Real Cases

Challenge: Manual rounds limited continuous vitals coverage; fall risk and infusion safety relied on intermittent observation.

Solution: IoT vitals telemetry, smart monitoring mattresses, mmWave touchless fall detection and smart IV management deployed across the ward.

Outcome: Closed-loop ward monitoring: automated vitals upload, proactive risk alerting, simplified nurse rounding, and a measurable lift in care-execution efficiency.

Challenge: Concept, packaging and price-tier validation hinged on slow recruitment of 30 in-depth interviewees, holding launch decisions for weeks.

Solution: iMario built a 2,000-strong synthetic panel from the brand's historical user archetypes — ages 25-45, novice to expert skincare users, plus tier 1-3 city male buyers. Three concurrent rounds tested packaging variants, feature ranking and price tiers, then a 10-person human IDI validated each key finding.

Outcome: Research cycle compressed from 5 weeks to under 24 hours, sample size grew from 30 to 2,000 respondents, cost dropped to one fifth.

Challenge: Sales teams need to practice high-stakes meetings with C-suite, department heads and procurement leaders who are notoriously hard to reach in advance.

Solution: iMario delivered LinkedIn-grounded synthetic personas mirroring the actual decision-maker profile, letting B2B sellers rehearse messaging and objection-handling against realistic stand-ins for their target accounts.

Outcome: WBR Insights now offers its enterprise clients a pre-meeting rehearsal layer, expanding access to hard-to-reach buyers and lifting downstream win rates.

Challenge: A large swimwear catalogue needed English, Spanish and Russian imagery and video for cross-border channels, but traditional creative production could not match catalogue scale or cost ceiling.

Solution: The team produced localised images and video for 300+ SKUs across English / Spanish / Russian markets through the EasySKU SaaS pipeline.

Outcome: Production costs cut by USD 12,000, cross-border e-commerce GMV up 23%.

Challenge: Multi-colour, multi-pattern, multi-size seatbelt webbing required defect inspection that traditional vision systems and 2-6 inspectors per line could not deliver at speed and consistency.

Solution: A patented AI-vision deep-learning inspection machine running at ≥100 m/min, detecting defects down to ≤1 mm across colour / pattern / size variants.

Outcome: One machine replaces 2-6 inspectors, capability +30% vs the incumbent system, technical specs at world-leading level, deployed across multiple plants globally.

Challenge: Rubber hoses with varied diameters, weave styles and surface patterns demanded 360° on-line defect detection at production cadence — beyond traditional inspection capability.

Solution: AI-vision deep-learning inspection machine with patented imaging optics and algorithms; in-line inspection at ≥45 m/min detecting sub-0.25 mm surface defects with >99.3% recall.

Outcome: Major lift in inspection capability and quality stability; deployed across multiple Sumitomo Riko plants worldwide.

Challenge: Raw webbing across 8 looms (30 production lines) needed continuous in-line defect detection — manual rounds could not maintain coverage.

Solution: Anomaly-detection AI algorithms applied to in-line inspection for 30 raw webbing lines, achieving ≥95% defect detection and ≥99% continuous coverage.

Outcome: Replaced traditional manual inspection, lifted detection capability and stabilised product quality with a short delivery cycle — a clear cost-down, throughput-up, quality-up result.

Challenge: Post-assembly airbag quality required simultaneous checks of 17+ items (presence, orientation, dimensions, labels, etc.), which traditional multi-operator visual inspection could not do consistently.

Solution: Full-stack AI-vision deep-learning system delivered with hardware, software and PLC integration to the existing line, performing in-line defect inspection across all 17 items.

Outcome: Replaced multi-operator simultaneous visual inspection, lifted detection capability and stabilised product quality.

Challenge: After glue coating, airbag fabric needed surface-quality detection covering drop-coating, needle-marks, glue-streaks, glue-spots and knot-threads — at production speed.

Solution: AI-vision deep-learning algorithms tuned for the five defect classes on coated airbag fabric, achieving recall ≥95% with false-alarm rate <5%.

Outcome: Quality-control coverage extended into the coating step with sustained accuracy and low operator workload.