These are real outcomes from real projects. We have changed company names where clients asked for confidentiality, but the numbers are exact.
We asked Trust AI Pursuit to build a demand-forecasting model for our 14 warehouse locations. Within five weeks the model was live, and our overstock costs dropped by 22 per cent in the first quarter. The team explained every decision in plain language, which made it easy to get buy-in from our board.
Our customer-support inbox was drowning. Trust AI Pursuit trained a classifier that routes tickets to the right department with 94 per cent accuracy. Response times fell from an average of 11 hours to under 3. Genuinely changed how our support team spends their day.
I was sceptical that AI could help a company our size (38 people). The discovery sprint proved me wrong. They identified a pattern in our warranty claims data that our quality team had missed for two years. The fix was straightforward once we knew where to look.
We needed number-plate recognition for a multi-storey car park with poor lighting and tight angles. Two other vendors told us the conditions were too difficult. Trust AI Pursuit delivered a system that reads plates correctly 97.6 per cent of the time, even at night. It paid for itself within four months through reduced barrier-staff costs.
A mid-size motor insurer in the Midlands processed around 1,200 damage claims per month. Each claim required a human assessor to review photos, cross-reference policy terms, and estimate repair costs. The average turnaround was 4.3 working days.
We built a two-stage pipeline. The first stage uses a convolutional neural network to detect damage type and severity from submitted photos. The second stage matches the detected damage against the claimant's policy coverage rules and generates a preliminary cost estimate.
Claims that the model classifies with high confidence (about 60 per cent of total volume) now go straight to the payment-approval queue. The remaining 40 per cent are flagged for human review, but the assessor receives the model's preliminary analysis, which cuts their review time roughly in half.
The project took seven weeks from kickoff to production. We continue to monitor model accuracy on a monthly retainer and have retrained the model twice as new vehicle types entered the insurer's portfolio.
Since 2021 we have completed 47 AI projects for UK businesses. Here is a summary of aggregate outcomes reported by clients at their six-month review.