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Event RSVP API
A REST API for managing events and RSVPs, built with Python and FastAPI. Handles event creation, RSVP tracking with yes/no/maybe responses, and attendee lookups.
The focus here was correctness by design: RSVPs are stored so that one person can't hold two conflicting responses, and valid response values are enforced at the input layer, so bad data can't enter the system. Includes clear API documentation and proper error handling.
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
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Expert-led, AI-assisted incident response, digital forensics, and threat containment for high-stakes cyber events.
MomentCut is an AI-powered video editing workflow that automatically analyzes footage, detects the best moments, and prepares export-ready clips. I designed and developed the interface while building a modern workflow focused on speed, usability, and content creation.
Built and maintained a HIPAA compliant production medical AI scribe system that uses multi-step LLM agents to extract clinical entities directly from physician-patient conversations and turn them into structured medical notes, cutting down the manual transcription work clinicians used to do after every visit.
Multi-stage clinical workflow: Documentation, coding, and review each have different logic and conditional paths, and the system needed to branch correctly between them without losing context.
Clinical accuracy: Generated notes had to be grounded in real patient history and clinical guidelines, not just plausible-sounding text.
Production reliability: As a live system handling real conversations, every agent run needed to be observable, debuggable, and monitored for cost and latency in real time.
Non-technical requirements gathering: Clinical needs had to be captured accurately from stakeholders without a technical background and translated into precise agent logic.
Approach:
Stateful agent orchestration with LangGraph
Designed LangGraph-based agent workflows with conditional branching, allowing the system to move correctly across documentation, coding, and review stages based on conversation content.
Context-grounded note generation with RAG
Built a RAG pipeline on AWS Bedrock with embeddings, so every generated note is grounded in the patient's actual history and relevant clinical guidelines rather than generic output.
Full production observability
Integrated Langfuse across all agent runs to track token usage, latency, and model KPIs, giving the team visibility into system health and cost in production, not just at build time.
Clinical stakeholder collaboration
Ran requirements sessions directly with clinical staff, converting their documentation needs into concrete agent behavior specs and validation criteria.
Results & Impact:
~40% reduction in manual transcription time for clinicians using the system.
Clinically grounded output, with notes tied to real patient history and guidelines instead of unsupported generation.
Full production observability, with token usage, latency, and model performance tracked continuously.
A workflow clinicians could trust, built through direct collaboration rather than a black-box handoff.
Provided Services & Solutions:
📌 AI Agent Development (LangGraph)
📌 RAG Pipeline Development (AWS Bedrock)
📌 LLM Observability (Langfuse)
📌 Cloud Infrastructure (AWS Lambda, S3, DynamoDB)
📌 Stakeholder Requirements Translation
📌 Production ML Systems
Tech Stack
Python · LangChain · LangGraph · AWS (Bedrock, Lambda, S3, DynamoDB) · Langfuse · TypeScript · REST APIs
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