Casting platform uses AI to widen the talent pool

The problem

Allcasting applied AI to prioritize talent fit over stereotypes, helping improve representation while speeding placements.

The AI solution

Allcasting sought to reduce bias and manual screening in casting workflows. For SMBs in media and events, slow, subjective selection processes delay production and limit reach.

The team used AI to parse profiles and audition media, ranking candidates on skill and fit signals. Editors reviewed shortlists and retained control over final decisions, combining speed with oversight.

Reported gains included faster placements and improved representation. See the coverage for examples and approach.

For SMBs: start with assisted ranking, not full automation. Track time‑to‑shortlist, diversity of finalists, and satisfaction of hiring managers.

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Results & metrics

• 10–15 hours saved per week
• 20–30% faster cycle times
• Lower error rates and fewer escalations

Lessons for SMBs

• Start small: pilot one workflow and measure it rigorously
• Blend heuristics with ML for quicker wins and safer rollout
• Train staff and bake new steps into SOPs to lock in gains

Learn more

Read the full case study: Raconteur: Allcasting diversity

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