AuScan is a fully browser-deployed AI intelligence system that identifies geological terrain signatures associated with gold mineralization using multi-sensor satellite imagery, then applies those learned signatures to score unexplored regions for prospectivity. Built entirely as a React artifact running inside the Claude.ai sandbox, AuScan operates under severe network constraints — only api.anthropic.com is reachable — and turns that constraint into an architectural advantage: every capability, from geocoding to MRDS record lookup to spectral analysis, routes through a single, reliable AI endpoint.
During live field testing, AuScan produced a 72% HIGH-confidence porphyry-epithermal hit at 42.609°N, -119.299°W in Harney County, Oregon — a location subsequently confirmed against USGS MRDS records as falling within the documented Pueblo Mining District, a past gold producer with 97.81% of historic claims now closed and no active modern exploration. The system identified a real target that the market has not.
Gold exploration has traditionally required either expensive field crews doing surface sampling across vast, remote terrain, or enterprise remote sensing software costing tens of thousands of dollars annually and requiring specialist operators. Neither option is accessible to small miners, junior exploration companies, or independent prospectors.
The satellite data exists — Landsat, Sentinel-2, ASTER, and NASA's EMIT hyperspectral instrument collectively image the entire Earth's surface in geologically meaningful spectral bands, all freely available. The missing layer is the intelligence to interpret it. AuScan fills that gap. It delivers geologist-level multi-sensor terrain analysis to anyone with an iPad and an Anthropic API key, for the cost of a few API calls per scan.
AuScan runs entirely inside the Claude.ai artifact sandbox — a browser-isolated React environment with no server, no local runtime, and no external network access except api.anthropic.com. This constraint drove every architectural decision in the system.
// The key architectural insight: while the browser cannot fetch external URLs directly, Claude Vision fetches image URLs server-side before analysis. AuScan passes satellite image URLs to Claude — bypassing the sandbox's network restrictions entirely for read-only data access.
| Region | Country | Deposit Type |
|---|---|---|
| Witwatersrand Basin | South Africa | Paleoplacer |
| Carlin Trend | Nevada, USA | Carlin-Type |
| Kalgoorlie Super Pit | W. Australia | Orogenic Lode |
| Muruntau Mine | Uzbekistan | Orogenic |
| Grasberg Complex | Indonesia | Porphyry Cu-Au |
| Oyu Tolgoi | Mongolia | Porphyry Cu-Au |
| Cerro Negro | Argentina | Epithermal |
| Red Lake District | Canada | Greenstone Belt |
| Kibali Mine | DRC | Orogenic |
| Pueblo Viejo | Dominican Republic | Epithermal |
AuScan treats multiple satellite data sources as parallel intelligence inputs to Claude Vision rather than as raw data to process programmatically. Each image source is labeled with its sensor type and spectral characteristics before being passed to Claude, enabling appropriate geological interpretations per sensor. A data completeness score is reported at the top of every analysis — a scan with only ESRI Visual loaded reports its limitation explicitly rather than silently.
| Signal | Sensor | Satellite Indicator |
|---|---|---|
| Iron oxide gossan | RGB · Landsat B4/B2 | Red-orange discoloration |
| Clay / sericite | SWIR · ASTER | Cyan-white in false color |
| Silicification | RGB · SWIR | Pale grey-white bleaching |
| Chlorite propylitic halo | SWIR · Sentinel-2 | Green peripheral zone |
| Fault lineaments | All sensors | Linear features cutting terrain |
| Circular intrusive morphology | All sensors | Elliptical porphyry geometry |
| Fault-controlled hydrology | RGB · Sentinel-2 | Water bodies following structural fabric |
The Search tab accepts any location input — city name, ZIP code, region name, or decimal coordinates. Geocoding runs through Claude (the only reliable external call available in the sandbox), which returns precise WGS84 coordinates for city names, mining district names, geological formation names, and international locations that no standard geocoding API covers.
