Hasab AI – Speech-to-Text Platform Development

Hasab AI is an advanced speech-to-text platform developed to convert spoken language into precise, real-time text using cutting-edge artificial intelligence. Built at Hasab Labs, the platform leverages deep learning and natural language processing to deliver high transcription accuracy across multiple accents and contexts.

The system was designed to address key challenges in speech recognition, including low accuracy with diverse speech patterns, latency in processing, and lack of contextual adaptation. As a Full Stack Developer, I was responsible for architecting and implementing both the frontend application (Next.js) and backend AI pipeline (Python). Development and deployment were conducted through Replit, ensuring a streamlined prototyping and testing workflow.

Hasab AI was engineered with a focus on scalability, usability, and reliability, providing a complete end-to-end solution for businesses and individuals requiring fast and accurate transcription services.

Frontend: Next.js, TailwindCSS, Framer Motion Backend: Python (FastAPI), WebSocket Streaming Database: PostgreSQL (Supabase) Infrastructure: Replit (development and cloud runtime), Supabase Auth & Storage Integrations: OpenAI Whisper and custom-trained speech recognition models

Backend AI Pipeline

Designed and implemented Python-based FastAPI microservices to handle real-time audio ingestion and transcription.

Integrated OpenAI Whisper and custom-trained AI models optimized for accent diversity and contextual accuracy.

Improved processing latency using asynchronous streaming and background task execution.

Authentication and Subscription Management

Integrated Supabase Auth for secure user authentication and role-based session management.

Implemented a subscription and credit-based usage system using Stripe, supporting both trial and paid plans.

Developed user API key management for developers utilizing Hasab AI’s transcription APIs.

Database and Storage Architecture

Designed efficient PostgreSQL schemas for user data, transcription records, and billing information.

Leveraged Supabase Storage for secure file management with fine-grained access control policies.

Implemented scalable usage tracking and rate-limiting mechanisms to support high traffic volumes.

Frontend Development and User Experience

Built the landing page and interactive dashboard in Next.js, reflecting the product’s design system and branding.

Developed responsive and performant UI components using TailwindCSS and Framer Motion for smooth user interaction.

Implemented core pages, including “Our Services,” “Get Started,” and user dashboards for managing transcriptions.

Deployment and Performance Optimization

Deployed and tested the complete system on Replit with containerized reproducibility.

Ensured seamless communication between the Next.js frontend and Python backend via REST and WebSocket APIs.

Established a stable CI/CD process for quick updates and continuous testing in the Replit environment.

Accuracy and Efficiency: Achieved over 95% transcription accuracy across diverse accents and environments.

Scalable Infrastructure: Built an API architecture capable of handling thousands of concurrent audio sessions.

Operational Adoption: Improved transcription efficiency by over 40% compared to manual workflows.

Technical Innovation: Demonstrated a scalable AI SaaS model developed entirely within Replit’s ecosystem.

Multilingual Capability: Delivered consistent results for English, Amharic, and Arabic speech recognition.