Everything you need to build, evaluate, and improve production AI.
From expert sourcing to dataset quality and model evaluation — the human intelligence layer behind reliable AI systems.
Overview
Three services. One operating system.
Each service can run independently or as part of one continuous delivery pipeline.
Dataset Quality Assurance
Multi-pass review that catches errors, bias, and policy issues before data ships.
Model Evaluation
Expert-led benchmarks that measure reasoning, tool use, and failure modes.
01 — DATA QUALITY
Dataset Quality Assurance & Moderation
A structured review layer applied to existing or newly collected datasets to identify quality issues, policy violations, labeling errors, and unsafe or non-compliant content before the data is used for training or fine-tuning.
- Multi-pass human review with inter-annotator agreement tracking
- Content moderation against your safety and policy guidelines
- Error and inconsistency detection in existing label sets
- Bias and representativeness audits
- Custom QA rubrics built around your model's use case
Typical outputs: cleaned datasets, QA reports with error taxonomies, moderation logs, and recommendations for pipeline improvements.
02 — EVALUATION
AI Model Evaluation & Reasoning Frameworks
Design and execution of evaluation sets that measure how well a model reasons, plans, and uses tools — built and graded by subject-matter experts rather than generic raters.
- Custom benchmark design aligned to your model's capabilities
- Expert grading against rubrics, gold answers, or rationale-based scoring
- Reasoning-trace generation and verification for chain-of-thought training
- Support for HLE-style, GDPVal-style, SciCode, SWE-bench, and Terminal-Bench formats
- Longitudinal evaluation for tracking model improvement across versions
Typical outputs: scored evaluation datasets, calibrated rubrics, grader agreement statistics, and detailed failure-mode analysis.
03 — PEOPLE
Expert Sourcing for AI Training
Recruitment, vetting, and management of subject-matter experts who contribute data, annotations, and judgments across STEM, the natural sciences, and the humanities.
- Sourcing across mathematics, physics, biology, chemistry, computer science, law, and more
- Credential and competency verification
- Task-specific onboarding and calibration rounds
- Ongoing project management and quality monitoring
- Flexible engagement models
Typical outputs: a staffed, quality-controlled expert workforce mapped to your project's exact domain and skill requirements.
Connected delivery
Use one service — or the full pipeline.
Auptonix can manage individual stages or the entire workflow end to end.
Not sure which service fits your project?
Tell us what you are building and we'll help define the right mix of expert sourcing, data QA, and evaluation.
Ready to start?
Build a reliable human-data and evaluation pipeline for your AI system.
Tell us what you are training, evaluating, or improving.