Deep Research Engine

Problem It Solves

Questions such as market structure, React vs. Vue, and AWS vs. Azure usually need more than a short answer or a few links. Ordinary search can mix weak sources, cherry-picked claims, and missing counter-evidence, making conclusions hard to audit. Deep Research Engine turns these tasks into a disciplined workflow where each claim can be traced to sources, confidence, and verification status.

How It Works

Rather than drafting a generic report, it follows an executable research loop:
- Input checks: decides whether the question is complex, verifiable, and non-real-time; if not, it suggests a better research framing instead of forcing an answer.
- Subquestion decomposition: splits the topic into 3-7 searchable subquestions with dependencies and priority.
- Evidence gathering: uses WebSearch and WebFetch to collect official docs, papers, reports, and industry sources, then grades source quality.
- Cross-validation and citation checks: core claims need multiple independent sources; citations must be accessible and supported by page content, otherwise flagged for review.
- Outline-first writing: builds a report skeleton before filling it with evidence, reducing loose structure and missing subtopics.

Boundaries and Caveats

It fits analysis, comparisons, decisions, and trend research that require multi-source support. It is not intended for weather, live prices, simple definitions, or purely subjective questions. It does not guarantee absolute accuracy; instead, it helps assess reliability through confidence levels, evidence labels, and review flags.