Challenge
Analysts relied on brittle notebooks; models drifted and ops had no repeatable path from training to production.
Case study · 2025
AI, ML & Generative AI
ML pipelines that turn signal into decisions
AI/MLPythonData EngineeringETL/ELTGCP
Client
Retail demand planning org
Engagement
ML platform & data engineering
Duration
10 months
Year
2025
Cognify Insight moved forecasting and anomaly detection out of brittle notebooks into production ML. Feature stores, training jobs, model registries, and drift monitors sit on disciplined data engineering so business owners get APIs and dashboards they can trust — and ML engineers get a path from experiment to serve without heroics.
A Python-based AI/ML platform for demand forecasting and anomaly detection, backed by disciplined data engineering.
Narrative
How the engagement moved from constraints to a shippable system.
Approach
We productionized feature stores, training jobs, and model registries with monitoring for drift and latency.
Solution
Cognify Insight serves scored predictions via APIs with dashboards for business owners and ML engineers.
Scope
What we delivered
Feature store and training job pipelines
Model registry with versioned deploys
Scoring APIs for demand and anomaly use cases
Drift and latency monitoring
Business and ML engineer dashboards
Runbooks for retraining and rollback
Highlights
What stood out
Forecast MAPE improved by 18%
Model deploy time from weeks to hours
Automated drift alerts into Slack/PagerDuty
Shared ownership between data, ML, and product
Engagement flow
A repeatable rhythm from discovery through hardening — tuned to this product’s constraints.
01
Notebook archaeology
Catalogued existing models, data debts, and silent failure modes.
02
Platform spine
Stood up feature store, training orchestration, and registry.
03
Productionize models
Migrated priority forecasts and anomaly detectors behind APIs.
04
Operate
Wired drift monitors, on-call alerts, and retraining cadences.
Technology stack
The delivery stack behind this engagement — client surfaces paired with services, data, and infrastructure.
Frontend
Nuxt.jsDashboard UICharting
Backend & ML
PythonAI/MLFeature StoreModel Registry
Data & Cloud
GCPETL/ELTData EngineeringBigQuery
Outcomes
01
Forecast MAPE improved by 18%
02
Model deploy time from weeks to hours
03
Automated drift alerts into Slack/PagerDuty
Looking ahead
Add causal uplift models for promo planning and self-serve feature requests.
More work
Onboard the Apeiriion express
Tell us about your product, platform, or modernization goal — we'll assemble a senior squad and a clear delivery plan.