Proof

Production AI, shipped and measured

Each case is anonymised, scoped against a KPI leadership already tracks, and owned by the client when we leave.

Industry

Function

Banking estate alerts looked urgent but carried no context.

A banking network with 3,000+ locations needed security teams to know which alarm mattered before response time was lost.

99.9%Uptime at Scale

<100msAlert Latency

Ambient SystemsComputer VisionAutomation

Featured

A luxury brand was recognised by name but misread by AI search.

Retail

A global jewellery brand needed AI search platforms to explain products correctly without diluting heritage, imagery, or positioning.

+60%AI Citations (Gifting)

+35%Recommendation Accuracy

Ambient SystemsAnalytics

Store leaders could not see the revenue leak across CRM, POS, logistics, and finance.

Retail

A US furniture retailer needed daily decisions to come from one operating picture instead of four conflicting reports.

$1MAnnual Revenue Uplift

$440KCost Savings

Data EngineeringAmbient SystemsAnalytics

Solar leaders waited days for answers buried in five systems.

Energy

A solar infrastructure enterprise needed 40+ leaders to read operational performance without depending on analysts for every question.

2×ROI in Year 1

−3 daysDecision Turnaround

Ambient SystemsData EngineeringAnalytics

Quality teams spent their week preparing reports instead of controlling lots.

Agri & Food

An agri and food manufacturer needed SAP-heavy reporting and seed-lot quality checks to move before analyst capacity became the bottleneck.

50%Productivity Lift

−48 hrsWorkflow Turnaround

Ambient SystemsAutomationData Engineering

Plant leaders priced orders without seeing machine energy cost.

Construction

An EPC contractor needed asset productivity, order output, and energy draw tied to margin before another delayed order erased profit.

+5%Order Pricing Uplift

−10%Energy Operations

AnalyticsData EngineeringAmbient Systems

A plant was missing delivery dates on one in five orders — with no system to see why.

Manufacturing

A manufacturing plant needed production schedules that accounted for inventory, maintenance, and leave before committing to customer dates.

−50%Orders Missing Expected Date

ImprovedCustomer Satisfaction

AutomationData EngineeringAnalytics

A contractor's win rate was falling and cost overruns rising — without knowing which bids were the problem.

Construction

A construction contractor needed to benchmark bids against real historical cost data to stop losing the right work at the wrong price.

+20%Win Rate on Revised Bids

~50%Bids Revised by Engine

AnalyticsData EngineeringAutomation

An African hardwood mill ran at half the global recovery benchmark — with no per-log data to understand why.

Construction

A tropical hardwood sawmill needed AI-driven yield prediction and order-mix optimisation to close a ten-point recovery gap worth seven figures annually.

+5 ptsRecovery Rate Uplift

7-figureAnnual Value at 100K m³

AnalyticsData EngineeringPredictive Maintenance

Seed intake decisions were made differently by every shift — and quality problems surfaced too late to avoid waste.

Agri & Food

An agricultural seed processing company needed a consistent, AI-driven intake process that standardised quality decisions and cut report-to-action turnaround.

UnifiedIntake Decisions Across Teams

LiveBatch-Level Quality Tracking

AutomationAmbient SystemsData Engineering

Featured

Machines kept stopping after shift change and repairs missed the root cause.

Manufacturing

A consumer products plant needed breakdown risk to surface before abrupt stoppages broke customer commitments.

−10%Unexpected Breakdowns

25% YoYTrend Reversed

Predictive MaintenanceAmbient SystemsData Engineering

Bring your most demanding operational challenge to a team built to answer it.

Bring the workflow, the data constraints, and the number leadership already watches.

Bring us the hard question Solutions