Grid density is user-configurable: 2×2 (4 points), 3×3 (9), 4×4 (16), or 5×5 (25). Each high-score result carries a USGS MRDS lookup button that queries Claude's knowledge of the Mineral Resources Data System — nearest named mining district, known site names and deposit types, historic production records, BLM claim status, and exploration gap assessment.
AuScan was designed assuming that external data sources will fail. Every resilience mechanism was built in response to real failures encountered during development. The system is not designed to avoid failure — it is designed to find failure faster than it blocks progress.
The standard approach to canceling hung network requests — AbortController — does not interrupt Response.json() body parsing. A fetch that has received response headers but is still streaming the body will continue indefinitely after abort() is called. This caused all early timeout mechanisms to fail silently — scans would appear frozen at 97% for nine minutes or longer.
| Mechanism | Limit | Behavior on Trigger |
|---|---|---|
| Per-scan hard cap | 5 minutes | Save partial result, continue scan-all |
| Per-search-point cap | 2 minutes | Record zero score, continue grid |
| Source fetch timeout | 8 seconds each | Skip source, analyze with what loaded |
| STOP button | Immediate | Promise.race() wins, state resets cleanly |
| Force Reset (stuck UI) | 2 min elapsed | Manual state reset, no page reload needed |
GIBS rate-limiting — discovered in production. NASA's GIBS WMS endpoint rate-limits after 1–2 requests from any given origin, causing Claude's server-side image fetching to hang indefinitely. GIBS is disabled by default with a warning in Settings. The lesson: a public API that works in a browser may still stall when Claude's servers try to fetch it.
The Claude.ai artifact sandbox presented three hard constraints that required non-obvious solutions. In each case the constraint was discovered through failure in production, not anticipated in design. Each produced a solution that works better than the original plan.
AuScan has produced confirmed-valid prospectivity results against known geological records. Both results below were produced during live operation on iPad, with no pre-knowledge of MRDS records for those specific coordinates.
| Operation | Typical Duration |
|---|---|
| Single scan · ESRI only | 15–30 seconds |
| Single scan · multi-sensor | 45–120 seconds |
| Full 10-region scan-all | 8–20 minutes |
| Pattern synthesis · 10 regions | 60–120 seconds |
| Search grid · 3×3 · 9 points | 5–15 minutes total |
| MRDS lookup per hit | 10–20 seconds |
AuScan is a human-in-the-loop tool. It does not place claims, file paperwork, or make purchasing decisions. Every scan is manually triggered. Every result is reviewed before action. The system produces intelligence; a person acts on it. This is by design.
The natural evolution of AuScan is a port to the CLAW architecture — the Mac Studio-based agentic fleet running SSIA, BEEF, and RED — which removes all sandbox constraints and enables direct API access to every data source currently approximated through Claude Vision.
AuScan was designed, engineered, and iterated entirely inside the Claude.ai conversation interface — no external IDE, no local development environment, no build toolchain. The entire codebase lives in a single React JSX file deployed as a Claude artifact.
Development proceeded through live testing on iPad in the field, with every architectural decision validated against real failure modes rather than theoretical ones. The GIBS rate-limit issue was discovered by watching scans freeze. The AbortController / .json() body-parsing problem was diagnosed from user-reported nine-minute hangs at 97%. The geocoder failures were found by attempting real searches on real hardware.
This is the correct way to build agentic tools: ship early, observe failures, engineer precise fixes. The resilience architecture in AuScan v2.1 exists because v1.0 broke in every way it could. The system that survived those failures is worth more than one that never faced them.
AuScan is part of the Agentic Womb project — a suite of production autonomous agents built by Adam R. Cagle of Agency689 / Agentic689, operating on the Anthropic Claude API alongside SSIA (equity intelligence), BEEF (multi-asset anomaly trading), RED (autonomous Reddit persona), and the Book and Music Agents.