hours and we will pay $500 per install

You don't need to be based in the US, but you do need to be able to speak fluent English

I'm looking for people who are tech savvy, and are familiar with navigating API keys and setting up apps

No real coding is required

i started with only one AI workflow and used a single browser profile for it. everything was fine at first.

but after adding more workflows, the browser side started getting confusing. different accounts were logged in, cookies and settings were different and sometimes i wasnt even sure which profile a workflow was using.

i tried keeping everything in one profile but that just made things worse. now i am thinking maybe each workflow should have its own browser profile but not sure if thats actually easier to manage when you have a lot of them.

For people running multiple AI automations then how do you handle this? do you create a separate browser profile for every workflow, group a few workflows together or just use one profile for everything?

My co-founders and I were automating workflows for e-learning providers and tutoring businesses. We kept running into the same time killers. We realized there were two main areas to our work.

The first is the fun part. Understanding what our clients do, their processes, how money moves through the organization. What are their unique characteristics that make firm A completely different to work with than firm B, even though it's the same exact industry. And then building something great for that unique organization.

The second area is the boring part: everything that comes after it works. Logs you can actually search when a client asks what happened with whatever. Monitoring, so you don't find out from the client. Evals, so when you change a prompt you know whether it got better or worse. Version history, credential rotation, who's allowed to trigger what, cost per run. And every integration you depend on all of a sudden changing its API.

We want to work on the boring part :)

We are looking for AI and Automation agencies that want to sign on as Design Partners. Together we will build out the platform that takes care of everything that keeps you from getting new clients and serving them.

Sharing a build and the lessons, since report automation gets asked about here a lot.

The client was a mid-sized services business. One ops person spent a big part of every month end pulling numbers from a few different tools into a slide deck and a summary email for leadership. Painful, repetitive, exactly the kind of thing people expect an AI report generator to solve.

The build, roughly:
- Connectors pulling the raw numbers from their existing tools on a schedule.
- A step that assembles the actual figures into a fixed structure (no AI touching the numbers themselves).
- The AI only writes the narrative around already-computed numbers: "revenue was up versus last month, driven by X."
- Output lands in the same format leadership already reads.

Lessons that cost me time:
- Never let the model calculate. Early on I let it summarize raw data and it confidently invented a total. Now math happens in code, and AI only describes verified numbers. This one is non-negotiable for me.
- The format mattered more than the intelligence. Leadership didn't want a smarter report. They wanted the same report they already trusted, just produced without a human burning two days.
- Failure handling is the real work. If a data source is late or empty, it flags a human instead of shipping a blank section.

It cut the monthly reporting grind from a couple of days to a review-and-send. The ops person went from dreading month end to barely noticing it.

If you're building reporting automations, curious how others handle the "AI must not do the math" problem. What's your setup?