Regulatory medical-writing platform
2023–2026
A platform that drafts regulatory documents for clinical trials from source evidence, built for a pharmaceutical documentation firm and its sponsors.
AI
Azure OpenAI
I used Azure OpenAI in the regulatory-medical-writing platform to draft regulated clinical documents from templates.
See its threads in the worldUsed on
Medical-writing platform archive
2024–2026
A document-authoring product that fills medical writers' templates from a sponsor's own source documents, with redaction of confidential detail.
Assistant for tax professionals
2023–2024
Chat assistant for US tax professionals that answers from a curated knowledge base and reads uploaded individual tax returns to suggest strategies.
Banking document chatbot
2026
A chatbot that answers banking customers' questions from the bank's own FAQ documents using retrieval-augmented generation.
Sales assistance for a sales-performance vendor
2024–2025
Sales-assistance software for a sales-performance vendor's reps: voice and camera task capture, business-card scanning, and prospect research reports.
Chatbot for a financial-advisory app
2023
A chatbot for a financial advisory web app that answers visitors, serves members from their profile and collects planner data.
Stories where it mattered
2026 · Regulatory medical-writing platform
A cache that remembered the failures
- Situation
- On a regulatory document platform, generated documents came out wrong in ways that did not reproduce, and retries repeated the bad output.
- Task
- Find the true cause in a pipeline whose output reaches a client in a regulated industry.
- Action
- I traced it to a global result cache that also stored partial failed runs, then re-architected generation around per-step isolation with failure-aware cleanup.
- Result
- Failed runs stopped affecting later ones, a retry became a real retry, and the written root cause let the team review it.
2026 · Regulatory medical-writing platform
Tables that arrived in the client's document as raw markup
- Situation
- Medical writing deliverables showed misplaced tables, detached titles, and sometimes raw markup visible as text in the client's document.
- Task
- Produce one root cause analysis for three separately reported bugs before anyone wrote a fix.
- Action
- I found three causes: raw XML mutation, incomplete model-emitted HTML, and context-blind placeholders. I moved tables to a structured contract with neighbour context.
- Result
- The whole class of defect went away, not just the three reported instances.
2024 · Regulatory medical-writing platform
Asking what onboarding was supposed to produce
- Situation
- Two engineers were joining a document platform module covering audit logs, automatic mapping and server management.
- Task
- Give onboarding an intended outcome rather than just an activity.
- Action
- I asked the lead for the target outcome, how we would judge pace, and when production value was expected, and offered a reference checklist.
- Result
- Onboarding ran against a stated target and a first-value date, and the checklist was reused.
Goes with
LLMs
Azure
OpenAI API
Token Management
PDF ProcessingThrow me a paper plane
Write a line; I’ll find it in my inbox.