AI Dictation & Editing Platform
Timeline
Summer 2024 - Spring 2025
Services Provided
Technical Leadership: Drove the end-to-end technical strategy from concept to launch, including MVP planning, architectural design, and tech stack selection for a real-time AI writing platform.AI System Design: Developed and maintained an LLM-based editing engine capable of preserving author voice, supporting multiple writing styles, and generating context-aware suggestions.Backend & ML Engineering: Built secure APIs and model orchestration pipelines to power core features like dictation, text enhancement, and stylistic adaptation.Product Development Collaboration: Partnered with product leadership and early users to prioritize and design intuitive, high-impact user features such as revision history, dictation input, and editing personas.DevOps & MLOps: Managed cloud infrastructure (Azure) for scalable inference, automated deployments (CI/CD with GitHub Actions), and performance monitoring.Data Privacy & Ethics: Designed systems with zero data retention from user input, enforcing strong privacy protections aligned with GDPR and ethical AI standards.Tools and Stack
Languages & Frameworks: Python (FastAPI), TypeScript, AngularAI/ML Stack: GPT-based language models via Azure OpenAI, speech-to-text integrationCloud Infrastructure: Microsoft Azure – including App Services, serverless functions, object storage, and managed databasesFrontend: Angular with server-side rendering, built for responsive desktop and mobile useDevOps: Docker, GitHub Actions, Azure DevOps for CI/CD automation and container orchestrationMonitoring & Analytics: Application Insights, user behavior analytics (e.g., PostHog or similar)Security & Auth: OAuth 2.0, JWT-based authentication, and role-based access control for secure user interactionCase Study Details
I served as CTO for an AI writing startup that launched publicly in 2025, delivering an intelligent platform for dictation, editing, and genre-specific writing support. The platform helps users create high-quality content faster - without compromising their unique voice or their privacy.
Challenge:
The project aimed to solve key pain points for writers and professionals:
- Long editing cycles
- Inconsistent writing quality
- High editing costs
- Lack of tools that preserve tone and intent
- Strict privacy concerns with generative AI tools
The goal was to launch an MVP that demonstrated value quickly while being compliant with global data privacy standards.
Solution:
As CTO, I:
- Led technical strategy and system architecture, balancing rapid prototyping with long-term scalability.
- Built the backend in FastAPI, supporting LLM-based editing workflows and dictation features.
- Integrated speech-to-text models and genre-specific editing personas for adaptive feedback.
- Deployed on Azure with Docker, using GitHub Actions for CI/CD and secure container builds.
- Implemented zero data retention policies to meet GDPR and ethical AI use guidelines.
- Delivered a performant, responsive frontend using Angular with server-side rendering.
Note: Specific implementation details are under NDA and have been omitted. All information above is based on publicly observable features or generalizable practices.
