SkyBrief: I Built a Drone Airspace Intelligence Tool with Gemma 4 26B MoE

This is a submission for the Gemma 4 Challenge: Build with Gemma 4

What I Built

SkyBrief is an AI-powered drone airspace intelligence assistant built with Gemma 4 26B MoE. Drop in a NOTAM, airspace map, or type a plain-English query — Gemma 4 reasons over it and returns a structured brief with conflict detection and a compliance checklist.

The problem it solves: NOTAMs (Notices to Air Missions) look like this:

!PHX 05/014 PHX NAV ILS RWY 8 LOC UNUSABLE 140DEG CW 220DEG BEYOND 18NM
BLW 4000FT MSL 2605161400-2605171400

A Part 107 pilot can parse that. A logistics coordinator managing 40 drone deliveries cannot. SkyBrief uses Gemma 4's reasoning to translate complex airspace data into plain-English operator briefs — with specific conflicts flagged and a ready-to-use compliance checklist.

Demo

🔗 Live app: https://skybrief-nine.vercel.app

Try these queries to see all three status states:

🟢 CLEAR:

I want to fly a drone in a rural area, Class G airspace, 200ft AGL, no airports within 10 miles, clear weather, daytime. Part 107 certified pilot. Is this flight legal?

🟡 CAUTION:

I need to fly at 350ft AGL, 4 miles from a Class D airport, winds 15kt gusting 22kt, daytime, Part 107 certified. What do I need to proceed?

🔴 RESTRICTED:

I want to fly over a stadium during a live NFL game, 500ft AGL, no waiver, downtown Chicago near O'Hare Class B airspace. What are my restrictions?

Code

🔗 GitHub: https://github.com/ashishjsharda/skybrief

Tech Stack:

• Model: Gemma 4 26B MoE via OpenRouter (free tier)
• Backend: Vercel Edge Functions — API key stays server-side, never exposed
• Frontend: Vanilla HTML/CSS/JS — zero dependencies, zero build step
• License: Apache 2.0

How I Used Gemma 4

I chose Gemma 4 26B MoE specifically — and the choice was deliberate:

Model	Why I considered it	Why I didn't pick it
Gemma 4 E4B	Runs on mobile / edge hardware	Not enough reasoning depth for multi-NOTAM conflict detection
Gemma 4 31B Dense	Maximum capability, 256K context	20GB+ VRAM, overkill for most queries
Gemma 4 26B MoE	Near-31B quality, 12GB VRAM, 256K context	— This is the one

Why MoE wins for this use case:

• Only ~4B parameters active per inference — near-31B reasoning at a fraction of compute
• 256K context window fits entire NOTAM batches in one prompt — no chunking, no lost context
• 86.4% on agentic tool-use benchmarks (up from 6.6% on Gemma 3) — handles multi-constraint airspace reasoning
• Apache 2.0 — can be deployed on-premises, air-gapped for aviation compliance environments where external APIs aren't allowed

How Gemma 4 powers SkyBrief:

1. Operator types a query or uploads a NOTAM/airspace map
2. Vercel Edge Function sends it to Gemma 4 26B MoE with a FAA Part 107 system prompt
3. Gemma reasons through airspace rules, identifies conflicts, generates checklist
4. Returns structured JSON: status (CLEAR/CAUTION/RESTRICTED), summary, conflicts, checklist

Gemma 4's thinking mode is the key unlock here. For complex multi-NOTAM scenarios, it reasons step-by-step through airspace geometry — producing verifiably correct outputs that single-pass models couldn't match. Previous models hallucinated on technical specifics or lacked reasoning depth for multi-constraint airspace scenarios. Gemma 4 26B MoE solved both.