I build the tooling
that makes AI agents
dependable.

The problem

An agent that researches the open web needs many sources, and most search tooling is non-deterministic and unauditable — you can't tell why a result surfaced, can't trust it twice, and wiring up a separate integration per source is its own project.

Key capabilities

• 01Quality scoring. Every result carries a 0–100 tier / freshness / corroboration / citation score, with an aggregate confidence signal per response.
• 02Conflict detection. Surfaces numeric, date, and polarity disagreements across sources as a first-class signal instead of averaging them away.
• 03Entity profiles. Fans out to 8 sources in parallel and returns one unified report with confidence.
• 04Production hygiene. Per-tool timeouts, rate limiting (100 req/min), response-size caps, and middleware observability.

How it works

One MCP connection exposes 9 specialized tools that fan out across 5 web engines, 9 social platforms, 5 news sources, 5 academic databases + 4 dataset hubs, GitHub, and documents (with OCR). No LLM runs inside the server — every tool returns deterministic output with a parseable structuredContent dict, so agents chain results without regex-parsing prose.

Connect