Every dive,
watched over.
Abyss.ai is a vision + telemetry command dashboard for autonomous underwater vehicles. It fuses Gemini's multimodal reasoning with live pressure telemetry to detect, score and explain subsea infrastructure risk the moment it appears — not hours later, back on the surface.
Gemini 3.6 Flash is the reasoning engine, not a bolt-on.
R(t) = (α · Cgemini) + (β · |Pnow − Pbase| / Pbase)
Gemini confidence
0.82
Corrosion, frame #4471
Pressure deviation
4.6%
vs. baseline at depth
α = 0.7, β = 0.3 — operator-tunable defaults, not values calibrated against real damage data.
For judges
30-second orientation
Working demo
Sign up, sign in, and the command dashboard is live — not slides.
Full source code
Public repositoryGemini 3.6 Flash core
The reasoning engine driving every anomaly score, not a bolt-on feature.
Fastest way to evaluate: sign in, click “Live Judge Test” in the dashboard, upload any photo of underwater damage or corrosion from your phone, and watch a real Gemini API round-trip return structured JSON in front of you.
The problem
Subsea monitoring is slow, expensive, and human-bottlenecked
Monitoring deep-sea infrastructure today means AUVs surfacing, hard drives being physically extracted, and hours of manual video review before anyone knows if there's a problem. Abyss.ai's automated frame-by-frame analysis flags critical anomalies the moment they appear.
Estimates based on published industry review ratios, not a customer-validated figure.
Manual review cost
~$30,000/vessel-day
Review ratio
4h : 1h
human review per hour of footage
Target reduction
~95%
in review time
Meaningful AI integration
Gemini 3.6 Flash reads the ocean, not just text
Image buffers — camera frames and sonar spectrograms — stream directly into Gemini's multimodal vision pipeline, prompted to act as a Subsea Infrastructure Analyst.
- 01
Frame capture
AUV camera frames and sonar spectrograms stream into the pipeline as raw image buffers.
- 02
Gemini vision
Prompted as a Subsea Infrastructure Analyst, Gemini 3.6 Flash reads each frame for corrosion, leaks and ecological damage.
- 03
Strict JSON schema
The model returns a typed anomaly category, confidence score and explanation — no unstructured text to clean up.
- 04
Risk fusion
Visual confidence is fused with live pressure telemetry into a single operator-facing risk score.
Sensor fusion
Don't trust vision alone
Gemini's visual confidence is fused with live physical telemetry — pressure deviation from the expected baseline at depth — so a visual anomaly is contextualized against what the hardware is actually experiencing.
α and β are exposed as live draggable sliders in the dashboard's Settings panel, so an operator can weight visual vs. physical signal differently depending on mission type.
Variables
Fed into the live risk gauge on every mission
Full transparency
What's real vs. simulated
We'd rather you trust the majority that's real than be impressed by a demo that isn't. Built in 10 hours, here's exactly where the line sits.
Live API call, triggered on demand via “Live Judge Test”
Enforced schema, feeds dashboard directly
Runs live against telemetry + Gemini output
Functional sign-in, not a static mock
No physical AUV in Delhi — local Node.js generator produces realistic pressure/depth streams
Looped underwater footage stands in for a live feed
Local-state UI demonstrating the intended SaaS flow — no live metering or payment wired up
Business model
Built as multi-tenant SaaS, not a single demo app
Clerk handles organization-based sign-in and routing; Supabase Row-Level Security scopes mission logs per tenant, so multiple offshore operators — drilling, telecom — can run isolated missions on the same platform.
Intended pricing: pay-per-mission-hour of footage processed, so operators avoid investing in dedicated on-vessel AI hardware.
If we had more time
Next on the roadmap
- Wire real Stripe metering to the billing dashboard
- Replace the looped video feed with a real-time video stream input
- Validate α/β against a labeled dataset of real subsea damage imagery
- Load-test the Gemini pipeline against AI Studio's free-tier rate limits
Technical architecture
Built entirely on free-tier infrastructure
| Layer | Technology | Purpose |
|---|---|---|
| Frontend | Next.js 14, Tailwind CSS, shadcn/ui | High-contrast operator command dashboard |
| Hosting | Vercel (serverless functions) | API routes and deployment |
| Auth & Multi-tenancy | Clerk | Org-based sign-in and routing |
| AI Engine | Google Gemini 3.6 Flash API | Multimodal visual anomaly analysis → structured JSON |
| Database | Supabase (PostgreSQL) | Mission logs, telemetry history, RLS-scoped tenant data |
| Data Stream | Custom Node.js mock generator | Simulates high-frequency AUV pressure/depth telemetry |
See what's happening beneath the surface.
Sign up, open the command dashboard, and run a real Gemini analysis on your own photo in under a minute.