What Our Clients Say About Working With Us

Challenge

A growing fintech firm had accumulated three years of disorganised data across five different storage systems. Their data scientists spent roughly 40% of working hours just locating and cleaning data before any analysis could begin.

Approach

novusynars consolidated the five sources into a single lakehouse architecture with standardised schemas, automated quality checks at ingestion, and a self-service data catalogue accessible to all teams.

Results

Data preparation time dropped by 65%. The number of failed batch jobs fell from roughly twelve per week to fewer than two. The engagement ran for eleven weeks.

Challenge

An insurance company was running seven different ML models for pricing, each with its own feature computation logic. Feature drift and inconsistencies between training and serving were causing a measurable gap in prediction accuracy.

Approach

novusynars implemented a centralised feature store that serves as the single source for all seven models — unified feature definitions, version control, and consistent serving for both batch and real-time inference.

Results

Training-serving skew was eliminated across all models. Model retraining cycles shortened from two weeks to three days. The project was delivered in seven weeks.

Challenge

A manufacturing group processing IoT sensor data had pipelines silently dropping records during peak loads. They were aware of latency issues but lacked visibility into where the bottlenecks sat in their processing chain.

Approach

A three-week health assessment mapped every stage of the pipeline — from sensor ingestion through transformation to the analytics warehouse. The team produced a detailed scorecard and priority-ranked improvement plan.

Results

The assessment identified four critical failure points responsible for 80% of data loss during peak periods. The client's internal team implemented the top three fixes within a month, reducing dropped records by 90%.