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Data and AI adoption, for people and teams

Technology built around your business.

AI, automation, data and custom software, implemented with you, not handed to you. Set up on your own machines, connected to the work you actually do, taught well enough that you run it without me, and improved as the business changes. Not a platform migration. Not a course. A partner who has done it for his own business first, and stays.

Two minutes A short welcome 2 min
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15+ Years in financial data analysis and cost estimation
5 hrs Returned to one client every week by a single automated step
1 chat That my own invoices, bank feeds, email and calendar answer to

Data foundations

Every business is somewhere on this road

Two ideas drive every engagement: know where you are, and fix the one constraint that is actually binding. Each stage below is a normal place to be, and each has a clear next step. The foundations come first. AI goes on top once they are there.

Stage 01

Experience-led

Decisions come from knowing the business cold.

You know the roads by heart. That knowledge is the asset. The next step is writing a little of it down, so it can be checked, shared and built on.

Stage 02

Data aware

The numbers are being collected.

A map in the glovebox, which is already more than most businesses have. The win is opening it before the trip instead of after.

Stage 03

Data informed

The big decisions are made with data.

Navigation for the important trips. The next gain is in the small daily calls, which add up to more than the big ones.

Stage 04

Data driven

Data is part of every decision, and the systems keep it fresh.

Live navigation. The route adjusts as conditions change, and every trip makes the next one better.

Most clients start at the first or second stage. That is the normal starting point, and it is where the fastest wins are.

Theory of Constraints

A system is limited by its weakest link. Strengthening any other link changes nothing. So I find the binding constraint first, and spend your budget there.

It's why my engagements tend to be smaller than clients expect, and why they pay back faster.

In practice

A real estate client was assigning costs to projects by hand. The constraint was not their software or their reporting. It was one manual step that had to happen before anything else could.

I automated that step first, then built the spend tracking and forecasting on top of it once the data was reliable. Their account of it is further down this page.

5 hrs Returned to the owner every week, in their own words
Email Calendar Bank feeds QuickBooks Sheets Postgres one chat jds_cash_position

AI, without the hype

I run my own business from one chat

Invoices, bank feeds, email, the calendar and the books are connected to one assistant. It prepares the next step on a schedule and waits for me to approve it. Nothing is sent without a yes.

It is the same setup I build for clients, and there is a recording of it rather than a promise about it.

Client story

What this looks like in practice

★★★★★
PG Living
Real estate investment firm
Started with A VBA routine to automate cost allocation
Grew into Spend tracking, revenue forecasting, and an automated offer calculator
Time returned At least five hours every week

We had a manual process for assigning costs to projects. JDS wrote a VBA routine that automated it, saving me at least five hours a week.

Once the process matured, JDS helped me create a dashboard to track spending by project and forecast revenue and expenses. With that in place, they began automating our “best offer” estimate using a proprietary formula and calculator.

Now they're helping me streamline project management. I can't wait to see what value they unlock next.

About

Who you'd actually be working with

Joy Data Solutions is one person and the AI he works with. Cost estimation, financial modeling, automation and adoption, for small businesses, real estate teams and nonprofits, with a low-risk way in.

Not sure where to start?

Most clients don't know which problem to hand me first. That's a normal place to begin, and the first call usually settles it.

01 — Low-risk entry Start with a single project and see the value before anything expands.
02 — Practical delivery I keep the big picture in view but work in short cycles, so something useful lands early.
Kevin Joy
Founder & principal consultant

Over fifteen years as a financial data analyst and cost estimator, evaluating the financial health of major contractors and federal programs, and presenting to senior government leadership.

Technical Python, R, SQL, Power BI, Qlik, Tableau, and applied machine learning — aggregating messy financial data and building models on top of it.
Education MS Operations Research, Kansas State University
Graduate Certificate in Data Science, Federal Postgraduate School
BS Operations Management, University of Maryland

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Thirty minutes, no charge. You'll leave with a straight answer about whether this is worth doing — even if the answer is no.

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Contact

Start with a conversation

Tell me what's not working. If I'm not the right fit I'll say so, and point you at whoever is.

You'll get A quick read on your current data maturity
You'll get At least one thing you can act on immediately
You'll get A rough roadmap, and an honest view on sequencing

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