InnCreTech's AI-native data and agents platform (warehouse, ETL, lineage, BI, multi-agent orchestration and governance). I own and lead its BI analytics module and DAVE, the module's AI analytics agent.
- Lead the BI analytics module end to end: a team of 4–6 engineers and testers, the system design (query layer, data structures, latency) and the AI agent. Mentor new engineers and run technical interviews.
- Forward-deployed client work: weekly calls with clients to gather requirements, walk through trade-offs and progress, and run demos, then turn their business needs into specs.
- Built AI features that let users ask questions in plain English and get charts and insights, cutting manual analysis by 90%.
- Improved DAVE, the module's AI analytics agent, which turns questions into database queries, finds trends and anomalies, and builds charts and reports: about 80% higher evaluation scores, 2× faster responses and two-thirds fewer errors.
- Made these gains by restructuring DAVE's skills, prompts and workflows, adding a check that compares its answers with the real chart data, and building a Playwright test framework to evaluate it across frontier and open models.
- Advised a client on their AI service under a 2-week deadline, guiding changes that made it 2–3× faster and cut prompt size and cost.
- Designed the dashboard engine (drill-down, conditional formatting, optimized SQL, in-memory caching, parallel queries) for sub-second responses on large datasets, and hardened the module's performance and security.
- Added white-label dashboard embedding with theming and API configuration, a reporting engine with ad-hoc queries and PDF/Excel/CSV export, and event-driven threshold alerts.
- Added data querying over JDBC and Spark and a search interface that lets non-technical users explore their data.
Debt financing software for private capital. As my focus moved to DataGOL, I took on a lead and mentor role here.
- Built an AI document feature that turns uploaded financial documents into structured financial data and lets users ask plain-English questions about the extracted results.
- Mentor newly hired engineers through onboarding and code reviews, and run technical interviews for engineering candidates.