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Collecting multimodal training data with consent

Model training5 mins read

Introduction

Traditional AI still runs on real-world data: speech, video, sensor streams and documents. The quality of that data sets the ceiling for the model.

Annotation and delivery

Annotators are trained on your guidelines, with a second reviewer on every file. Datasets arrive versioned and documented, loaded straight into your project.

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Model training

Build frontier models and agents that learn from experience.Why the next generation of enterprise AI will be trained on your own work, and what it takes to run that loop safely.General-purpose models are remarkable, but they don't know your contracts, your claims rules or the shorthand your engineers use. The gap between a demo and a dependable system is almost always domain knowledge.Learning from experience closes that gap. Instead of prompting harder, you let a model practise on your tasks, score the results against what good looks like, and keep the improvements.Continue reading

Fine-tuning or prompting? A practical decision guide

Guides

Introduction

Most teams reach for prompting first, and they should. It's fast, cheap to change and easy to review. But prompts hit a ceiling when a task needs a consistent format, specialised vocabulary or behaviour that's hard to describe in words.

5 mins readContinue Reading

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