Testimonials
What Our Clients Say
These aren’t just happy quotes — they’re results we delivered.
Read how we helped teams reduce complexity, accelerate delivery, and build with confidence under pressure.
Alex Meinders
Research Technician
Don helped us tackle a backlog of over 90,000 images from our camera trap network — a review process that would normally require multiple passes by volunteers and staff. He evaluated leading AI wildlife identification platforms, tested them against our ‘truth set,’ and identified the best options for accuracy, scalability, and ease of use. His work reduced the amount of manual review required while keeping quality and accuracy high, turning what felt like an impossible task into a process that allowed us to catch up on images for the first time ever — likely saving us weeks worth of work. We were so happy with Don’s solution that we have shared it in presentations to highlight how much more work we were able to accomplish.

Alex Meinders
Research Technician
Don helped us tackle a backlog of over 90,000 images from our camera trap network — a review process that would normally require multiple passes by volunteers and staff. He evaluated leading AI wildlife identification platforms, tested them against our ‘truth set,’ and identified the best options for accuracy, scalability, and ease of use. His work reduced the amount of manual review required while keeping quality and accuracy high, turning what felt like an impossible task into a process that allowed us to catch up on images for the first time ever — likely saving us weeks worth of work. We were so happy with Don’s solution that we have shared it in presentations to highlight how much more work we were able to accomplish.

Alex Meinders
Research Technician
Don helped us tackle a backlog of over 90,000 images from our camera trap network — a review process that would normally require multiple passes by volunteers and staff. He evaluated leading AI wildlife identification platforms, tested them against our ‘truth set,’ and identified the best options for accuracy, scalability, and ease of use. His work reduced the amount of manual review required while keeping quality and accuracy high, turning what felt like an impossible task into a process that allowed us to catch up on images for the first time ever — likely saving us weeks worth of work. We were so happy with Don’s solution that we have shared it in presentations to highlight how much more work we were able to accomplish.

Laura Taylor
VP Marketing and Product
Don turned a fuzzy scope into decisions we could stand behind. He ran discovery sessions with internal stakeholders, customers, and customers’ customers, documented requirements as high-level user stories, and prioritized them with MoSCoW scoring. He led an enterprise headless CMS evaluation with a defensible scoring model and clear trade-offs, then ran three vendor pilots with our team and executed his own technical POC to prove integration and feasibility. To align Product, Design, CS, and Engineering, he utilized an AI prototyping tool (Onlook) to generate two full-stack prototypes: one for the end-user experience and another for the business-user portal. This provided everyone with a working, end-to-end view of the product before the build. We ultimately chose to build in-house, faster and with fewer surprises, because the trade-offs were explicit and the MVP roadmap was clear.

Laura Taylor
VP Marketing and Product
Don turned a fuzzy scope into decisions we could stand behind. He ran discovery sessions with internal stakeholders, customers, and customers’ customers, documented requirements as high-level user stories, and prioritized them with MoSCoW scoring. He led an enterprise headless CMS evaluation with a defensible scoring model and clear trade-offs, then ran three vendor pilots with our team and executed his own technical POC to prove integration and feasibility. To align Product, Design, CS, and Engineering, he utilized an AI prototyping tool (Onlook) to generate two full-stack prototypes: one for the end-user experience and another for the business-user portal. This provided everyone with a working, end-to-end view of the product before the build. We ultimately chose to build in-house, faster and with fewer surprises, because the trade-offs were explicit and the MVP roadmap was clear.

Laura Taylor
VP Marketing and Product
Don turned a fuzzy scope into decisions we could stand behind. He ran discovery sessions with internal stakeholders, customers, and customers’ customers, documented requirements as high-level user stories, and prioritized them with MoSCoW scoring. He led an enterprise headless CMS evaluation with a defensible scoring model and clear trade-offs, then ran three vendor pilots with our team and executed his own technical POC to prove integration and feasibility. To align Product, Design, CS, and Engineering, he utilized an AI prototyping tool (Onlook) to generate two full-stack prototypes: one for the end-user experience and another for the business-user portal. This provided everyone with a working, end-to-end view of the product before the build. We ultimately chose to build in-house, faster and with fewer surprises, because the trade-offs were explicit and the MVP roadmap was clear.

Mike Gary
Sr. Director of Operations
For our ERP launch, we had to audit 20K+ images from 10 years of artwork, a job that would have taken weeks of manual work. Don built an image matching system that cross-referenced our files against our artwork list and flagged only what needed review. The result: 94% of our files matched automatically, with the human-review app used only for edge cases. It turned a tedious, weeks-long slog into a process we could finish in days.

Mike Gary
Sr. Director of Operations
For our ERP launch, we had to audit 20K+ images from 10 years of artwork, a job that would have taken weeks of manual work. Don built an image matching system that cross-referenced our files against our artwork list and flagged only what needed review. The result: 94% of our files matched automatically, with the human-review app used only for edge cases. It turned a tedious, weeks-long slog into a process we could finish in days.

Mike Gary
Sr. Director of Operations
For our ERP launch, we had to audit 20K+ images from 10 years of artwork, a job that would have taken weeks of manual work. Don built an image matching system that cross-referenced our files against our artwork list and flagged only what needed review. The result: 94% of our files matched automatically, with the human-review app used only for edge cases. It turned a tedious, weeks-long slog into a process we could finish in days.

Let’s Build What Matters — Now
We’ve cut delivery times by 90%+, freed 60% of team time, and launched MVPs in under 90 days. Let’s start yours now









Let’s Build What Matters — Now
We’ve cut delivery times by 90%+, freed 60% of team time, and launched MVPs in under 90 days. Let’s start yours now









Let’s Build What Matters — Now
We’ve cut delivery times by 90%+, freed 60% of team time, and launched MVPs in under 90 days. Let’s start yours now








