Rationale-augmented labeling
Every label is paired with a concise human rationale: supporting evidence, assumptions, ambiguity notes, and a confidence level. Critical for subjective and expert-level tasks.
Research-backed data labeling, benchmarks & AI evaluation.
Delta Evals builds the kind of training and evaluation data that frontier models actually need: rationale-rich labels, expert STEM reasoning tasks with verification artifacts, rubric-driven grading, and disagreement-aware annotation — all under tight reviewer calibration.
Expert annotators across STEM, code, and reasoning
Calibrated reviewers, gold tasks & adjudication
Human–LLM collaborative workflows
13
Capability areas
8+
Engineering domains
100%
Reviewer calibration
∞
Rationale per label
Capabilities
Thirteen research-grade capabilities, one accountable team.
Most labeling vendors stop at a label. We deliver structured reasoning, supporting evidence, ambiguity notes, confidence levels, and verification artifacts — the things that actually move evals.
Expert-created STEM reasoning tasks
Multi-step engineering and STEM prompts authored by domain experts — designed to test deep reasoning, not surface recall.
Solution traces & verification
Step-by-step derivations, equations, unit checks, boundary conditions, alternate paths, and verification logic accompany every expert-authored task.
Rubric-driven evaluation
Detailed rubrics decompose quality into measurable criteria — correctness, reasoning consistency, math validity, feasibility, clarity, completeness, safety, and severity.
Failure-mode & blind-spot analysis
Adversarial cases, near-misses, counterfactual variants, and stress tests that surface hallucination, overconfidence, numerical errors, and broken physical constraints.
Disagreement-aware labeling
We preserve annotator disagreement, document interpretations, and produce soft-label distributions instead of forcing a single majority vote on inherently subjective tasks.
Human–LLM collaborative annotation
LLMs draft labels, rationales, or candidate solutions; experts verify, correct, reject, or improve. Faster iteration, expert oversight at the center.
Active-learning-driven labeling
We prioritize the most informative, uncertain, diverse, or high-impact samples — so every expert hour produces maximum eval lift.
Preference ranking & comparative eval
Pairwise preferences, best-of-N selection, rubric-based scoring, and explanation-based review with notes on why one response is more correct, safer, clearer, or more useful.
Multimodal annotation with grounding
Text, image, audio, video. Evidence spans, bounding boxes, timestamps, object/event descriptions, and cross-modal consistency checks.
Reviewer calibration & QA
Qualification tasks, calibration rounds, gold-standard items, blind review, inter-reviewer agreement tracking, adjudication, label-error audits, and feedback loops.
Dataset & benchmark quality systems
Guideline development, rubric refinement, dataset documentation, leakage checks, difficulty tagging, failure taxonomies, and version-controlled quality reviews.
Neuroscience annotation
Specialized expert-reviewed labeling for EEG, MRI, brain-tumor pathology, and multimodal research datasets — seizure and sleep events, neuroimaging ROIs and lesions, cell and tissue segmentation, and structured research metadata for AI and analysis pipelines.
Domain coverage
Engineering & STEM, end-to-end.
Our task authors and reviewers come from real engineering practice — academia, R&D labs, and industry. We design prompts and rubrics that domain specialists would recognize as legitimate.
Mechanical
Electrical
Materials
Industrial
Automotive
Robotics
AI / ML
Applied engineering
Mathematics
Physics
Multi-step problem authoring
From first-principles derivations to design trade-offs and constraint reasoning across coupled subsystems.
Verification-first design
Every task ships with checkable artifacts: equations, unit checks, boundary conditions, and alternate solution paths.
Adversarial & edge cases
Near-miss numerics, off-by-unit traps, ill-posed prompts, and physically impossible scenarios that punish overconfident models.
Soft labels for ambiguity
Where reasonable experts disagree, we model the disagreement instead of erasing it.
Process
A workflow built for evals, not throughput-only.
We treat each engagement like a small research project: define the failure modes you care about, design tasks & rubrics around them, and iterate with calibrated humans.
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1
Scoping & failure-mode discovery
We map the model behaviors you want to improve or measure, identify likely failure modes, and turn them into a task taxonomy.
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2
Guidelines & rubrics
We co-author annotation guidelines and grading rubrics, with explicit ambiguity policy and severity definitions.
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3
Train and align the team
Before anyone touches your real data, every annotator and reviewer works through a small set of pre-labeled practice items. We use the results to confirm they apply the guidelines consistently — and to surface places where the guidelines themselves need to be sharpened.
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4
Production labeling & review
Multi-pass labeling, blind review, gold injection, inter-reviewer agreement tracking, and adjudication for hard cases.
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5
Quality audits & delivery
Label-error audits, leakage checks, difficulty tagging, dataset documentation, and version-controlled releases.
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6
Feedback loops
Continuous calibration, guideline updates as edge cases surface, and post-eval analysis to feed the next batch.
Why Delta Evals
Reasoning is the product. Labels are the byproduct.
Most teams want labels. The teams pushing the frontier want the reasoning behind those labels — that is what trains better models and produces honest evals.
Research-backed methodology
Soft labels, disagreement modeling, rubric grading, active learning — drawn from peer-reviewed practice, not vendor folklore.
Real domain experts
Engineers and scientists who have shipped real systems author and review your tasks — not generic crowd workers.
Verification by default
Solutions arrive with derivations, unit checks, boundary conditions, and alternate paths — already auditable.
Quality you can audit
Every batch is shipped with IRA stats, gold pass-rates, calibration drift, and label-error audit notes.
Globally distributed team
A deep talent pool across STEM, code, and reasoning — overlap-friendly time zones and competitive engagement terms.
Custom workflows, not templates
Each engagement gets bespoke guidelines, rubrics, and tooling tailored to the failure modes you care about.
Have an evaluation problem worth solving?
Tell us the model behaviors you want to measure or improve. We'll come back with a labeling and benchmark plan in days, not weeks.