Real-time conversational avatar runtime
End-to-end model stack for a full-duplex 3D avatar with identity persistence and bounded-latency inference.
SVC // 07
AI Model Development
From data curation through architecture, training infrastructure, evaluation, and deployment. We build purpose-built models — including vision-language and complete multi-modal systems — when the right answer is not "call the biggest API."
▌▌▌ WHAT WE DELIVER ▐▐▐
DELIVERABLES
Whole-stack model engineering.
- // DATA PIPELINES Ingest, label, dedupe, and version every training corpus with provenance and policy filters.
- // ARCHITECTURE Pick the right family for the budget — encoder, decoder, retrieval-augmented, vision-language, audio-vision-language, or fully multi-modal — and justify the choice on paper.
- // VL & MULTI-MODAL TRAINING Image, video, audio, and text streams trained jointly. Custom tokenizers, modality adapters, cross-attention design, and curriculum schedules tuned to the workload.
- // TRAINING INFRA Repeatable jobs, checkpointing, observability, and reproducible artifacts.
- // EVAL SUITE Domain-specific benchmarks, regression tracking, and human-rated samples per release.
- // MODEL CARD Written disclosure of capability, limits, training data, and acceptable use.
- // DEPLOYMENT Inference service with quantization, batching, autoscaling, and a quiet rollback story.
▌▌▌ REPRESENTATIVE ENGAGEMENTS ▐▐▐
DOSSIER
Selected work — redacted.
▌▌▌ HOW WE WORK ▐▐▐
PROCESS
Curate. Train. Evaluate.
- // 01 CURATE Data quality, license posture, and policy filters resolved before the first epoch.
- // 02 TRAIN Repeatable training jobs against a written evaluation budget — not the largest model we can afford.
- // 03 EVALUATE Benchmarks, regression tracking, human review, and an honest model card per release.