Real feedback from businesses we have worked with. We have included project details where the client gave permission.
"We asked Trusted Ai Wave to build a demand-forecasting model for our 1,200-SKU catalogue. They delivered in five weeks. Our stock-out rate dropped from 9% to under 3% in the first quarter after launch, and overstock write-offs fell by £140k that same period."
"The NLP classifier they built for our customer-service inbox sorts 800 tickets a day into 14 categories with 94% accuracy. Before that, two people spent half their mornings doing it by hand. Those two now focus on the tickets that actually need a human."
"Honest about what would and would not work. They told us upfront that one of our three proposed use cases did not have enough training data, and suggested a cheaper rule-based approach for that one instead. Saved us about £25k."
"We needed a computer-vision system to inspect printed circuit boards on our line. They trained the model on 6,000 labelled images from our own production run and got false-reject rates below 0.5%. The system paid for itself in three months through reduced manual QA hours."
A mid-size e-commerce company selling home furnishings across the UK was repricing its 3,400 products manually every Monday. The merchandising team spent roughly six hours each week adjusting prices based on competitor screenshots and gut feel. Margins were inconsistent: some products were priced 15% below market, others 20% above, with no clear logic connecting the two.
We trained a gradient-boosted regression model on 14 months of sales data, competitor-price feeds and seasonal indicators. The model outputs a recommended price for each SKU every morning, along with a confidence score. Products with low confidence are flagged for human review instead of being repriced automatically.
The system runs as a scheduled job on AWS Lambda and writes prices directly to the client's Shopify store via the admin API. The total cloud cost is about £18 a month.
The merchandising team now spends Monday mornings reviewing the 8% of flagged SKUs rather than repricing the entire catalogue. Revenue per session increased because prices better reflected what customers were willing to pay, not what a competitor happened to charge last Thursday.
Aggregated across all completed engagements since 2018.