Workflow Guides

Tag peloton shots with bibs on BACK of jersey (hidden by saddle bags). Master moto-photographer workflow and deliver stage results within hours. AI detects all 150+ riders.

The complete workflow for tagging DTM and GT racing photos with AI. Process 2,000+ photos per weekend managing multiple classes and gentleman drivers.

Tag karting race photos 10x faster. Handle hand-written numbers, DIY placement, mud-covered karts, and parental delivery expectations with AI-powered workflow.

Tag 20,000-100,000+ marathon photos in hours, not days. Master multi-bib detection, folded bibs, and finish line chaos with AI vision. Same-day delivery for runners.

The complete workflow for tagging MotoGP, Moto2, and Moto3 race photos with AI. Handle 3 classes across multiple sessions — process 4,000+ photos per weekend in minutes.

The complete workflow for tagging NASCAR race photos with AI. Process 3,000+ photos per race in minutes, handling pack racing at 200mph.

The complete workflow for tagging WRC and rally race photos with AI. Handle remote locations, mud-covered numbers, one-pass-per-stage challenge, and limited connectivity for end-of-day delivery.

The complete workflow for tagging World Superbike Championship photos with AI. Process 2,000+ photos per weekend in minutes, not hours.

Tag open water and pool swimming photos efficiently. Master curved cap numbers, water spray, and body marking ID with AI-powered detection.

Tag track day photos 10x faster. Handle tape numbers, magnetic plates, no official entry lists, and paying customers expecting same-day results.

Tag 1,000-5,000 remote mountain photos with hidden bibs under rain jackets. Master multi-checkpoint workflow, headlamp glare, and offline processing. Offline-first AI for no-connectivity events.

Complete workflow for tagging triathlon and Ironman race photos. Manage 3 disciplines, different bib positions, and transition chaos. Tag 10,000+ photos per event in hours, not days.

The complete workflow for tagging World Endurance Championship and Le Mans 24h race photos with AI. Handle 5,000-10,000 photos across day-night-dawn lighting, multiple classes, and driver changes.

F1 photographers shoot 3000-5000 photos per race weekend across 5 sessions. Wire service deadlines are measured in minutes, not hours. Per-driver team galleries require metadata precision. Here's the only workflow that works.

Marathons generate 20,000-100,000 photos per event. Revenue is directly tied to match rate. Top-finisher galleries need same-day delivery. Here's how to batch-process and deliver faster than competitors.

Cycling bibs are on the BACK, hidden by saddle bags and water bottles. Peloton density makes rider isolation impossible. AI reads frame numbers too. Here's how.

MotoGP fairing numbers compress at 60° lean angles, riders hide numbers, multiple classes overlap. Here's how AI vision reads numbers where OCR can't see them.

F1 entry lists change between races, wildcard drivers are added late, and team names shift due to sponsorship. Here's how to match AI-detected car numbers to official FIA data automatically.

Triathlon events have 3 separate disciplines with different race numbers. Here's how to match detected numbers to participants across swim, bike, and run with a single CSV workflow.

Wire service F1 photographers must deliver within hours, not days. Manual tagging of 3000-5000 photos takes 8+ hours. Here's how AI delivers driver galleries before competitors.

MotoGP has 3 classes (MotoGP, Moto2, Moto3) with separate delivery deadlines, often within the same day. Manual tagging each class separately takes 8+ hours. Here's how AI delivers all three class galleries before the deadline.

F1 wire service photography requires standardized IPTC captions with driver name, team, circuit, and session. Here's how AI auto-generates editorial metadata for instant delivery.

50,000 marathon participants means 50,000 photos that each need individual IPTC metadata. Here's how AI-powered metadata generation drives photo sales and archive organization.

Cycling pelotons have 50-100 riders packed together, bibs on their backs (not fronts), and overlapping frames. Here's how AI detects and tags every visible bib at scale.

Marathon finish lines have 10-20 runners per frame. Traditional tagging misses everyone except the leader. Here's how AI detects and tags every visible bib.