Each study attaches an AI capability to the operating metric it moved. Recommendation engines, autonomous agents, fraud models, and vision pipelines — built on real data, shipped into production, observable after launch.
Starting points when teams ask what AI can realistically do for their operating metrics. Browse the full archive on our case studies page.
A recommendation engine trained on session intent, order history, and catalog embeddings replaced static merchandising rules across the whole journey — from landing to checkout.
Demand forecasting and yield models now run alongside the booking stack, adjusting inventory, pricing, and promotions in real time as the market shifts around them.
Portfolio-level models forecast tenant risk, optimize listing exposure, and flag capex windows before they turn into emergencies — all inside the existing operator dashboard.
Support triage, fraud scoring, predictive maintenance, document intelligence — the AI capabilities that earn their keep across industries. Pair these with our AI agents and AI and ML services capabilities for a full production stack.
Handles 75% of inbound tickets end-to-end across CRM, billing, and knowledge base. Response time fell from 12 minutes to under 2.
60% of interviews booked without a human coordinator, 40% reduction in time-to-hire, zero calendar-collision tickets in 90 days.
Behavioral model flags anomalous transactions the instant they happen — replacing an overnight batch review with sub-second alerts.
Telemetry-driven anomaly detection cut unplanned downtime by 50% and moved the maintenance team from reactive to scheduled.
NLP pipeline drafts SOAP notes from consultation audio, freeing clinicians from 90 minutes of after-hours charting per day.
Entity and clause extraction over 1M+ documents — redlines and risk flags delivered in seconds instead of billable hours.
The difference between a pilot that stalls and an AI capability that compounds is usually one of these four choices made early. We insist on all four before we take an engagement live.
Every engagement opens with the operating number AI is supposed to move — AOV, time-to-resolve, defect rate, stockout days. Model quality is judged against that KPI, not accuracy alone.
Two to four weeks on a slice of live production data. No synthetic benchmarks, no sandbox demos — the model has to behave on the messy real-world signal or it doesn't graduate.
Every production deployment ships with traces, evaluation harnesses, and drift monitors. When performance degrades, the team sees it before the business does.
For high-stakes decisions, the AI drafts and a human approves. We design the handoff carefully — the goal is compounding human judgement, not replacing it.
AI earns its place differently in every sector. Browse industry-specific work in healthcare, finance, retail, and more.
We measure AI work the way a CFO does — as revenue created, cost avoided, or risk reduced. These are cross-portfolio medians from the last 24 months of production deployments.
Bring the operating metric you want to move. We'll scope the AI build, prove it on real data, and ship it to production with the telemetry your board will actually trust.