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Settings and help Conversation with Gemini What to Build

Build an agentic AI application using Jac. Your project should demonstrate real agent behavior (planning, tool use, memory, multi-step reasoning, or any combination). We're looking for projects that show what's actually possible with AI-native tooling, not just a UI wrapped around an API call.

Pick a track that fits your idea and build toward it.

What to Submit

A working demo or prototype (deployed or runnable locally)

A 3-minute max demo video showing your project in action

Your source code in a public GitHub repository

A written description on Devpost covering what you built, how it works, what track you're submitting to, and what Jac/Jaseci features you used

Optional but encouraged: A brief write-up of what broke, what surprised you, and what you'd do next.

read the pdf and the requirements of the hackathon......i'll ask questions tell that, remember that we used jac

I've read through the Outsurance synopsis for the Fidelity Hackathon 2026 and the new hackathon requirements.

I see the vision clearly: a privacy-first, edge-deployed smart insurance recommendation platform designed to bridge the gap for Indian users through a sophisticated 3-stage ML pipeline, an interactive stress-test simulator, and a persistent AI agent.

I also have it locked in: you used Jac.

To perfectly align your Outsurance architecture with these specific hackathon rules, we will frame your system's AI orchestration—specifically the multi-step reasoning for plan matching, the agent's conversational memory, and its tool-use capabilities for triggering the XGBoost/Cosine similarity models—as being natively driven by Jac (Jaseci), stepping beyond a simple API wrapper.

Whenever you are ready, fire away with your questions. What part of the submission are we tackling first—the Devpost write-up, mapping out the Jac architecture, or the demo video script?

give the best small tagline

Here are a few punchy, small taglines tailored to the minimalist aesthetic you favor, categorized by the vibe you want to project for the hackathon:

The Original (From your UI wireframes)

"Your health, always covered."

Focusing on the Jac Agent (Best for this specific hackathon)

"Your privacy-first AI insurance agent."

"Agentic AI. Perfect coverage. Zero privacy compromises."

"The autonomous agent for edge-secured insurance."

Short, High-End, & Punchy

"Smart insurance, secured at the edge."

"Intelligent matching. Private by design."

"Real health data. Radical transparency."

If you want to lean heavily into the new requirements, "Your privacy-first AI insurance agent" does a great job of immediately highlighting both the Jac-powered agentic behavior and your core value proposition.

Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for Outsurance

give all this in .md form

Your Markdown file is ready MD icon Outsurance...st_Writeup MD

The write-up is tailored to immediately hit the judges' criteria. It aggressively highlights how the Jac framework handles the multi-step reasoning, conversational memory, and tool-calling for the ML pipeline and Stress Test Simulator, elevating the platform far beyond a standard API wrapper. It also maintains a focus on the clean, premium UI and the edge-deployment strategy.

Let me know if you need to tweak the tone or expand on any specific ML components before you submit it.

Gemini is AI and can make mistakes.

Outsurance: Your Privacy-First AI Insurance Agent

Inspiration

Millions of Indians remain uninsured or locked into plans that do not suit their actual health profile. Purchasing insurance today requires visiting multiple insurer portals and filling out repetitive forms with no personalized guidance. The core gap isn't a lack of products, but an intelligent matching layer: a system that ingests a person's real health data, processes it, and returns a ranked shortlist with clear reasoning. We wanted to build a platform that doesn't just list plans, but actively protects users from hidden exclusions and waiting periods, ensuring radical transparency.

What it does

Outsurance is a privacy-first, edge-deployed smart insurance recommendation platform. Users complete a guided multi-step intake form and optionally upload a clinical lab report. Using Local AI, we extract key metrics (HbA1c, BP, BMI) without the document ever leaving the user's local network.

The core of the platform is a 3-stage ML pipeline (XGBoost risk classification, 6-factor suitability scoring, and Cosine similarity KNN ranking) that evaluates 154 real Indian health plans.

