AIHumanity
Use Case

Generic Emotion Models Miss Your Users

Off-the-shelf emotion recognition is trained on generic data — it misjudges clinical patients, game characters, industry-specific speech patterns, and specialized sensor hardware. A model fine-tuned on your data reads your users correctly, without changing your integration.

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Same API
no integration changes
Domain-tuned
accuracy on your data

Same contract, better accuracy

Built for teams where generic emotion models fall short.

Drop-in upgrade

Same API. Better numbers.

The fine-tuned model keeps the same label space and API as the base model — your integration code doesn't change, only the accuracy on your domain does.

  • ›No SDK changes required
  • ›Same calibration contract
  • ›Side-by-side A/B harness to prove the lift
Your data, your terms

Trained on your consented data, not ours

Model tuning runs on data you provide and control, with a scoping call up front to define what's in and out of bounds.

  • ›Consented data only
  • ›Sample model card before commitment
  • ›Scoping call to define data boundaries

Where this applies

🏥

Clinical research

Domain-tuned models on consented patient data.

🎮

Game studios

Tune to your characters, your players, your genre.

🏢

Enterprise CX

Industry-specific emotional cues at scale.

🤖

Robotics OEMs

Optimized for your sensor hardware and form factor.

Let's tune a model for your problem

Send us a note. We'll schedule a scoping call and share a sample model card.

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Talk to David