Delta Evals helps AI companies build higher-quality training and evaluation data through two connected services: subject-matter expert labeling panels and industrial video-data pipelines.
Our core service is multi-expert labeling: each item can be reviewed by calibrated domain experts, producing label distributions, rationales, disagreement analysis, QA reports, and export-ready datasets. For teams working with robotics, embodied AI, computer vision, or industrial ML, we also source real-world video footage and build the filtering, segmentation, annotation, metadata, and event-tagging workflows that make that footage usable for model training.
One company, two connected data engines. Most teams start with expert labeling; teams building robotics and computer-vision systems add the video-data pipeline.
For model teams that need reliable human judgment, not generic crowd labels. AI labs, eval teams, post-training teams, and benchmark teams.
For robotics, embodied AI, and computer-vision teams that need real-world training footage — plus the engineering to make it usable.
We route each item to calibrated domain experts and return more than a label: label distributions, reasoning, confidence, disagreement analysis, and QA-ready exports. When experts disagree, we measure that disagreement instead of forcing a fake single answer.
This is the work Delta Evals is built on — data labeling for complex or ambiguous tasks, expert review of model outputs, preference and post-training data, private benchmark creation, red-team and failure-mode labeling, rubric-based evaluation, and human-vs-LLM calibration studies.
We source hands-on POV and workbench footage from real production environments, then build the filtering, segmentation, annotation, metadata, and event-tagging workflows that turn raw video into training-ready datasets.
We provide the footage and build the pipeline that makes it usable at scale — for robotics imitation learning, embodied AI, action segmentation, tool-use and manipulation modeling, human-to-robot task transfer, manufacturing computer vision, and operational workflow analysis.
Every engagement ships with the evidence that the data is trustworthy: calibration, gold items, inter-reviewer agreement, disagreement analysis, and audit-ready QA.
Your data is reviewed by people who understand the domain — engineers and scientists who have shipped real systems, not generic crowd workers. Every annotator passes a subject-matter qualification before touching production data.
Before touching real data, every reviewer is calibrated against pre-labeled gold items. We track calibration drift and gold pass-rates across the engagement.
When experts disagree, we measure and deliver that uncertainty as a distribution — entropy, confidence, and disagreement class — instead of forcing a fake single answer. Why soft labels?
Every batch ships with structured exports, QA reports, rubrics, metadata, and a dataset card — IRA stats, gold pass-rates, and label-error audit notes included.
Our engineers build the filtering, segmentation, annotation, event-tagging, and metadata workflows that make raw data — text or video — usable at scale.
Client data, operational footage, worker imagery, and facility identifiers are handled under NDA-default workflows. Client data is never entered into public LLM tools unless explicitly permitted in the SOW.
Find your team below — each one comes to us for a specific shape of data problem.
Need expert review, scoring, disagreement analysis, and failure taxonomies.
Need preference data, rationales, rubric labels, and expert distributions.
Need real-world human task footage, segmented and annotated for training.
Need curated video datasets with event tags, metadata, and QA.
Need footage and operational data from real factory or workshop environments.
Need private benchmark packs, red-team labeling, and human-vs-LLM calibration as advanced use cases.
We source qualified subject-matter experts on demand. The people designing and reviewing your tasks are working practitioners who would recognize the work as legitimate — not generic crowd labor wearing the language of the domain.
From first-principles derivations to design trade-offs and constraint reasoning across coupled subsystems.
Every task ships with checkable artifacts: equations, unit checks, boundary conditions, and alternate solution paths.
Near-miss numerics, off-by-unit traps, ill-posed prompts, and physically impossible scenarios that punish overconfident models.
Where reasonable experts disagree, we model the disagreement instead of erasing it.
Every engagement ends in structured, documented files — not a messy spreadsheet. These are the artifacts a typical labeling or video engagement ships.
labels.jsonlPer-item label distribution, confidence, and reviewer rationale.
rubric.yamlPer-criterion anchors, severity definitions, and ambiguity policy.
qa_report.pdfInter-reviewer agreement, gold pass-rates, and items flagged for re-review.
dataset_card.mdIntended use, provenance, license, and limitations of the dataset.
events.jsonlTask segments and detected events for video collections.
metadata_schema.jsonTask, sub-task, tool, scene, and environment tags for footage.
A single Delta Evals label is not a checkbox. It is a structured artifact: the decision, the reasoning, the evidence, the assumptions, alternate paths, and an explicit confidence level. Below is one anonymized engineering item — exactly the shape every item in a delivered batch takes.
TASK A thin-walled cylindrical pressure vessel (D = 1.0 m, t = 6 mm) is specified to hold a working fluid at 250°C and 12 bar gauge. Using textbook thin-wall theory, is the design within the reviewer-stated allowable stress? [illustrative anonymized example — not drawn from any proprietary standard. Material allowable stress shown below is a reviewer-stated reference value.] MODEL OUTPUT "Yes, the design is acceptable." DECISION UNSAFE — exceeds reviewer-stated allowable stress SEVERITY Critical (primary structural) RATIONALE • Reviewer-stated allowable at 250°C: σallow ≈ 88 MPa • Hoop stress σ = pD / 2t = (1.2 MPa)(1.0 m) / (2 × 0.006 m) = 100 MPa • σ exceeds σallow by ∼14% — design fails margin EVIDENCE thin-wall hoop-stress relation (textbook); allowable from licensed standard consulted by reviewer ASSUMPTIONS thin-wall approximation (t/D « 0.1); no corrosion allowance ALTERNATE t ≥ 7.0 mm satisfies the stated margin CONFIDENCE 0.94 AMBIGUITY low REVIEWERS 3 of 3 agree DISAGREEMENT none
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 experts — whether the input is text or video.
We map the model behaviors or capture goals you want to improve or measure, identify likely failure modes, and turn them into a task taxonomy.
We co-author annotation guidelines and grading rubrics, with explicit ambiguity policy and severity definitions.
Before touching real data, every reviewer passes a calibration set against pre-labeled gold items. Disagreements drive guideline refinement.
Multi-pass labeling, blind review, gold injection, inter-reviewer agreement tracking, and adjudication for hard cases.
Label-error audits, leakage checks, difficulty tagging, dataset documentation, and version-controlled releases.
Continuous calibration, guideline updates as edge cases surface, and post-delivery analysis to feed the next batch.
A bounded, low-risk way to test Delta Evals on your data before committing to a larger engagement — validate fit, calibrate the rubric, and surface the first batch of failure modes.
Delta Evals handles model outputs, proprietary engineering data, operational footage, and worker imagery. Confidentiality is treated as the most important contract we hold with every client.
Mutual NDAs available before any sensitive information is disclosed. Client materials are retained only for the engagement and securely deleted after.
Client data is never entered into public LLM tools unless explicitly permitted in the SOW. Human experts remain responsible for final labels, rationales, and QA.
Worker consent is handled up front. Faces and facility layout are excluded when not needed for the task.
Facility identifiers and sensitive operational details are protected throughout capture, processing, and delivery.
Tell us the datasets you need to label or the footage you need to capture. We'll come back with a scoped plan in days, not weeks.