Foundation Models
Large models, engineered for the enterprise.
We research and operate foundation models spanning language, vision, and multi-modal reasoning — with an emphasis on efficiency, evaluation, and deployability at scale. The work spans pre-training recipes, post-training alignment, and serving kernels that keep latency predictable under load.
Pre-training
Efficient scaling laws across text, image, and code.
Post-training
Alignment, instruction tuning, and preference optimization.
Evaluation
Task-grounded, adversarial, and long-horizon benchmarks.
Serving
Optimized kernels for latency-bound production workloads.
Research to runtime
Foundation models at Zansoc are not research artifacts parked in a paper. Each training run is paired with evaluation suites that mirror customer tasks, safety red-teams, and cost envelopes. Successful candidates graduate into Orchestrator model channels with documented cards and monitoring hooks.
Efficiency as a first-class goal
We invest in data quality, mixture design, and architecture choices that improve tokens-per-watt — not only headline benchmark scores. Distillation and speculative decoding recipes ship alongside the base models so production teams can meet latency SLOs.
Enterprise constraints baked in
Licensing clarity, data residency, and fine-tuning on private corpora are part of the stack design. Customers can extend models without surrendering the ability to audit what changed between versions.