Fullstack & AI systems that scale.

A TypeScript SDK batches events — 50 events or 5 seconds, whichever first — with exponential backoff, and posts them to a /capture endpoint that enqueues to an async DuckDB writer. Natural-language questions map to one of 8 named SQL queries against a single wide event table, so answering them never costs a model call.

Claude Haiku extracts structured attributes from the raw brief — category, audience, price tier, channel fit — with JSON retry logic. Claude Sonnet then scores publishers across five weighted dimensions and drafts persona-specific creative, behind an abstract LLMClient interface so Claude or OpenAI can sit behind the same pipeline.

Agents act independently on low-risk steps but escalate above defined risk thresholds — every decision constrained to a validated schema and written to an immutable audit log. Keeping agents HIPAA-compliant meant schema-validated outputs and full auditability on every action, plus human escalation for high-risk steps, without collapsing response latency.

Built the streaming pipeline off the transactional databases, and separately designed dev → staging → prod promotion pipelines so every change followed the same release path. Streaming changes out of live tables without adding load or risking data drift — ordering guarantees and replay-safety had to hold up under real transaction volume, not a demo.

Built the chatbot end-to-end as part of the founding team — a retrieval pipeline over live bank rate and product data, with a conversation flow that never oversteps into unlicensed advice. Bank rates and terms change daily, so a hallucinated return figure is a real trust and compliance risk — solved with RAG over a continuously-synced catalog plus deterministic fallbacks for numeric claims.

Designed and owned the payment service from scratch — idempotent processing, reconciliation, encrypted data handling — and led the integration pattern each new banking partner plugged into. Different APIs, failure modes, and settlement timing per partner meant the reconciliation and retry layer had to be robust enough to guarantee correct money movement, with no double bookings and no silent failures.

Designed and shipped the platform end-to-end, then built automation that kept selling prices and deal listings in sync across microservices the moment buying prices changed. Deal state lived across multiple microservices at once — a stale sync meant customers could buy an expired deal, so the fix had to be automated and real-time.

Each promotion type — percentage, fixed — is its own class registered in a lookup table, so adding a new type means one file and one registry line; the cart service, routes, and database never change. Promotions are checked against the delivery date, not "now," and when multiple promos match a line, best-discount-wins avoids stacking into a negative total.