Platinum data: raising the bar

How PlatinaData AI produces expert-grade labels that set a new quality ceiling.

PlatinaData AI  |  White Paper

41%→21%Hallucination reductionWith carefully screened RLHF annotators (InstructGPT)
27,540High-quality vendor annotationsOutperformed millions of crowd-sourced examples (Llama 2)
1,000Expert-curated examplesMatched frontier models on 43% of prompts, no RLHF (LIMA)
3–4×Platinum vs. golden dataPlatinum labels outperform 3–4× the volume of conventional golden data in controlled pilots

Data quality now sets the ceiling on model performance

The quality of training and evaluation data now sets the ceiling on model performance. A growing body of state-of-the-art research makes this unambiguous. OpenAI's InstructGPT work showed that carefully screened annotators cut closed-domain hallucination from 41% to 21%. Meta's Llama 2 team replaced millions of crowd-sourced examples with roughly 27,500 high-quality vendor annotations and saw a material quality jump. The LIMA study demonstrated that just 1,000 expertly curated examples could match or beat frontier models on 43% of prompts, with no RLHF. And across autonomous driving, content moderation and adversarial safety, recent studies converge on the same finding: when labels require professional judgment, expert annotation is not a preference but a requirement. Annotation errors cascade directly into unsafe and unreliable model behavior.

This is the gap PlatinaData.AI was built to close. We produce platinum data: labels that meet or exceed the conventional “golden” standard because they carry the weight of genuine professional judgment. Where golden data is typically produced by trained generalists working from rubrics, platinum data is produced by practicing experts answering the harder question: not “does this match the rubric?” but “is this correct in the view of a practicing professional?” This is what moves the needle on frontier reasoning suites, safety red-teaming, specialized coding benchmarks, medical and legal QA, and reasoning-model post-training.

In controlled pilots on content moderation, PlatinaData's platinum labels outperformed 3–4× the volume of conventional golden data, confirming at our own scale what the published research predicts.

We can deliver this quality at scale without the usual quality decay. Our advantage is the full pipeline, not any single step: we recruit experts against a role specification for each program (PhDs, MDs, JDs and senior practitioners across high-resource and long-tail languages); we vet, train and test them on client-specific guidelines and edge cases rather than generic tutorials; and we keep them calibrated through ongoing coaching, blind double-rating and inline managerial review.

This is reinforced by our proprietary annotation platform, which embeds QA, consensus scoring and one-click expert escalation (“Ask a Peer”, “Ask a Manager”) directly into every step of the workflow. The combination is what lets us add capacity without diluting judgment, the failure mode that degrades most managed-crowd operations.

Our programs are led by operators from the world's leading technology companies. Ex-Google and frontier-lab leads run our content moderation and autonomous driving programs, bringing frontier-scale operating experience and a culture of user focus, evidence-based debate and accountability to the teams producing your data.

01

Introduction: why platinum data matters

The AI industry has converged on the language of “golden data” to describe labels considered ground truth. In practice, however, golden data is often produced by generalists trained with heavy rubrics. That is appropriate for many tasks. It is not sufficient when the label itself requires professional judgment: when the question is not “does this answer match the rubric?” but “is this answer correct in the view of a practicing expert?”, or when the question is nuanced and requires professional judgment.

We use the term platinum data to describe labels that carry that weight of professional judgment. Platinum data is what makes a difference on the hardest evaluations: frontier reasoning suites, safety red-teaming, specialized coding benchmarks, medical and legal question answering, and post-training data for reasoning models.

LLM post-training

LIMA: 1,000 curated examples matched frontier models on 43% of prompts with no RLHF. Llama 2: 27,540 expert annotations beat millions of crowd-sourced examples.

Autonomous driving

A 2025 multi-organisation study across six companies found annotation quality problems cascade directly into unsafe model behaviours and degraded reliability.

Content moderation

Annotator demographics create 23-percentage-point accuracy gaps in toxicity classification. Expert annotation is essential for avoiding systematic policy failures.

Adversarial AI safety

DeepMind's STAR framework: expert-matched annotators raised in-group hate-speech detection to 50% vs 41% for out-group raters (p<0.01), catching harms generic crowds miss.

