Coding Agent
13 datasetsFrom full-stack development to complex software engineering.
EXPERT-BUILT / MODEL-READY
Expert data.
Frontier AI.
From expert reasoning to real agent workflows. High-difficulty training and evaluation data for the next generation of intelligent systems.
26Evaluation
datasets
datasets
3Capability
domains
domains
100K+Real-world
business scenarios
business scenarios
A matrix of possibilities.
26 DATASETS / 3 CAPABILITY DOMAINS26 datasets across 3 capability domains
General Agent
5 datasetsReal knowledge work, tool use and reliable task execution.
Reasoning
8 datasetsExpert knowledge and rigorous reasoning across disciplines.
Task scope, dataset availability and acceptance criteria are configured per project. Benchmark names indicate evaluation alignment.
One portfolio. Multiple training paths.
Configure tasks, difficulty, data fields and evaluation criteria around your model.
SFTRLHFAgent trainingEvals
Trusted by teams building frontier AI.Selected clients · Project details subject to confidentiality
AlibabaTiktok SeedMinimax
01 / SERVICES
Your capability goal.
Our data program.
Expertise, production infrastructure and quality controls, brought together around the capabilities you need to build.
Pilot / Scale / Continuous supplyPost-training data
Instruction-response pairs, expert solutions, preference feedback and multi-turn interactions for SFT, RLHF and other post-training approaches.
High-difficulty expert data
Researchers and domain specialists design, produce and review work that requires deep subject knowledge and rigorous problem-solving.
Model & agent evaluation
Evaluation sets, task design and scoring criteria for reasoning, tool use, task execution and stability across extended workflows.
Custom data solutions
Data specifications, difficulty distributions, production processes, quality metrics and delivery formats tailored to your model and product.
Data quality operations
Cleaning, structured parsing, deduplication, format validation, multi-level review and post-delivery processing supported by our own tools.
02 / DELIVERY STRUCTURE
More than answers.
Data you can work with.
From task context to expert review, configure the evidence and metadata your training or evaluation pipeline needs.
CODING AGENT / DELIVERY DESIGN
Executable work. Verifiable results.
Build datasets around repository context, realistic engineering tasks and evidence that the outcome works.
- Software engineering and web development
- Research reproduction and scientific workflows
- Operations, terminal tasks and controlled security tests
GENERAL AGENT / DELIVERY DESIGN
Real tasks. Complete trajectories.
Connect the original request, the working context and the final deliverable in one reviewable record.
- Professional knowledge work and file-based tasks
- Tool use, state transitions and recovery
- Computer interaction and multi-skill workflows
REASONING / DELIVERY DESIGN
Expert depth. Explicit grading.
Turn difficult domain problems into structured questions, rigorous reference solutions and reviewable scoring criteria.
- STEM, mathematics and competitive programming
- Medicine, law, finance and specialist knowledge
- Multimodal problems and frontier scientific reasoning
SFT: instructions and expert responses · RLHF: preferences and scoring · Agents: interactions and tool trajectories · Evals: tasks and rubrics
03 / THE PEOPLE BEHIND THE DATA
Hard problems need
deep expertise.
Founded by PhDs and specialists from leading universities. Our team combines model research, algorithm development, data engineering and large-scale project delivery.
20+Full-time scientists
and technical experts
and technical experts
260+Experts in our advisory
and collaboration network
and collaboration network
EXPERTISE / STRUCTURE / INTELLIGENCE
Selected academic backgrounds of our founding and core team
StanfordUniversityUC BerkeleyUniversity of CaliforniaYaleUniversityPekingUniversityTsinghuaUniversityNUSNational University of Singapore
Specialist teams assembled around the discipline, difficulty and capacity each project requires.
Math & CSScienceMedicineLawFinanceHumanities
04 / QUALITY & DELIVERY
Quality is a process.
We build it in.
Automated checks and expert judgment work together, with clear responsibilities from contributor qualification to final acceptance.
01
Qualification
Assess domain knowledge and task readiness.
02
Calibration
Align contributors on standards and examples.
03
Sampling
Monitor production and correct quality drift.
04
Expert review
Check professional accuracy and consistency.
05
Lead review
Resolve complex issues with subject leads.
06
Acceptance
Validate format and agreed delivery criteria.
07
Trace & improve
Find root causes and update the process.
Infrastructure for scale.
Our own task management and data tools support assignment, production monitoring, automated validation, quality reporting and version control.
DATA OPERATIONS / PROCESS VIEW
AssignValidateReviewDeliver
Task history / Issue labels / Review status / Data versions
Confidentiality, throughout.
Project-specific controls protect client data, model information and working materials across the delivery lifecycle.
- AgreementsEmployment, confidentiality and project-specific terms.
- AccessPermissions matched to project roles and requirements.
- HandlingDevice, file transfer and local storage controls.
- CloseoutArchiving or secure deletion as instructed by the client.
Quality is linked to contributor compensation, incentives and future task eligibility. Sampling, cross-review, blind review and consistency checks are configured for each project.
05 / WORK WITH US
Start with a task.
Build a data advantage.
For foundation models, code models, reasoning systems, domain models and agent products. Choose the engagement that fits your next milestone.
01 / VALIDATE
Pilot program
Align on target capabilities and acceptance criteria. Use a small batch to validate task design, production methods and pipeline compatibility.
02 / SCALE
Project delivery
Draw on experience delivering multiple large-scale data projects, with dedicated leads, parallel production and phased acceptance.
03 / KEEP BUILDING
Ongoing partnership
Maintain a steady supply as your models evolve. Adjust task coverage, difficulty and standards through recurring feedback and direct team access.
A practical starting point for your next data program.
What to prepare for a scoping conversation
- Model or product focus and current capability gaps.
- Training approach: SFT, RLHF, other post-training or evaluation.
- Task types, disciplines, difficulty and language requirements.
- Expected volume, batch schedule and target delivery dates.
- Sample records, required fields and acceptance criteria.
- Data formats, usage rights and confidentiality requirements.
