Features
Seme provides three core identity investigation capabilities, from photo identification to deep research, meeting diverse needs. All features share the same AI investigation engine and output standardized structured dossiers.
Each feature is built on the core methodology of multi-round search, cross-validation, and evidence classification. The system covers an average of 15-20 independent sources per investigation, using the E1-E5 five-tier evidence classification system to evaluate the credibility of each finding, with an average trust score of 78%. Whether identifying someone from a photo, describing target criteria in natural language, or conducting deep investigations on known individuals, Seme generates complete investigation dossiers in 10-30 minutes.
Seme's three core features share the same AI investigation engine but are each optimized for different use cases. Face Search uses deep learning CNN models (FaceNet/ArcFace) to convert facial photos into 128-512 dimensional embedding vectors, matching via cosine similarity with 99.5%+ accuracy on the LFW benchmark dataset. Semantic Search uses natural language processing to parse user intent and automatically search the internet for matching individuals. Deep Research conducts multi-round comprehensive investigations on known targets across six dimensions: background education, work experience, social profiles, connections, timeline, and relationship graph. All features provide REST API interfaces for integration with existing systems.
Face Search
Identify anyone from a single photograph
Face Search uses AI-powered deep learning to match a facial photograph against databases of known faces. The system converts facial images into high-dimensional embedding vectors using convolutional neural networks (CNNs) like FaceNet and ArcFace, achieving 99.5%+ accuracy on the LFW benchmark. Face search bridges the gap between having a photograph and knowing who the person is — used by security teams, journalists, and HR professionals for identity verification. The technology works by detecting facial landmarks (68-468 key points including eye corners, nose bridge, jawline contour, and lip boundaries), normalizing the face region, and generating a compact 128-512 dimensional embedding vector that captures the unique geometric relationship between these landmarks. This embedding is invariant to lighting changes, minor occlusions, aging (up to 10 years), and angular variations (up to 30 degrees from frontal), making it robust for real-world deployment. Seme integrates face search with its deep research engine — once a face match is confirmed with confidence above 0.85, the system automatically triggers a comprehensive identity investigation that queries 15-20 independent sources, cross-validates findings, and generates a structured dossier with trust score. This end-to-end workflow transforms a single photograph into a complete identity profile in under 30 minutes, a process that traditionally requires days of manual investigation by skilled analysts.
Semantic Search
Find people using natural language descriptions
Semantic Search understands the intent and contextual meaning behind natural language queries to find people matching specific criteria. Unlike keyword-based search, Seme's semantic search interprets complex descriptions like "all researchers who worked at Tencent AI Lab between 2018 and 2022" and discovers matching individuals across multiple data sources — even when no exact keyword match exists. The system uses a multi-stage pipeline: first, a large language model parses the natural language query into structured search parameters (role, organization, time period, location, skills, seniority level); then, parallel search agents query 8-12 platforms simultaneously (LinkedIn, Google Scholar, GitHub, Twitter/X, ResearchGate, ORCID, public records, and news archives); finally, results are ranked using a composite scoring algorithm that weighs semantic similarity (40%), source authority (25%), data freshness (20%), and cross-validation between sources (15%). This approach discovers candidates that keyword-based platforms miss entirely — for example, finding researchers who published papers affiliated with an institution but never updated their LinkedIn profile, or identifying professionals who attended specific conferences without explicitly listing that experience online. Each search result includes a match confidence score and source citations, and users can click any candidate to trigger a full deep research investigation that generates a complete identity dossier with trust score.
Deep Research
Multi-round AI investigation for comprehensive identity dossiers
Deep Research is a multi-round AI investigation process that systematically explores multiple angles of a subject's background. It follows a structured methodology: research planning, parallel search execution across 15-20 independent sources, gap analysis, cross-validation (2+ sources per fact), and report synthesis. A typical investigation involves 3-4 rounds, producing a structured dossier with education, career, social profiles, connections, timeline, evidence classification (E1-E5), and trust score. The investigation engine begins by generating a research plan that identifies 5-8 investigation angles (professional history, social presence, biographical data, education verification, association mapping, media coverage, legal records, and financial connections). Each angle is assigned to a dedicated search agent that queries multiple sources in parallel using both SearXNG meta-search (aggregating 70+ search sources) and OpenAI Web Search API. After each round, a gap analysis module evaluates coverage across all angles, identifies missing information, and generates targeted follow-up queries for the next round. Cross-validation is the core differentiator: every extracted fact is compared against independent sources, classified as corroborated (2+ independent sources), single-source (1 source only), or contested (conflicting sources). Corroborated facts receive high confidence weighting (5x for E1 government sources, 3x for E2 authoritative media, 2x for E3 professional platforms), while single-source facts are flagged for manual review. The final trust score is calculated as a weighted average: source quality (40%), cross-validation rate (35%), and information completeness across 8 dossier sections (25%). A score above 80% indicates high confidence with minimal gaps; 60-80% suggests moderate confidence with some unverified claims; below 60% indicates significant information gaps requiring additional investigation.
User Feedback
"The face search feature helped me quickly confirm a candidate's identity, saving significant manual verification time."
"Semantic search lets me describe criteria in natural language to find matching people — much more precise than traditional search engines."
Platform Data
Seme has helped security teams, journalists, and HR professionals complete thousands of identity investigations. The platform is based on OSINT best practices, all data sources are publicly available information, and it complies with GDPR data processing requirements.
Related Resources
- Investigation Methodology — Learn about the five-step investigation process and evidence classification system
- Comparison Analysis — Seme vs traditional methods and other tools across 12 dimensions
- Use Cases — Talent recruitment, corporate security, due diligence and more
- Glossary — 25+ identity intelligence professional term definitions
Frequently Asked Questions
What are Seme's core features?▾
Seme offers three core features: Face Search (identify from photos), Semantic Search (discover people via natural language), and Deep Research (multi-round investigation on known targets).
What is face search accuracy?▾
Seme's face search achieves 99.5%+ accuracy on the LFW benchmark, using 128-512 dimensional deep learning embedding vectors for matching.
What is the difference between the three features?▾
Face Search is for "I have a photo, who is this?"; Semantic Search is for "find people matching criteria"; Deep Research is for "I know this person, tell me everything." All three output the same structured investigation dossier.
How long does an investigation take?▾
A typical investigation takes 10-30 minutes, including 3-4 rounds of automated search and verification. Complex targets may take longer. Users can manually add additional search rounds to improve coverage.
Get Started with Seme
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