Engagements across automotive, manufacturing, fintech and AI-native products — each scoped against a business metric agreed before we started. Client names are withheld under NDA; full references available on request during procurement.
Manual booth inspection was catching defects inconsistently across shifts. We deployed a vision system that identifies orange peel, runs, sags and dirt inclusions at full conveyor speed — running in shadow mode for six weeks until line supervisors trusted it, then taking the primary call.
Vibration, motor current and temperature telemetry fused into a survival model that flags bearing wear and die degradation three to nine days ahead — converting a monthly firefight into a planned-maintenance conversation.
Emerging failure modes were only visible once they hit structured warranty codes — months after dealers first noticed them. Language models over free-text technician notes now surface clusters weeks earlier, feeding straight into engineering change requests.
Six plants ran incompatible MES and historian stacks, making group-level OEE meaningless. We built a governed lakehouse with a common asset model and data contracts — so plant comparisons finally reflect reality rather than reporting convention.
Intermittent demand across 60,000 SKUs and 400 dealer locations. A hierarchical forecasting and inventory-positioning model cut safety stock materially while improving first-time fill rate at the counter.
An ingestion and analytics platform handling high-frequency CAN-bus and GPS data — powering driver scoring, battery health monitoring and predictive service scheduling across a mixed ICE and EV fleet.
Thin-file applicants were being declined by a rules engine that couldn't see them properly. A gradient-boosted scorecard with alternative signals widened approvals while cutting losses — with reason codes on every decision so declines remain explainable.
Batch fraud review was catching losses after settlement. A streaming feature pipeline and online model now score every transaction inline, with a shadow challenger model running continuously so thresholds stay calibrated as fraud patterns shift.
A greenfield AI product — not a chatbot bolted onto an existing app. We designed for uncertainty from the start: visible confidence, inline citation, easy correction and a refusal path, then shipped it as a paid tier.
Growth had outrun the architecture — deploys took four hours and infra spend was growing faster than revenue. We moved them onto an internal developer platform with autoscaling, observability and FinOps guardrails in the pipeline.
Planners were forecasting in spreadsheets with a 31% error rate. We built a hierarchical forecasting system with promotion and seasonality features, then redesigned the S&OP process around it so the numbers were actually used.
Letters of credit were being keyed by hand across three back-office sites. We deployed a document intelligence pipeline with confidence-based routing — 82% straight-through, the remainder queued to specialists with extracted fields pre-filled.
A retrieval-grounded assistant over product docs, past tickets and release notes — with strict citation, refusal behaviour on low confidence, and a weekly evaluation loop run by the client's own support leads.
Discounting decisions were made by gut feel across 90 outlets. An elasticity model tied to inventory age, regional demand and competitor pricing now recommends incentive levels — with guardrails so no recommendation breaches OEM policy.
A greenfield dispatch application with an optimization engine at its core — mobile app for drivers, control tower for planners, and an exception model that learns which overrides planners actually make.
A compliance deadline moved forward by a quarter. We stood up a managed pod of backend, QA and DevOps engineers inside three weeks — all still with the client twelve months later.
Analytics in a regulated environment needs lineage, validation and change control by default. We designed the platform and the qualification evidence together, so the audit was a formality rather than a project.
Before automating anything, we mined the actual process from system logs. The result reordered the automation roadmap entirely — two of the top-priority candidates turned out to be rework loops that simply needed deleting.
The board approved AI investment but had no way to assess risk. We built a tiered governance model, a standard evaluation harness and a model registry — now the gate every internal and vendor AI system passes through.
Detailed write-ups, reference calls and architecture walkthroughs are available under NDA during evaluation.
Consulting is easy to fake with activity. We avoid that by agreeing a single primary metric, a measurement method and a baseline in week one — then reporting against it whether it flatters us or not.
We'll share architecture diagrams, measurement methodology and reference contacts for the engagements closest to your situation.
