Fraud Detection · Marketing Personalization · Risk Management
Previously hampered by an outdated tech stack and siloed data, which caused frequent failures and lengthy ML model training, Simpl faced substantial scalability and operational challenges.
Reported impact
Simpl achieved measurable business impact on Databricks, including 90% faster-to-deploy ML models; 35% improvement in the analytics team’s turnaround time for reporting tasks; This transformation has not only accelerated time to market by 90% but also democratized data access, allowing a broader range of personnel to engage directly with business-critical data..
Simpl, India’s one-tap checkout and post-purchase experience platform, is on a mission to deliver secure and hassle-free transactions through advanced machine learning (ML) and artificial intelligence (AI). Catering to around 30,000 merchants and managing over 10 million transactions monthly, Simpl demonstrates a significant influence in the fintech sector, bolstered by over 100 active workflows that drive their PayLater services and various financial operations. Previously hampered by an outdated tech stack and siloed data, which caused frequent failures and lengthy ML model training, Simpl faced substantial scalability and operational challenges.
Operational, customer, and transactional data lands in Databricks and is transformed through managed pipelines stored in Delta Lake before being served through Databricks SQL
35% improvement in the analytics team’s turnaround time for reporting tasks
90% faster-to-deploy ML models
Operating exclusively in India, Simpl collaborates with approximately 30,000 merchants and manages over 10 million monthly user transactions, showcasing their substantial market footprint.
Simpl’s array of 100+ active workflows and 10K+ daily SQL queries demonstrates their robust focus on business analytics, reporting, data engineering operations and ML model training.
This transformation has not only accelerated time to market by 90% but also democratized data access, allowing a broader range of personnel to engage directly with business-critical data.
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
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