High-quality training data to push the frontier of AI models
We assembled an expert recruiter network that fills hard-to-hire technical and operational roles at top startups. Through it, we’ve built a vetted pool of engineers, ML experts, and specialists we can mobilize quickly for domain-specific tasks.
This workforce allows us to form specialized teams and deliver exactly the experts needed for any dataset — producing high-quality human data with unprecedented speed.
Build Better AI Models
Computer Use Trajectory Models
Computer Use Trajectory Models
We capture full step-by-step recordings of how people actually use software—every click, keystroke, and screen interaction. These trajectories provide ground-truth demonstrations that teach AI agents to navigate and operate digital tools the way humans do.
Each dataset spans common enterprise and engineering workflows, from Salesforce and HubSpot to IDEs and terminal sessions. By linking actions to natural-language instructions, they enable models to generalize across apps, handle edge cases, and perform tasks end-to-end without brittle scripting.
We capture full step-by-step recordings of how people actually use software—every click, keystroke, and screen interaction. These trajectories provide ground-truth demonstrations that teach AI agents to navigate and operate digital tools the way humans do.
Each dataset spans common enterprise and engineering workflows, from Salesforce and HubSpot to IDEs and terminal sessions. By linking actions to natural-language instructions, they enable models to generalize across apps, handle edge cases, and perform tasks end-to-end without brittle scripting.
Egocentric Robotics Data
Egocentric Robotics Data
We create datasets designed for robotics and embodied AI, from household task automation to industrial applications. A core resource is our large-scale egocentric (head-mounted) GoPro dataset spanning 300+ real-world tasks—laundry folding, gardening, gemstone sorting, and more.
Each clip is paired with prompts and annotations linking demonstrations to natural-language instructions, making skills reusable and transferable. Our datasets capture failure-prone edge cases and bridge gaps left by shallow or noisy public resources, ensuring models learn what matters for real-world deployment.
We create datasets designed for robotics and embodied AI, from household task automation to industrial applications. A core resource is our large-scale egocentric (head-mounted) GoPro dataset spanning 300+ real-world tasks—laundry folding, gardening, gemstone sorting, and more.
Each clip is paired with prompts and annotations linking demonstrations to natural-language instructions, making skills reusable and transferable. Our datasets capture failure-prone edge cases and bridge gaps left by shallow or noisy public resources, ensuring models learn what matters for real-world deployment.
Supervised Fine-Tuning (SFT)
Supervised Fine-Tuning (SFT)
High-quality prompt–response pairs with full chain-of-thought reasoning examples form the backbone of these datasets. They span diverse domains, including STEM, legal, multilingual, and medical tasks.
By showing step-by-step reasoning, they teach models the correct paths rather than just the final answers. Each example is validated by expert contractors to ensure accuracy and minimize noise.
High-quality prompt–response pairs with full chain-of-thought reasoning examples form the backbone of these datasets. They span diverse domains, including STEM, legal, multilingual, and medical tasks.
By showing step-by-step reasoning, they teach models the correct paths rather than just the final answers. Each example is validated by expert contractors to ensure accuracy and minimize noise.
Evaluation Benchmarks
Evaluation Benchmarks
Datasets that measure valuable benchmarks provide stress tests for ambiguity, compositional reasoning, and domain transfer. They are designed to expose how models perform under challenging conditions rather than just on standard test sets.
They also capture rare failures such as hallucinations, bias, and factual drift that often surface post-deployment. Each benchmark is built with reproducible protocols, enabling longitudinal tracking of genuine improvements versus overfitting.
Datasets that measure valuable benchmarks provide stress tests for ambiguity, compositional reasoning, and domain transfer. They are designed to expose how models perform under challenging conditions rather than just on standard test sets.
They also capture rare failures such as hallucinations, bias, and factual drift that often surface post-deployment. Each benchmark is built with reproducible protocols, enabling longitudinal tracking of genuine improvements versus overfitting.
