DELPHIC

The challenge

Giving digital avatars truly expressive, context-aware behaviors remains a major bottleneck, since most systems deliver canned responses or flat emotional ranges. Coordinating animation, speech, facial acting, and branching decision logic in real time is typically custom, brittle, and expensive.

I was COO of the company that built DELPHIC, responsible for operational leadership and the production architecture that made the system possible.

The solution

DELPHIC is a full-stack toolkit combining:

• Context engine managing state, histories, user attributes (no heavy ML training/inference).
• Animation orchestration (full body + facial + "punch moment" effects) tied to dialogue.
• Branching conversation system synchronized with expressive animation.
• Seamless support for either hand-crafted or AI-generated text & lines.

The engine ingests environmental cues, user history, emotional tone, and real-time interaction context. Then, it selects the best animation and dialogue pairing dynamically.

Key capabilities

• Expressive avatar orchestration: full face + body + contextual "punch moments."
• Dynamic branching: responses vary by user profile, tone, conversation history.
• Lightweight context engine: no heavy ML stack required; system logic drives choice.
• Modular integration: works with human authored text or LLM-generated lines.
• Synchronized behavior: avatar response and animation are tightly coupled and context aware.
• Massive diversity from minimal data: Hours of unique motion from ~140 s of source animation

Prototype capabilities

• DELPHIC is the second major revision of the HABTIC system, built on prior experience in procedural avatar animation.
• Its branching context logic enables differentiated responses. For example, a first-time user gets a friendly explanation, but a previously skeptical return user receives a more tailored response.
• Delivers highly personalized avatar interaction without needing full AI training per user or scenario.

Outcomes

• Delivered avatar interactions with character-level expressiveness, without requiring per-user AI training.
• Built a system that works above the dialogue layer, compatible with hand-authored text or future LLM integration.
• Proved that contextual, expressive digital humans can operate at scale without cloud streaming or heavy ML infrastructure.