Okay so, you're probably here because you’ve got data, and you’ve heard 'AI' and 'machine learning' enough times to think, 'Maybe there's something in this for my business.' You're also probably a little wary, and that's smart. There's a lot of noise out there, and it can be tough to figure out what's real and what's just marketing. I get it.
What I do isn't about chasing the latest buzzword. It's about looking at your actual business problems – maybe you have too much inventory, or customers churning, or processes that just take too long – and seeing if a machine learning model or a data science approach can actually make a difference. I'm not gonna promise the moon, but I will promise an honest assessment and a straightforward path forward, for businesses right here in Miami Beach, Florida.
## What I actually do for Miami Beach clients
Mostly, I help businesses get more out of their data. That could mean building a predictive model that forecasts sales more accurately, so you're not overstocking or missing opportunities. Or it might be setting up an automated system that flags potential issues in your operations before they become big headaches. Think things like customer segmentation to personalize marketing, or optimizing pricing strategies based on market data.
I’m usually brought in for specific, scoped projects. It’s rarely about 'digital transformation' in some vague sense; it's more about 'can we predict which customers are likely to leave next quarter?' or 'how can we automate the categorization of these incoming documents?' I work with your existing data, clean it up, and then apply suitable machine learning techniques to get to a tangible outcome. My goal is to build something useful that your team can then maintain, or that I can help you maintain, without needing a whole new data science department.
## How a Miami Beach engagement usually goes
1. **Initial Chat & Discovery (1-2 weeks):** We start with a free 20-minute call to see if there’s even a potential fit. If there is, I'll propose a small, fixed-fee discovery phase. This is where I dig into your current data, systems, and processes. I'll ask a lot of questions. The output is a clear project proposal, outlining the problem, the proposed solution, the expected outcomes, and a detailed cost estimate. You'll know exactly what you're getting into.
2. **Data Prep & Model Building (4-8 weeks):** Once we agree on a project, I'll get to work. This involves a lot of data cleaning and engineering – frankly, it's often the bulk of the work. Then I'll select and train the appropriate machine learning models. I keep you in the loop with regular updates, showing you the progress and explaining things in plain English.
3. **Validation & Integration (2-4 weeks):** We'll test the model or solution rigorously with your team. This is where we make sure it actually works in your real-world environment and delivers on the agreed-upon objectives. Then, I'll help you integrate it into your existing workflows, whether that's through a simple dashboard or an API connection.
4. **Hand-off & Support (Ongoing):** After everything is up and running, I provide documentation and training for your team. My aim is for you to be self-sufficient, but I'm always available for ongoing support, maintenance, or further enhancements if needed. I don't just build it and disappear; I want to see you succeed.
## What it costs, roughly
I prefer fixed-fee engagements for defined projects. This way, you know the total cost upfront, and there are no surprises. For a typical pilot project – something that demonstrates the value of a machine learning model or data analysis – you're usually looking at a range from the mid-four figures to low-five figures. Larger, more complex implementations can certainly go higher, but we’ll always have a clear proposal with a clear price before any significant work begins. The discovery phase itself is a separate, smaller fixed fee.
## Who I'm usually NOT a fit for
I'm not the right choice if you're looking for a massive, multi-year 'digital transformation' project with a huge team. I'm a solo operator, remember. I also probably won't be a fit if you don't have any data to begin with, or if your data is so messy it would take a year just to clean it up before we could even start modeling. And if you're expecting instant, magical results without putting in some effort on your end to collaborate and provide context, then I'm probably not your guy. I also don't do 'pie-in-the-sky' research projects; I focus on practical applications with measurable business value.
## Getting in touch
If any of this sounds like what you need for your Miami Beach business, and you appreciate a straightforward approach, I'd genuinely like to hear from you. The best way to start is to book a 20-min call on my calendar. No pressure, just a chat to see if I can help.
FAQs — Miami Beach
How do I know if AI is worth it for my Miami Beach business?
The best way is to start with a clear business problem. Do you have data related to that problem? If so, there's a good chance AI or data science could offer a solution. A quick chat can help us figure this out.
Do you work with companies outside Miami Beach, Florida?
Yes, while I love working with local businesses, my services are available remotely to clients across Florida and beyond. Location isn't a barrier for most of my project types.
What does the first 2 weeks look like after I decide to work with you?
After an initial agreement, we'd start a discovery phase. I'll get access to relevant data, systems, and key people. My goal is to understand your operations thoroughly to propose the most effective path forward.
Can you integrate with my existing software?
I always aim to integrate solutions into your current tech stack as smoothly as possible. This often involves using APIs or working with common data formats to ensure compatibility with your existing software.
What kind of data do I need to have?
You typically need historical data related to the problem you're trying to solve. For example, if you want to predict sales, you'd need past sales figures, product information, and maybe marketing data. The more relevant data, the better.