CareConnect Pro
A community engagement and assistance platform that uses LLMs to help users ask better questions, create posts faster, and find more relevant answers through contextual retrieval.
Product Overview
Problem
Communities and support platforms often struggle with repetitive questions, low-quality posts, slow responses, and difficulty finding relevant prior discussions.
Solution
An AI-assisted experience that helps users craft better posts, get accurate contextual answers, and quickly surface relevant knowledge from existing community content.
Context-aware Chat
Chat assistant for Q&A and guidance — every response anchored in community context.
RAG Retrieval
Surfaces relevant community knowledge to cut duplicate questions and reduce hallucination.
Role-Based Dashboards
Tailored views for users, researchers, and developers — with visibility into predictions and logs.
Moderation-friendly
Structured conversation flow that's safe, reviewable, and easy to moderate.
Impact
Replace these placeholders with real metrics (GA4, DB counts, logs).
250+
Users / Testers
30+
Communities Supported
< 2s
Avg Response Time
120+
Weekly Active Users
Adoption Signals
- Users spend less time drafting posts and receive better-structured responses
- Higher engagement on posts created with AI suggestions
- Reduced duplicate questions through retrieval of similar discussions
Team
Madan Venkatesh
Senior Software Engineer — AI Specialist
What they do
Owns end-to-end product (UX → APIs → LLM orchestration). Builds mood detection, meal logic, dashboards, and integrations.
Community impact
Drives weekly demos, ships new features, and supports other teams with reusable AI patterns (RAG/chat, evals, deployment).
Shree Vamshika Manandi
Senior Software Engineer — Full Stack
What they do
Implements UI flows, onboarding, responsive design, and performance optimizations.
Community impact
Maintains shared UI components, helps other B360U teams move faster with consistent design patterns.
Sairam Konda
Senior Software Engineer — Full Stack
What they do
Improves recommendation quality, evaluates mood signals, and iterates on explainability (e.g., reasons behind suggestions).
Community impact
Creates evaluation dashboards and documentation so other teams can reuse model testing practices.
Other Key Points
- LLM-powered suggestions for posts/questions + smart replies based on community context
- RAG-style retrieval of relevant content for more accurate answers and less hallucination
- Role-based dashboards (User / Researcher / Developer) with visibility into predictions and logs
- Moderation-friendly design: keeps conversation safe, structured, and easy to review