Real results
at scale.

As frontier models grew stronger, simple coding benchmarks stopped providing meaningful evaluation signal. The need shifted from "can the model produce the right answer?" to "can an agent investigate a realistic environment, plan, execute, debug, and produce a verifiably correct result?"

Zotus AI ran an end-to-end benchmark program — domain research, task design, Harbor-compatible environment construction, oracle reference solutions, automated verifiers, frontier-model trials, and QA. Our team of 30+ engineers and domain specialists created 1,000+ executable Terminal-Bench 2 tasks, with 900+ accepted on delivery.

A frontier robotics lab needed high-fidelity human motion data to train humanoid locomotion policies. Existing video-based pose estimation introduced unacceptable noise.

Lab-grade marker-based motion capture with expert operators across multiple studios, delivering sub-millimeter joint trajectories with full metadata.

An AI lab building tool-using agents needed ground-truth evaluation data — multi-step terminal tasks with verifiable outcomes.

Senior software engineers created, executed, and graded complex agent tasks spanning file systems, APIs, databases, and web interactions.

Pixel-perfect 3D annotations across lidar and camera streams with sub-frame temporal consistency for their perception stack.

Multi-sensor data processing with custom tooling, achieving high consensus accuracy across instance segmentation, lane markings, and 3D cuboids.

High-quality human preference data from experienced software engineers for complex coding tasks.

Senior developers across multiple programming languages, delivering preference rankings and reward signals for SFT and RLHF pipelines.

A foundation model team needed step-by-step verification of chain-of-thought outputs for competition-level math and formal logic problems.

PhD-level mathematicians and logicians annotating reasoning traces — flagging logical errors, verifying proofs, and providing corrected solution paths for reward model training.

A vision-language model needed diverse, high-quality instruction-response pairs grounded in complex images — charts, diagrams, documents, and real-world scenes.

Domain experts producing detailed visual Q&A, spatial reasoning tasks, and multi-turn conversations anchored to image content with pixel-level grounding annotations.

Rigorous red-team evaluation before a major release — adversarial testing across safety categories.

Multi-category red-team evaluations producing structured vulnerability reports and severity-scored datasets for model hardening.

High-quality instruction-following data across dozens of languages with cultural nuance and domain expertise.

Global expert network — native-speaker linguists and domain specialists — producing culturally-adapted, quality-gated datasets.