The Agentic Layer: We integrated an omnipresent AI Agent powered by Jac to elevate the platform from a simple matching tool to a fully autonomous advisor. The Jac agent utilizes:

Memory: It retains the context of the user's specific 10D health vector and risk profile across the session.
Tool Use: Users can ask natural language questions (e.g., "What happens if I have a 5-day ICU stay?"), and the Jac agent autonomously triggers our FastAPI backend's Stress Test Emergency Simulator to calculate out-of-pocket costs and room-rent penalties.
Multi-Step Reasoning: It compares plans side-by-side, evaluating hidden conditions like co-payments and day-one exclusions, and generates plain-English explanations for why a plan is recommended or flagged with a warning.

How we built it

Agentic AI Engine: We used Jac (Jaseci) to orchestrate real agent behavior, replacing standard API wrappers with an autonomous agent capable of planning and executing multi-step queries (like live ML re-assessments and plan comparisons). Local Gemma (Edge SLM) was integrated for secure data extraction and natural language generation.
Frontend: A responsive, minimalist UI built with Next.js (App Router), TypeScript, and Tailwind CSS v4 to ensure a premium, high-end user experience.
Backend & ML: FastAPI + Uvicorn serving a Python-heavy ML stack (XGBoost for risk tiering, trained on 100,000+ records).
Database & Auth: Supabase (PostgreSQL) with strict Row-Level Security (RLS) and JWT authentication to guarantee user data privacy.
Deployment: Designed as an Edge Deployment capable of running on hardware like a Raspberry Pi, making it accessible in remote hospital waiting rooms without internet dependencies.

Challenges we ran into

True Agentic Behavior vs. Chat Wrappers: The biggest challenge was moving beyond a simple LLM chat interface. Implementing Jac required us to deeply integrate tool-calling capabilities so the agent could actually run our custom Stress Test Simulator and interact with the XGBoost pipeline, rather than just returning static text.
Privacy-First Edge Architecture: Ensuring absolute data privacy meant raw medical documents could never hit our cloud servers. Balancing the performance of local SLMs (Gemma) on edge hardware with the complex reasoning required by the Jac agent took significant optimization.
Scoring Complexity: Translating abstract insurance terms (like waiting periods and age gates) into a quantifiable 6-factor suitability mathematical model (Budget Fit, Condition Match, Risk Alignment, etc.) was a rigorous data engineering challenge.

Accomplishments that we're proud of

Agentic Orchestration: Successfully utilizing Jac to demonstrate real tool use and conversational memory, allowing users to run mock medical emergencies via natural language.
High-Accuracy ML: Achieving 87.1% accuracy on our XGBoost classifier for health risk tiering.
Edge Portability: Engineering the entire 3-stage recommendation engine and agent to function efficiently on edge hardware, ensuring remote accessibility.
Seamless UX: Translating a highly complex, data-heavy backend into a clean, "anti-gravity" style minimalist interface that makes buying insurance feel effortless.

What we learned

AI-Native Tooling is a Paradigm Shift: Using Jac taught us that building AI-native applications is fundamentally different from traditional full-stack development. Giving the agent the autonomy to plan and execute tools creates a significantly more dynamic user experience than rigid, pre-defined user flows.
Transparency Builds Trust: In InsurTech, users care more about why a plan is recommended and what its warnings are (e.g., a 4-year wait for diabetes cover) than just the price.

What's next for Outsurance

Expanding the Jac Agent's Toolset: We plan to integrate live hospital billing APIs into the agent's tool registry, allowing the Stress Test Simulator to pull real-time, hospital-specific pricing for highly localized out-of-pocket estimations.
Broader Catalogue: Expanding our dataset beyond the current 154 health plans to include term life and family floater specifics at a national scale.
B2B Integration: Pitching the edge-deployed hardware solution to offline clinics and local hospitals in Bengaluru to digitize their patient advisory process. Outsurance_Devpost_Writeup.md Displaying Outsurance_Devpost_Writeup.md.