# NeuroCast AI — Agentic Stroke Coordination + Verified Transfer Packets
> **One-line summary:** NeuroCast AI is an agentic, real-time stroke coordination layer that turns messy clinical inputs + remote video telemetry into **interpretable triage decisions** and a **NeuroCast Verified Transfer Packet (VTP)** that is verifiable, tamper-evident, and easy to share across care teams.
---
## Why I built this
Stroke care is brutally time-sensitive. Minutes lost before a specialist even sees the patient translate into worse outcomes and higher cost. What bothered me most wasn’t “lack of AI”—it was **lack of coordination**:
- The *same* information exists (notes, meds, timeline, vitals), but it’s scattered.
- Early triage is inconsistent outside major centers.
- Handoffs are often non-standard and hard to trust.
I wanted to build something that feels like the future of healthcare operations: an **agent** that can watch real-time signals, summarize the truth, show its work, and produce a transfer artifact others can rely on.
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## What it does (in human terms)
NeuroCast AI is **not a diagnosis system**. It’s a **coordination + triage acceleration tool** that helps clinicians, coordinators, and caregivers answer:
1. **What happened?**
2. **Why does it matter?**
3. **What should happen next?**
It provides:
- **Agentic pipeline visibility** (every step emits events live so you see the “agent thinking”)
- **Remote Home Check-In telemetry** via real-time video understanding (camera or uploaded video)
- **Numeric decision workflow** for structured reasoning you can trust
- **NeuroCast Verified Transfer Packet (VTP)**: a signed, hash-verifiable packet designed to be immutable/auditable over time
---
## How the system works
### 1) Inputs
NeuroCast accepts two major input streams:
- **Clinical inputs**: past surgeries, medication lists, EHR snippets, EMS notes, vitals, timeline events, imaging notes (messy text + structured data).
- **Home Check-In telemetry**: real-time camera feed or uploaded video to capture symptoms and motion patterns remotely.
### 2) The agentic pipeline (visible end-to-end)
The pipeline is built as discrete steps that emit a **live event stream**:
- **INGEST**: normalize structured fields and attach case metadata
- **REDACT**: remove PHI before any downstream processing
- **COMPRESS**: TokenCo compresses messy text to reduce cost and focus on critical context
- **EXTRACT**: derive evidence-backed risk flags (interpretable)
- **NUMERIC**: Wood Wide AI performs numeric reasoning on structured/time-series features
- **ROUTE**: deterministic gating into actionable next steps (e.g., HOLD / ESCALATE / PROCEED)
- **PACKET**: generate the transfer packet
- **VTP**: cryptographic verification + optional immutable commitment
The event stream is delivered using **Server-Sent Events (SSE)** so the UI can render “agent actions” in real time.
---
## Sponsor tracks & how they’re used
### Overshoot.ai — Home Check-In (Real-time vision telemetry)
I built **NeuroCast AI Home Check-In** to support remote monitoring:
- A user can run a **live camera stream** or upload a **video file**
- Overshoot runs a vision model over time windows and returns **text or JSON**
- These detections can be interpreted into a triage signal and an alert payload
This gives NeuroCast a “real-world perception layer” rather than only text inputs.
### Wood Wide AI — Numeric Trust Layer
Wood Wide is used as the numeric reasoning engine for structured data:
- Converts time-series and event features into stable numeric outputs
- Enables consistent, interpretable numeric intelligence (prediction + clustering)
- Helps produce a decision workflow that can be tested and monitored
Instead of relying on an LLM to “do math,” numeric reasoning is handled by a dedicated layer designed for reliability.
### Kairo — Smart Contract Security Gate + Verifiability Architecture
The **NeuroCast VTP** is designed to be committed into an immutable audit trail (on-chain or similar). Kairo is integrated as the security gate:
- Before deploying or committing contract logic, Kairo runs **analysis** and returns a decision:
- `ALLOW`, `WARN`, `BLOCK`, `ESCALATE`
- This creates a “trust boundary” so we don’t ship unsafe contract code
Even when commitment is simulated locally, the architecture is set up so Kairo can be placed in CI and deploy gates.
### Token Company (TokenCo) — Prompt compression for cost + speed
Clinical text is often long and redundant. Compression helps:
- Reduce token usage and latency
- Retain the high-signal medical details
- Keep downstream agent logic faster and cheaper
### TRAE AI IDE — How it was built
TRAE was central to the workflow:
- Iteration speed for full-stack changes
- Rapid refactors across the UI + API routes
- Tight feedback loop to keep the prototype functional
I treated the IDE as an “AI development engineer” to keep momentum across multiple sponsor integrations.
---
## What I learned (the biggest takeaways)
1. **“Agentic” has to be visible to be believable.**
Users trust a system more when they can see actions happening step-by-step.
2. **Numeric reasoning should not be done by text models.**
LLMs are great for language, but decision-grade quantitative work needs deterministic outputs.
3. **Trust is a product feature, not just a security feature.**
The Verified Transfer Packet is not “extra”; it’s what makes cross-org sharing realistic.
4. **Medical-adjacent demos require a safety posture.**
Clear redaction rules + synthetic-only messaging matters for credibility.
---
## Challenges & how I overcame them
### 1) Port conflicts and multi-app structure
The repo evolved with a prototype UI and a Next.js app router backend. Ensuring the frontend hit the right API routes required careful dev setup and consistent base URLs.
### 2) Real-time streaming reliability
SSE is simple and reliable, but reconnect logic and event replay needed to be designed so demos wouldn’t break mid-stream.
### 3) Making sponsor integrations demoable
Integrations only “count” if judges can see them. I had to push outputs into the UI:
- TokenCo compression metrics
- Wood Wide numeric outputs
- VTP verification steps
- Security-gate architecture for Kairo
- Live telemetry feed for Overshoot
### 4) Safety constraints (PHI)
I implemented an explicit REDACT step and a policy that raw packet text never becomes UI evidence or logging material.
---
## A tiny bit of math (why minutes matter)
If the cost of delay scales roughly linearly, then:
\[
\text{Cost Increase} \approx \$10{,}000 \times \left(\frac{t}{10\ \text{minutes}}\right)
\]
So a 20-minute reduction in “decision + reporting delay” is roughly:
\[
\$10{,}000 \times 2 = \$20{,}000
\]
This isn’t claiming exact savings per patient in all contexts—it's an intuition for why workflow acceleration has real economic impact.
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## What’s next (post-hackathon)
- Expand Home Check-In prompts + schemas to cover more symptom patterns (e.g., fall detection, confusion, speech difficulty).
- Add alert routing integrations (care team paging, SMS, facility workflows).
- Make VTP commitment real on a testnet and enforce Kairo gates in CI/CD.
- Improve model calibration and interpretability of the numeric outputs with richer feature attribution.
---
## Closing
NeuroCast AI is built around a simple idea:
> Make the **right decision** easier to reach, faster to communicate, and easier to trust.
That’s what the agentic pipeline + numeric trust layer + verified transfer packet is designed to deliver.
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