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Client names are kept private. Every example below is a working tool, built on systems the business already had.
Client: a Des Moines nonprofit farm program with growers from many countries.
The problem: Lab soil tests for 15 plots came back as 30-plus pages of numbers. Growers needed to know what to spread, how much and when, often in a second language.
What I built: A two-part tool. Growers pick their plot, their crops (searchable by the names they use) and their area, and get one plan card with amounts in pounds and ounces and a read-aloud option. Staff get a view that shows the source behind every number. Where published university guidance doesn't support an amount, it says ask staff instead of guessing.
Where it stands: In beta, under expert review. Covers 15 tested plots, about 175 crops and 31 fertilizer products.
Client: a Midwest manufacturer selling to government, school and institutional buyers.
The problem: Bids were spread across federal, state and cooperative purchasing sites. Someone checked them by hand when they had time, so deadlines slipped and good-fit bids went unseen.
What I built: A scheduled search that pulls new opportunities every day, filters them by product category and keywords, scores how well each fits, and sends a short weekly digest. A dashboard tracks deadlines, documents and which bids the team chose to pursue.
What changed: Bid review went from a scramble across a dozen sites to a short, ranked list each week, and fewer deadlines were missed.
Client: a Midwest manufacturer with commodity-heavy inputs.
The problem: Raw materials are the biggest line on the cost sheet, and price moves showed up in margin a quarter late. Pricing decisions leaned on old supplier quotes.
What I built: A tracker that pulls published commodity and input price indexes every week, charts the trend, and flags moves past a set threshold. It translates each move into estimated cost impact on the top products, so the numbers mean something to sales and finance.
What changed: Pricing and purchasing got an early read on cost moves, so conversations about price increases started weeks sooner.
Client: a Midwest manufacturer.
The problem: The forecast was a spreadsheet built from rep opinions. The owners wanted a number they could defend in a board meeting and a clear view of what was driving it.
What I built: A model built from order history, seasonality and open orders that produces a low, expected and high range instead of one number. It breaks the change down by top customers and product lines and refreshes monthly.
What changed: Board reviews spent less time arguing about the number and more time on the customers and products behind it.
Client: a Midwest manufacturer selling online and through distributors.
The problem: Competitor prices and stock levels changed weekly across dozens of retailer and distributor sites. The team heard about price cuts from customers, after the deal was lost.
What I built: A monitor that checks matched competitor products on a schedule, records price and availability, flags drops and stockouts, and sends a weekly summary with the changes that matter.
What changed: The team sees competitor moves the same week and can respond on price or push product when a competitor runs out.
Shipping dashboard: freight cost by lane and carrier in one view, so quoting and carrier choices use current numbers.
Prospect research pipeline: turns a list of target companies into researched accounts with the right contacts, recent news and talking points.
Sales call prep: a one-page brief on any account before a meeting, covering history, open issues and what to ask.
