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In production, an AI data agent that's plausibly wrong is worse than no agent at all: inflated revenue, dropped customers, double-counted metrics that pass review and surface in a board deck weeks later. SignalPilot produces data that's correct, not just plausible, and stays safe on a real warehouse because AutoFyn, the security agent behind disclosed Next.js and MetaMask bugs, audits it without stopping. The benchmarks (#1 on Spider 2.0-DBT, 96.9% on ADE-Bench) just prove it.

SignalPilot resolves 62 of 64 ADE-Bench tasks, the highest score on dbt Labs' analytics engineering benchmark. Three skills and one sentence took us from 71% to 96.9%.

Today, we're thrilled to announce that SignalPilot has claimed the #1 spot on the Spider 2.0-DBT leaderboard — beating JetBrains' Databao by over 7 points. By solving dbt, the hardest problem in the transformation layer, we proved AI agents can be trusted with enterprise data pipelines when they have the right guardrails.

From empty directory to 16 models, 25 passing tests, and full documentation — in a single conversation. A step-by-step trace of how Claude Code and SignalPilot MCP built a complete dbt project.

Why governed database access through MCP changes the AI-assisted analytics engineering workflow — from AST-level query parsing to automatic LIMIT injection and full audit trails.