PREDICTIVE ANALYTICS & ML
Forecast demand, detect anomalies, and score risk using models trained on your operational data.
Engagement
Overview
We help teams move from spreadsheets and gut feel to models that update with fresh data. Our focus is practical ML — models that integrate into dashboards, alerts, and planning cycles your business already uses.
What we build
Demand & revenue forecasting
Anomaly detection
Classification & scoring
01 · Scope
We help teams move from spreadsheets and gut feel to models that update with fresh data. Our focus is practical ML — models that integrate into dashboards, alerts, and planning cycles your business already uses.
Demand & revenue forecasting
Time-series models for inventory, sales, and capacity planning with confidence intervals.
Anomaly detection
Spot unusual patterns in transactions, sensor readings, or user behavior before they escalate.
Classification & scoring
Predict churn, fraud risk, lead quality, or equipment failure probability from structured features.
Decision dashboards
Surface predictions alongside historical trends for planners and operators.
Typical stack
02 · System blueprint
A production path from the current process to a workflow your team can operate.
01
Review data quality, label availability, and baseline metrics.
02
Train, validate, and compare models with reproducible experiments.
03
Run backtests and shadow mode with domain experts before go-live.
04
Schedule inference jobs or real-time scoring with drift monitoring.
03 · Impact
The problem
Plans are still driven by spreadsheets and last month’s actuals, so surprises arrive after they have already cost you.
The solution we install
Models that forecast, flag anomalies, and score risk — wired into the dashboards and alerts planners already open.
Movement we target
Planning
Last period plus guessworkForward view with range
Exceptions
Found after the factFlagged in the stream
Review
Random samplingRisk-ranked queues
04 · Use cases
Improve stock levels and production schedules with better forward visibility.
05 · Methodology
Four phases from discovery to kaizen — scoped to your systems, not a generic week-count.
Map the problem and the state of your data.
A small proof-of-concept validates real value.
Integrated into your existing systems, into production.
Continuous improvement, guided by real operating data.
06 · Frequently asked
A short discovery conversation is enough to map the problem, the systems involved, and whether a pilot or a fuller build is the right first step.
Yes. We integrate into ERP, CRM, and internal apps you already operate — the point is a workflow your team keeps, not a disconnected demo.
Yes. Consulting, coordination, and handover run in Japanese and English.
The production workflow, the integration points, and the operating view your team uses day to day — with documentation and a kaizen path after go-live.