# RFix (rfix.ai)

Simulates terrain-aware RF scenarios and exports labeled IQ recordings with metadata for testing receivers and training RF machine-learning models.

- Atlas card: https://inite.ai/en/atlas/rfix.ai
- Site: https://rfix.ai
- For: RF engineers validating hardware and DSP algorithms, or building labeled RF datasets for ML.
- Use 0/100 · alive 87/100

## Used for

- generate labeled IQ recordings from simulated RF scenarios
- test DSP algorithms against realistic RF recordings
- batch-produce RF datasets for machine learning training

## Price

Not read yet.

## For developers

- MCP server: ai.rfix/mcp (namespace; open on 2026-10-21, 6 tools)
- Read 2026-10-21

## MCP server, measured daily

Server: https://mcp.rfix.ai/mcp, checked every day since 2026-10-21 (15 checks).

- Answered: 100% of daily checks
- Median first answer: 164 ms
- Sign-in: none
- Context its tool definitions take: about 864 tokens (6 tools)
- Tool definitions: B, 83/100
- Failing "params_described": 6 of 6 tools
- Method: https://inite.ai/en/atlas/mcp/report

## Your data

- Certifications stated: gdpr
- Training on your data: conditional (https://rfix.ai/privacy.html)
- Read 2026-10-21

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Source: INITE Atlas, https://inite.ai/en/atlas/rfix.ai. JSON: https://inite.ai/api/atlas/rfix.ai. MCP: https://inite.ai/api/mcp (atlas_lookup).