BlueCurrent is a conversational, multi-agent AI platform that fuses satellite Earth Observation data, AIS vessel tracks, bathymetric sonar, and weather advisories — letting fishermen, coastal authorities, and researchers simply ask, in plain language:
💬 "Where's today's nearest fishing zone?" 💬 "Is it safe to sail tomorrow?" 💬 "Are there any cyclone alerts near me?"
...and get back an explainable, map-backed, evidence-cited answer — not just raw data.
Built for Smart India Hackathon 2026 · Problem Statement 26176 — ORCA (Dept. of Space / ISRO) · Team ResQ
| Category | Capabilities | |
|---|---|---|
| 🗣️ | Conversational AI | Marine AI chatbot · Multi-agent status/design view · Explainable AI evidence display |
| 🗺️ | Situational Awareness | Unified interactive marine map · Real-time hazard alert panel · Automatic geotagging |
| 🐟 | Fisheries Intelligence | Potential Fishing Zone (PFZ) recommendations · Fish reproductive-habitat mapping |
| 🛟 | Safety & Risk | Marine safety score · Smart geofencing · Safe-route optimisation |
| 🛢️ | Environmental Monitoring | Oil-spill detection workspace · Underwater debris / sonar-anomaly detection |
| 🚢 | Fleet Intelligence | AIS vessel-correlation workflow |
| 📡 | Operations | Automated operational reports · Low-connectivity / offline-ready mode |
flowchart LR
A["🗣️ User Query<br/>(Any Indian Language)"] --> B["🧠 Planner /<br/>Orchestrator Agent"]
B --> C["🤝 Specialist Agents<br/>Weather · PFZ/Ocean<br/>Geo-Risk · Vessel/AIS"]
C --> D["🛰️ Multi-Source<br/>Data Fusion<br/>Satellite EO · AIS · Sonar"]
D --> E["💡 Explainable Response<br/>Chat · Map · Alerts"]
style A fill:#065A82,color:#fff,stroke:#00d4aa,stroke-width:1px
style B fill:#21295C,color:#fff,stroke:#00d4aa,stroke-width:1px
style C fill:#1C7293,color:#fff,stroke:#00d4aa,stroke-width:1px
style D fill:#1C7293,color:#fff,stroke:#00d4aa,stroke-width:1px
style E fill:#065A82,color:#fff,stroke:#00d4aa,stroke-width:1px
A Planner Agent decomposes every query and coordinates specialist agents that independently retrieve and correlate live multi-source marine data — then a synthesis layer builds one explainable, cited response with maps and alerts.
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Frontend
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Backend
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AI Layer
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Data Sources
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Deployment: Frontend on Vercel · Backend on Render
- Node.js 18+
- npm
git clone https://github.com/Godson-82/BlueCurrent.git
cd BlueCurrent
npm install # frontend deps
cd server && npm install # backend deps
cd ..Root .env (frontend — see .env.example)
VITE_API_URL=http://localhost:4000
VITE_WS_URL=ws://localhost:4000server/.env (backend — see server/.env.example)
ANTHROPIC_API_KEY=
ANTHROPIC_MODEL=claude-haiku-4-5-20251001
GROQ_API_KEY=
GROQ_MODEL=qwen/qwen3.8-27b
PORT=4000
⚠️ Never commit real API keys. Both.envfiles are gitignored.
# Terminal 1 — backend
cd server && npm start
# Terminal 2 — frontend
npm run devVisit http://localhost:5173
| Frontend | Auto-deploys to Vercel on push to main |
| Backend | Auto-deploys to Render on push to main (root: server/) |
MIT — see LICENSE.

