Deep Research
By Seme Research Team · Updated May 22, 2026
Definition
Deep Research is a multi-round AI investigation process that systematically explores multiple angles of a subject's background to produce a comprehensive identity dossier. Unlike single-query searches, deep research follows a structured methodology: research planning (designing investigation angles — professional, social, biographical, educational), parallel search execution (running multiple queries simultaneously across search engines and databases), gap analysis (evaluating coverage and generating follow-up queries), cross-validation (verifying facts across independent sources), and report synthesis (generating a structured dossier). A typical deep research investigation involves 3-4 rounds, each building on findings from the previous round, covering 15-20 independent sources per subject.
How It Works
The deep research engine operates in five sequential stages. Stage 1 — Research Planning: AI analyzes the input (name, photo, or criteria) and designs a multi-angle investigation plan covering professional history, social presence, biographical data, education, and associations. Stage 2 — Parallel Search: multiple search agents query SearXNG, web search APIs, academic databases, and social platforms simultaneously. Stage 3 — Gap Analysis: AI evaluates coverage completeness and generates targeted follow-up queries for under-explored areas. Stage 4 — Cross-validation: facts are verified across independent sources, classified as corroborated (2+ sources), single-source, or contested. Stage 5 — Report Synthesis: all validated facts are compiled into a structured dossier with timeline, evidence classification, and trust score.
Example
Given "Zhang Wei, former AI researcher at Baidu," deep research might produce: Round 1 discovers his LinkedIn showing 5 years at Baidu, a PhD from Tsinghua, and 12 published papers. Round 2 finds his GitHub with 3 open-source ML projects, a personal blog, and Twitter presence. Round 3 uncovers a patent filing, a conference speaking engagement, and a board position at an AI startup. Round 4 cross-validates all findings, flags that his claimed Stanford visiting scholar stint has no corroborating sources (E5), and generates a final dossier with 87% trust score.
Applications
- •Comprehensive background investigation for executive hiring
- •Investment due diligence on startup founders and key personnel
- •Pre-merger investigation of target company leadership
- •Compliance and regulatory screening for financial institutions
Key Statistics
| Metric | Value | Source |
|---|---|---|
| Investigation Rounds | 3-4 rounds | Seme methodology |
| Sources per Subject | 15-20 | Seme platform data |
| Average Dossier Trust Score | 78% | Seme analytics |
| Time to Complete | 10-30 minutes | Seme platform data |