Every engagement starts with understanding the problem — and ends with a measurable result.
How a mid-size contractor eliminated $420K in annual waste by redesigning their estimating and bidding process — no AI required.
Hargrove was winning fewer bids despite growing demand. Their estimating process relied on spreadsheets, tribal knowledge, and guesswork. Project managers were spending 15+ hours per bid, frequently underpricing jobs or losing because they were too slow to respond. They had no data on why they won or lost.
We analyzed 3 years of bid history — wins, losses, margins, timelines — and built a probability model showing exactly which job types, sizes, and clients they had the highest win rate and margin on. Then we redesigned their entire estimating workflow: standardized templates, a pricing framework based on historical data, and a scoring system to prioritize high-probability bids.
Bid turnaround dropped from 5 days to under 2. Their win rate jumped 31% because they were bidding smarter — focusing on jobs they were statistically most likely to win at the best margins. The estimating team went from reactive to strategic. No AI, no new software — just better data and a better process.
"We were bidding on everything and winning less. Edge showed us the data on which jobs we actually win — and now we bid half as much and make more money."— Tom Hargrove, President
How a youth sports training facility used data modeling to optimize scheduling, pricing, and athlete retention — increasing revenue 38% in one season.
Coastal ran 200+ training sessions per week across baseball, basketball, and soccer but had no system tying it together. Scheduling was done on whiteboards, pricing was flat across every time slot, and they were losing 40% of athletes between seasons with no insight into why. Peak hours were overbooked while off-peak slots sat empty.
We analyzed 18 months of booking data, athlete attendance patterns, and churn timing. We built a custom scheduling and management platform with dynamic pricing that charged more for peak slots and less for off-peak — balancing demand across the week. We also built a retention model that flagged at-risk athletes based on attendance drops, giving coaches a chance to intervene before they quit.
Facility utilization nearly doubled as off-peak slots filled up. Athlete retention jumped from 60% to 91% because coaches could catch disengagement early. Revenue grew 38% in a single season — not from more athletes, but from smarter pricing, higher retention, and better use of existing capacity.
"We were running a million-dollar facility on spreadsheets and gut feel. Edge gave us a system that actually shows us what's working and what's not — and our revenue proves it."— Mike Trevino, Owner
How a DTC brand used predictive analytics to optimize inventory — cutting stockouts by 78% and reducing overstock by $200K.
Meridian sold 800+ SKUs across multiple channels and had no idea what to order or when. They were constantly either out of stock on bestsellers — losing sales — or sitting on $200K+ in slow-moving inventory eating warehouse costs. Their ordering was based on "gut feel and whatever sold last month."
We built a demand forecasting model using 2 years of sales data, seasonality patterns, marketing spend, and channel-level trends. The model predicted demand per SKU per week, factored in lead times, and generated optimized purchase orders automatically. We also built a dashboard that gave the buying team real-time visibility into what to order, when, and how much.
Stockouts dropped 78% in the first quarter. Dead inventory was cleared and the reorder model prevented it from building back up — freeing $200K in working capital. Gross margin improved 23% because they stopped panic-ordering at premium shipping rates and discounting overstock to clear it.
"We went from guessing to knowing. The model tells us exactly what to buy and when — we haven't had a major stockout in 4 months."— Ryan Choi, CEO
Book a free consultation and we'll dig into your biggest challenge — then show you what the data says about solving it.