Building the Data Foundation for AI-First Products

The team had world-class ML algorithms but no data infrastructure to feed them. Customer data was scattered across 12 different systems — CRM, product analytics, support tickets, billing, marketing automation, and legacy databases. Data scientists spent an estimated 70% of their time on data wrangling: extracting, cleaning, and joining datasets manually. Model training cycles took 3 days because data had to be batch-processed weekly. The company was unable to ship AI features to production because the pipeline from model to deployment did not exist.

We built a unified data platform in three phases. Phase 1 created a real-time ingestion layer that captured events from all 12 source systems into a centralized data lake with schema validation and deduplication. Phase 2 built the feature store — a curated, versioned repository of ML-ready features computed from raw data, eliminating redundant preprocessing across teams. Phase 3 deployed the ML operations pipeline: automated model training, A/B testing infrastructure, model versioning, and one-click deployment to production with monitoring and automatic rollback on performance degradation.

Data silos were reduced from 12 fragmented systems to 1 unified platform with real-time data availability. Model training time dropped from 3 days (weekly batch) to 4 hours (real-time streaming). Time from model development to production deployment decreased from 6 weeks to 3 days through the MLOps pipeline. The data science team reclaimed 70% of their time for actual model development. The company shipped 4 AI-powered product features in the first quarter after platform deployment — compared to zero in the previous year.