Verified trajectories for frontier agents
DeepLLMData builds verified training and evaluation data for frontier agents: software-engineering trajectories, enterprise computer-use workflows, and expert-designed model-stumping evaluations for AI labs and enterprises.
Capabilities
Hard environments, consequential decisions, and outcomes that can be checked — built with the experts who understand the work.
End-to-end traces across real codebases: planning, tool calls, multi-file edits, terminal execution, and tests. Built for the class of long-horizon work measured by SWE-bench and Terminal-Bench.
Domain experts design adversarial tasks, evaluation rubrics, and failure analyses for consequential workflows in finance, healthcare, research, and operations.
Long-horizon trajectories for browser and desktop agents: navigating business systems, resolving exceptions, and completing work under policy constraints.
Professionally curated visual data with scene-level semantics, spatial and temporal reasoning, physical consistency checks, and artifact evaluation.
SFT examples, preference pairs, critiques, and rubric traces for coding, STEM, multilingual, and professional reasoning.
Finance, healthcare, law, technical, and multilingual datasets spanning reasoning, retrieval, evaluation, and multimodal tasks.
Approach
The sequence is deliberate. Each gate turns an ambiguous capability gap into stronger, more measurable training signal.
We translate your model goals into concrete task specs, difficulty bands, and acceptance rubrics — so every example has a job.
Domain specialists — engineers, researchers, clinicians, analysts — are screened and assigned to the work that needs their judgment.
Gold sets, inter-rater checks, executable tests, and iterative feedback keep the signal grounded as the program scales.
Clean schemas, documented fields, and export formats your training pipeline can ingest without another cleanup pass.
Datasets
Start from ready inventory when you need speed, or use these as blueprints for a custom collection.
Multi-file engineering tasks with agent trajectories, reference patches, and executable tests.
Request the pack briefLong-horizon browser and desktop workflows with policy checks and outcome verification.
Adversarial cases and expert rubrics for analytical, planning, and decision-making failures.
Scene-level semantics, temporal reasoning, physical consistency, and visual artifact judgments.
Expert comparisons across clinical scenarios for domain-sensitive preference optimization.
Instruction following and preference data across languages for cross-lingual post-training.
Need a custom slice, language, or difficulty band? Tell us what your model still cannot do — we will scope the pack.
Inquire about a datasetFor experts
DeepLLMData works with specialists who can write, critique, and evaluate at a professional standard — remotely, on projects that match their domain.
Next step
Tell us the capability gap, the domain, and the timeline. We will come back with a concrete data plan — not a generic pitch deck.