Okay so, you're here because your Wichita business has data, and you suspect there's more value in it than you're currently getting. Maybe you've heard about machine learning and data science, and you're wondering if it's just for the big guys with huge budgets. I'm here to tell you it's not. I've spent years in the trenches, building and deploying models that actually make a difference to a company's bottom line. I'm not here to sell you a 'digital transformation journey' or some 'AI strategy roadmap' that sits on a shelf. I'm here to build stuff that works.
I like to think of myself as a kind of neighborhood shop for all things data and AI. I don't have a huge team, fancy offices, or a sales department. What I do have is a deep understanding of how to take raw data, clean it up, analyze it, and then build predictive models or automated systems that solve real business problems. I'm not trying to be everything to everyone; I'm good at what I do, and I'm upfront about what I can and can't do.
## What I actually do for Wichita clients
When a Wichita company comes to me, they usually have a specific problem in mind. Maybe they want to predict customer churn, optimize pricing, forecast sales more accurately, or automate a repetitive data-heavy task. I don't just talk about these things; I build them. This often looks like developing a custom machine learning model that takes your historical data and spits out predictions or recommendations. Think of it like having a super-smart employee who can analyze millions of data points in seconds.
Another common project involves setting up data pipelines and automation. For example, if you're manually pulling data from three different systems into a spreadsheet every week, I can build an automated solution that does it for you, cleans it, and presents it in a dashboard. This frees up your team to focus on what they do best, instead of being bogged down by data wrangling. I'm all about making your data work harder for you, so you don't have to.
## How a Wichita engagement usually goes
1. **Initial Chat & Discovery (1-2 weeks):** We start with a free, no-pressure 20-minute call to see if there's even a potential fit. If it seems like I can help, I'll then propose a more in-depth discovery phase. This usually involves me spending a week or two understanding your business, your data, and the specific problem you're trying to solve. I'll ask a lot of questions, look at your existing data, and identify key metrics. I'll then provide a concrete proposal outlining the project scope, deliverables, timeline, and fixed fee. 2. **Data Prep & Model Building (3-8 weeks):** Once we agree on a plan, I'll dive into your data. This is where the bulk of the work happens: cleaning, transforming, and engineering features from your raw data, then building and training the machine learning models. This is an iterative process, and I'll keep you updated regularly on progress, challenges, and any insights I uncover. 3. **Testing & Refinement (2-4 weeks):** After the initial model is built, we'll spend time testing it thoroughly with your team. We'll run it against real-world scenarios, gather feedback, and make adjustments to improve accuracy and ensure it meets your business needs. My goal is always a solution that is both technically sound and practically useful. 4. **Deployment & Handoff (1-2 weeks):** The final step is getting the solution into production, whether that's integrating a predictive model into your existing software, setting up an automated report, or delivering a standalone application. I'll make sure everything is running smoothly and provide documentation and training so your team can confidently use and maintain the new system. I'm not gonna just drop it on you and disappear.
## What it costs, roughly
I typically work on a fixed-fee basis for projects, which means you know the total cost upfront, no surprises. This isn't hourly billing where things can drag on indefinitely. For most practical machine learning or data automation projects, my fees usually range from, say, a few thousand dollars for a focused pilot up to the low five figures for more complex, multi-month engagements. It really depends on the scope and complexity, but I'll always give you a clear, detailed quote after our discovery phase. I try to be as transparent as possible, because nobody likes hidden costs.
## Who I'm usually NOT a fit for
I'm probably not the right fit if you're looking for a massive consulting firm with dozens of analysts and shiny reports that just tell you what you already know. I also tend not to be a good fit for businesses that don't have any data yet, or where the data is so messy it would take months just to clean up before any real analysis can begin. My sweet spot is working with Wichita businesses that have at least some historical data and a clearly defined problem they believe data could help solve. If you're just looking to 'do AI' because it's a buzzword, without a specific business goal, I'm probably not your guy. I prefer to work on projects where I can deliver tangible results within a few months, not years.
## Getting in touch
If anything I've said resonates with you, and you're running a business here in Wichita, Kansas, or nearby, I'd genuinely love to chat. The easiest way to get started is to book a 20-min call directly on my calendar. We can talk about your challenges, your data, and whether a focused machine learning or data science project might be the right next step for you. It's a quick, no-obligation conversation to see if I can help, and I promise to be honest if I'm not the right person for the job. Let's talk about what's possible.
FAQs — Wichita
How do I know if Machine Learning is worth it for my Wichita business?
The best way to figure this out is to identify a specific problem where better predictions or automation could save you money or make you more money. If you have historical data related to that problem, there's a good chance machine learning could help. We can explore this together on a call.
Do you only work with companies in Wichita, Kansas?
While I appreciate connecting with local Wichita businesses, I actually work with clients across the country. The principles of data science and machine learning apply universally, and remote collaboration makes it easy to work with anyone.
What does the first 2 weeks of an engagement typically look like?
The first 1-2 weeks are usually dedicated to a focused discovery phase. I'll get to know your business, dive into your existing data, and ask a lot of questions to fully understand the problem and define a clear project scope. This helps ensure we're on the same page before any real development begins.
I have a lot of data, but it's really messy. Can you still help?
Data cleaning and preparation is a huge part of any data science project. I can definitely help with messy data, though it might add to the project timeline and cost. The cleaner the data, the faster we can get to building and deploying a useful model.
What kind of commitment do I need to make on my end?
Your main commitment would be providing access to relevant data and making key stakeholders available for regular check-ins and feedback sessions. Your insights into your business are crucial for building a truly effective solution.