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Processed vehicles were passing through inspection with damage going undetected, resulting in downstream incident claims and penalty costs. Manual inspection was slow, inconsistent across operators, and created a bottleneck during peak volume.

Ran customer discovery sessions to map the current inspection workflow, quantify miss rates, and identify the highest-cost damage categories. Partnered with data science on training data strategy and defined success metrics weighted toward precision (false positives erode inspector trust). Built a human-in-the-loop review step so inspectors could validate and correct model outputs, creating a feedback loop that improved model accuracy over time.

Equipment processing centers were collecting scheduling and throughput data inconsistently across sites, making it impossible to forecast capacity or identify bottlenecks. Customer teams were double-booking slots and losing visibility into daily operations.

Interviewed operators at multiple sites to map real scheduling workflows vs. documented ones. Prioritized a minimal first release around the highest-friction touchpoint (slot booking) and layered in data capture incrementally to avoid overwhelming adoption. Partnered with change management to train users and track adoption metrics post-launch.

Freight invoices were being generated manually and validated against contracts by hand, causing delayed billing cycles, disputed charges, and working-capital drag. Finance teams spent hours per week reconciling discrepancies.

Mapped the existing invoice-to-payment flow to identify where manual work was introducing delay and error. Prioritized rules-based contract validation first, with exception routing for edge cases. Defined acceptance criteria around cycle time and dispute rate, and instrumented the rollout with dashboards for finance leadership.