01
Forecasting at scale
Demand, revenue, and capacity models with hierarchical reconciliation.
Expertise
Predictive & MLOps.
Forecasting, anomaly detection, and full MLOps — from feature store to drift monitoring.
Practice
Expertise
Delivery
Riyadh · London · Lahore
Model
Fixed-scope · Retainer
Timeline
6–16 weeks / phase
01 — What we do
Three pillars that carry the practice.
02
Feature store & registry
Centralized features and models with lineage, versioning, and reuse.
03
Drift & quality monitoring
Automated retraining triggered by data-drift and performance decay.
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We ship this practice as a small, opinionated system — running infrastructure, evaluation harnesses, and the rituals that make leadership trust the numbers.
02 — Capabilities
What we build inside this practice.
Every engagement is scoped as a small system, not a slide deck. You get running infrastructure, documentation, and the metric-store hooks to measure it.
Scope an engagement01Time-series forecasting (Prophet, N-BEATS, TFT)
02Anomaly & outlier detection
03Feature store (Feast / Tecton)
04Model registry & CI/CD
05Drift & performance monitoring
06Shadow deployment & canaries
07Explainability (SHAP)
08Cost-aware inference serving
Reference architecture
How the pieces move.
03 — In production
Numbers from the field.
12%
Forecast MAPE improvement
Vs. incumbent ERP forecasting module.
7min
Detection to alert
On production drift events.
60+
Models in production
Across a single governed registry.
Ready to scope this into your stack?
Book a working session with a lead architect.