I build AI products that have to work in the real world — not just in demos.
My work sits at the intersection of product strategy, applied AI, user trust, and production reliability. I care about building AI systems that are grounded, evaluated, observable, and safe enough for users to rely on when the stakes are high.
Most recently, I founded and led Spellbound, an LLM-powered fertility and pregnancy guidance platform that scaled to 100K MAU, became cash-flow positive within 12 months, was adopted by 10+ clinical partners, and exited to a healthcare chain.
I use this GitHub to share sanitized product case studies, AI workflow patterns, and prototype systems that reflect how I think about building reliable AI products.
I like building from first principles:
- Start with the user workflow and the real failure modes
- Define what the product is — and what it should never try to be
- Treat prompts, retrieval, memory, and evals as product surfaces
- Design evals and safety gates before scaling
- Use human-in-the-loop workflows where the AI should not act alone
- Measure quality in production, not just in demos
- Iterate across UX, prompts, retrieval, routing, safety, and model behavior
I’m strongest at the product-engineering boundary: translating ambiguous user problems into AI workflows, making technical trade-offs explicit, and partnering deeply with engineering to ship systems users can trust.
I’m currently building and documenting production-inspired AI prototypes that explore how to move from impressive demos to reliable, high-trust AI products.
- 🧪 AI evals before launch — designing eval harnesses, synthetic user scenarios, LLM-as-judge rubrics, and release gates for agentic workflows
- 🛡️ Safer RAG systems — exploring retrieval quality, source grounding, hallucination checks, confidence gating, and citation-aware responses
- 🤝 Human-in-the-loop AI — designing escalation paths for moments where the AI should clarify, defer, or route to a human instead of acting alone
- 🧭 Agentic workflow design — turning ambiguous user goals into structured AI workflows with clear boundaries, tool use, memory, and fallback behavior
- 📊 Production AI observability — thinking through telemetry, feedback loops, drift signals, rollback triggers, and quality monitoring after launch
- ✍️ Writing about applied AI product work — sharing notes on what breaks, what needs to be measured, and how product teams can build trust into AI systems from day one
Commercial product · Not open-source
An LLM-powered fertility and pregnancy guidance platform for high-trust, medically sensitive user journeys.
Product/system areas:
RAG · Clinician-verified knowledge base · OCR report interpretation · Model routing · LLM-as-judge evals · Safety guardrails · Human escalation · AI observability
Impact:
Scaled to 100K MAU, adopted by 10+ clinical partners, became cash-flow positive within 12 months, and exited to a healthcare chain.
Commercial product · Not open-source
An AI job-search and interview-prep copilot designed to help candidates move from generic applications to targeted, high-conversion job-search execution.
Product/system areas:
Semantic job matching · Resume tailoring · Interview simulations · Hiring-manager research · Role-specific rubrics · Candidate feedback loops · Personalized prep workflows
📊 [Brand OS] — Strengthen your brand on LinkedIn by creating and posting consistent content in your unique voice as well as improve it by tracking its performance
🛡️ [Trade X] — Deep equity research and automated trading agent
🧪 [RocketShop] — Create brand content for your D2C store
🎯 [Sureshot AI] - Initial version of BlitzPrep
- AI systems: Agentic workflows, RAG, multimodal AI, model routing, personalization
- Quality & safety: LLM evals, LLM-as-judge, hallucination checks, guardrails, red-teaming, human-in-the-loop review
- Production AI: Observability, telemetry, feedback loops, rollback triggers, cost/latency tradeoffs
- Product: 0→1 strategy, PMF discovery, product roadmap, GTM, experimentation, cross-functional execution
- Technical: SQL, Python, prototyping, prompt/workflow design, AI system debugging
- Exited Founder & Head of Product at Spellbound (acquired Feb, 2026)
- Previously Senior AI PM at Magic Technologies and AI PM at KPMG India
-
- Early career Software Engineer at Juniper Networks
- Carnegie Mellon University, MS in Software Management
- Based in San Francisco Bay Area