Cross-Platform AI SaaS Enhancement for Sintra

Sintra is an AI-powered business assistant platform with a suite of specialized "AI employees" that help automate content, support, marketing, and operational tasks. It works through a central web dashboard and an app, with workflows, automation and customizable AI helpers for different business functions. User experiences vary but many find it helpful for productivity and outsourcing routine work.

Sintra's core concept is giving you role-based AI assistants, each trained for a specific business function, Social media manager, Custom support specialist, Copywriter, SEO specialist and etc. These aren't separate apps you install individually - they are roles inside the Sintra platform you interact with via chat-like conversion. You feed them context about your business so they can offer outputs tailored to you.

React, Next, React Native, Expo, Node, Open AI, Vector DB, Tailwind CSS, Shadcn UI, Redis, Stripe

The company was building a mobile-first and web accessible SaaS platform offering AI "employees". The initial version struggled with slow AI responses, inconsistent context struggled with slow AI responses, inconsistent context retention across agents, unreliable third-party integrations and poor cross-platform UX performance

I was responsible for designing and implementing a full stack solution across web and mobile that would deliver:

Real-time AI chat with streaming responses

Persistent multi-agent memory per workspace

Automation workflows with external integrations

Seamless, responsive, and secure SaaS experience across iOS, Android and Web

I built a cross-platform React Native app with TypeScript, featuring modular agent-based UI for switching AI roles. I optimized performance using Redux Toolkit + RTK Query, memoization, lazy loading, and background task handling.

I developed a responsive web interface with React, Next.js and Tailwind CSS leveraging reusable components for shared mobile functionality. I enhanced performance and SEO with SSR/ISR and integrated real-time AI chat via WebSocket streaming with optimistic UI updates.

I integrated mobile and web clients with backend LLM services, enabling token-by-token streaming for faster AI responses. I implemented persistent session management to ensure a seamless cross-device user experience. I integrated vector databases to provide workspace-level AI memory and persistent conversation history. I ensured consistent AI context across mobile and web clients for seamless multi-device experiences.

I developed a cross-platform workflow dashboard with secure OAuth integration and robust API handling, including rate limits, retries and error reporting.

I implemented secure authentication and storage across mobile and web and built a Stripe-powered subscription and feature-gating system for a multi-tenant SaaS model.

I reduced perceived AI latency by 45% across mobile and web using streaming and optimistic UI, while improving cross-platform session retention by 30% through context synchronization and UX optimization.

I enhanced consistency of AI outputs across devices with a centralized memory system and ensure automation reliability across work flows.

This enabled the successful launch of scalable iOS, Android and Web apps supporting thousands of concurrent users.

This project highlighted my ability to manage complex asynchronous pipelines, optimize cross-platform UX, and deliver reliable, high-performance workflows.

It empowered users with seamless AI-driven experiences across iOS, Android, and Web apps, supporting thousands of concurrent users.