SocialSense
One AI dashboard for every social account
SocialSense is an AI-powered social media management platform for influencers, creators, small businesses, and social media managers who need to run multiple accounts from one place — social account integration, sentiment analysis, engagement insights, AI content recommendations, automated responses, and reporting in a unified dashboard. Bitlogs transformed the idea into a functional web MVP with a scalable backend, an AI/ML microservice, and a modern frontend.
ContextSocialSense — AI-powered social media manager
75.6%
Multilingual sentiment accuracy
1
Unified AI dashboard
10+
Core workflows shipped
Inside the product.
A walkthrough of the actual interfaces — merchant portal, operations console, and the two mobile apps shipped end-to-end.
Dashboard Overview
What was broken, risky, or missing.
Managing social media across multiple platforms was time-consuming, repetitive, and hard to scale — users had to check each platform, read comments and messages, gauge sentiment, respond to common questions, write captions, and track performance separately.
Existing tools focused on scheduling, analytics, social listening, or basic sentiment — but none combined an AI-driven workflow for multilingual sentiment, automated responses, content recommendations, and unified inbox management.
The decisions — and the trade-off.
Bitlogs approached SocialSense as a modular, AI-enabled SaaS platform — not a monolith — using a hybrid microservices architecture: a NestJS backend for core business logic, and a separate FastAPI microservice for AI-heavy tasks.
React + Vite for a fast, responsive frontend dashboard.
NestJS, Node.js, TypeScript, PostgreSQL, and Prisma for the core backend.
Redis + BullMQ for asynchronous background processing, so heavy AI tasks don't block API requests.
An independent FastAPI ML microservice for sentiment analysis and prediction.
A fine-tuned XLM-R sentiment model for English, Urdu, and Roman Urdu social text.
Google Gemini + LangChain for AI captions, recommendations, and RAG-based automated responses.
Docker, GCP, and Vercel for a production-ready deployment setup.
The Trade-off
Separating the AI layer from the main backend added communication complexity — but made the system more scalable, easier to maintain, and safer to extend.
Concrete, shipped, documented.
A complete MVP covering the major workflows of a modern social media management platform.
Product
- Auth (registration, login, password reset)
- Social account linking & management
- Unified inbox for connected-platform messages
- RAG-based automated response workflow
- Sentiment dashboard (positive / neutral / negative) with manual override
- Engagement insights dashboard
- AI caption generation from text or image prompts
- Post creation & scheduling, report generation & download
Architecture
- NestJS backend API
- FastAPI AI/ML microservice
- PostgreSQL schema (users, accounts, posts, analytics, reports)
- Redis + BullMQ queue-based processing
- Dockerized deployment; frontend on Vercel, backend & ML on GCP
Key Technical Metric
52% → 75.6%
The fine-tuned XLM-R sentiment model improved from ~52% initial accuracy for Urdu and Roman Urdu to ~75.6% final accuracy after fine-tuning.
Before and after — honestly.
Before
- Multiple platforms managed separately
- Manual reading of comments and audience feedback
- No centralized inbox
- Limited visibility into sentiment trends
- Manual caption writing and manual reporting
- No AI-based automated response workflow
- Hard to analyze Urdu and Roman Urdu text accurately
After
- One dashboard for all connected social accounts
- Unified inbox for messages
- AI sentiment classification for posts and comments
- Dashboards for sentiment trends and engagement insights
- AI-generated captions and recommendations
- Downloadable reports
- Queue-based background processing for responsiveness
- Fine-tuned multilingual sentiment model up to 75.6% accuracy
The result was a working AI-powered social media management MVP that reduced manual effort, improved visibility into audience feedback, and created a foundation for future SaaS growth.
Bitlogs helped turn SocialSense from a complex AI-based idea into a working product with real dashboards, automation, sentiment analysis, and a scalable technical architecture.
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