Face to many is an image-based identification model built for one-to-many face matching: a single input face is compared against a large enrolled gallery to find the closest matches. Designed for practical identification workflows rather than pairwise verification, it detects faces in photos or video frames, extracts compact faceprints (embeddings), and performs highly optimized searches across indexed databases. Users can identify individuals quickly across groups from dozens to millions of enrolled identities. Practical benefits include real-time matching for live video streams, support for multiple enrolled images per person to improve match quality, and reduced bias through synthetic data augmentation that expands variability in lighting, pose and expression. Built-in adversarial augmentation and anti-spoofing techniques improve resistance to presentation attacks and some manipulated imagery, increasing reliability in security-sensitive deployments. The model scales to enterprise and government use cases where speed and low latency are critical: efficient indexing and search algorithms enable rapid lookup even in very large galleries. Typical use cases are security and surveillance, airport screening, access control for large organizations, law-enforcement searches, and crowd monitoring. Limitations and responsible-use considerations are important. Accuracy depends on enrollment and training data quality; poor lighting, occlusions or low-resolution inputs can produce false positives or negatives. Identification raises privacy, ethical and legal issues—deployers should use consent, clear policies, audit logs and legal compliance measures. When used responsibly, Face to many gives organizations a fast, scalable tool to identify individuals across large populations while offering mechanisms to improve fairness and robustness.