Sanket Rajeev Sabharwal
Co-Founder
PhD in Computer Science (ML and computer vision) from the University of Genoa. Published in IEEE Access and Nature Scientific Reports. Former Deloitte senior analyst. Both founders are embedded in every engagement, owning delivery end-to-end across product builds, ML systems, and data platforms.
Publications
2024
Computational Modeling of Synchronization and Leadership Dynamics in Small Group Interactions Doctoral Thesis, University of Genova
2024
2023
2022
2022
2020
Sentimenter: Monitoring Sentiments of Citizens by Analyzing their Tweets 6th Italian Conference on ICT for Smart Cities and Communities
Articles
- Architecture review that tests runtime ownership
- When to rebuild vs refactor software
- ML model lineage for audit compliance
- Marketplace payment architecture for the second year
- Nonfunctional requirements change sprint planning
- Product analytics instrumentation before A/B tests
- Dimensional modeling for governed metric definitions
- Customer analytics platforms and the value moment
- Build vs buy framework that prices the exit path
- AI production readiness beyond model accuracy
- How to vet a software agency using delivery evidence
- AI vendor lock-in and the exit key you were never given
- LLM integration patterns and sentences that become records
- ML monitoring and the price hidden inside a success rate
- LLM fine-tuning deployment needs rollback paths and canary releases
- How to scope a software project around business decisions
- AI agent governance starts with SLAs for production write access
- CMS and low-code deployment controls for production systems
- Test failure modes before you launch
- How completed workflows drive SaaS retention
- Managing custom SaaS features without debt
- How to choose between a data warehouse and data lake
- Enterprise computer vision procurement requires device trust and data provenance
- The false economy of the quick MVP
- Hyperparameter optimization to maximize ML performance
- The software development lifecycle - A time-tested protocol for founders and product leaders
- The multimodal gap in enterprise AI
- Feature engineering decides machine learning outcomes
- The Machine Learning checklist