Data Capture

Useful 3D avatar interactions depend on useful context. We capture and structure your source material, then train bespoke LLMs that support the personality, knowledge, and behaviour your avatar needs.

This can include role briefs, policies, product knowledge, training material, audience guidance, and organisation-specific language.

The data capture process is shaped around live avatar use: what the character should know, how it should respond, what it should avoid, and how teams will review and improve the experience over time.

We focus on the data that actually affects the conversation. That usually means clear role context, trusted facts, boundaries, examples of good and bad answers, and the language your audience already uses.

We design Pxl-Persona projects so core processing runs in house, on premise, on our own machines, rather than relying on cloud AI providers for the standard service.

This means avatar, voice, data, AI processing, and deployment work can be built around sovereign Pxl servers, giving organisations clearer control over where their material is processed and how the workflow is operated.

Most organisations already have useful material, but it is rarely ready for an avatar session as-is. We help sort, label, summarise, and structure that material so it can support practical scenarios.

This can involve pulling important points out of long documents, separating public information from sensitive internal notes, and turning policy language into prompts and scenario rules.

When the source material is organised well, avatars can ask more relevant questions, stay closer to the intended role, and give users a more realistic practice experience.

It also makes future updates easier. As policies, products, courses, or roles change, the source material can be refreshed without redesigning the whole experience.