In line with these findings, our own experiments have shown that models tuned on platinum data perform better than the same LLMs tuned on 3–4× as much golden data. Platina's platinum data is labeled at a higher quality caliber, and also optimized for maximum informativeness by ex-Google Data Intelligence Analysts who have designed, built and vetted models that serve billions of users across the world.

02

Hiring: recruiting experts, not just raters

PlatinaData AI does not run a generic crowd and then filter for quality. We recruit against a role specification for each platinum program, and our standing rater pool is largely built from experts with graduate-level credentials and meaningful professional experience.

  1. Role specification
  2. Application screening
  3. Domain assessment
  4. Credential verification
  5. Calibration exam
  6. Ongoing development

Hiring & onboarding pipeline: 6-stage expert selection and continuous development cycle.

Where our experts come from

  • Graduates and alumni of top universities, including PhDs, MDs, JDs and master's degrees in the target domain.
  • Practicing professionals, including clinicians, attorneys, quants, research scientists, senior software engineers and specialized linguists.
  • International hires in every major region, aligned to the buyer's time zone and cultural context.
  • Head and tail language coverage at expert level: high-resource and long-tail languages where finding a domain expert is itself a research project.
From the research In clinical imaging, expert radiologists achieve inter-observer agreement (Randolph's κ) up to 0.99, while mixed-experience pools drop to 0.40 on the same task. The foundational crowd-annotation study found that averaged non-experts can match a single expert on simple objective tasks only, a result consistently reversed when tasks require professional judgment.
03

Training: modules that respect expertise

Expert raters do not want to sit through basic rubric tutorials designed for generalists. Our training program brings experts up to speed on client-specific guidelines, edge cases, tooling and escalation paths, without insulting the expertise they already bring.

  1. Role-specific onboarding
  2. Adjacent task modules
  3. Calibration with worked examples
  4. Ongoing coaching sessions
  5. Guideline drift monitoring
  6. Tooling certification (LLM & non-LLM)

Training architecture: onboarding foundation with 5 continuous learning streams.

  • Role-specific onboarding, not one-size-fits-all. An MD onboarding to a clinical reasoning project sees a different curriculum than a software engineer on a code-eval project.
  • Modules for immediate and adjacent tasks, so raters can move laterally within a program as it evolves.
  • Calibration against worked examples authored by senior experts, with explicit discussion of the hardest edge cases.
  • Ongoing coaching sessions rather than a one-time certification: guideline drift is caught in days, not months.
  • Tooling certification in both LLM-assisted and unassisted modes.
From the research MMLU-Redux found that 9% of questions in one of the most widely used LLM benchmarks contained annotation errors, and that correcting them changes which models rank highest: a direct consequence of insufficient annotator expertise during benchmark construction. Our expert raters are exposed to these challenges so they avoid them at scale.
04

Management: investing in the people behind the labels

The most important lever for platinum data quality is not a rubric or a tool. It is the relationship between a rater and their manager. Expert raters have options; they will stay engaged with work that respects them, and disengage from work that does not.

What we do differently: we invest in our raters

  • Our compensation is competitive, but the larger investment is in development: mentorship pairings, training for adjacent skills, and visible paths into senior and lead roles.
  • We treat raters as colleagues, not an interchangeable crowd. Every rater has a named manager, a regular 1:1 cadence, and a clear channel for feedback.
  • We cultivate belonging, value and pride. Our experts describe Platina as a place they are proud to work for, not a gig platform they occasionally log into.
  • Voluntary attrition is tracked as a first-class quality metric, because every expert who leaves takes calibration with them.
  • Our management layer includes former Google staff carrying forward “Googleyness” norms: user focus, evidence-based debate, humility, and doing the right thing even when no one is watching.
From the research Calibration accumulates over time: reward-model accuracy on RLHF preference data is bounded by the noise floor of the underlying annotations, and that floor is set by annotator consistency. Consistent annotators are retained annotators.
05

Operations: excellence as a daily practice

Platina operates on the principle that every employee (rater, reviewer, manager, project manager) takes personal pride in the work.

  1. Intake & config
  2. Rater assignment
  3. In-task escalation
  4. Managerial review
  5. PM sign-off & delivery

Operations workflow: 5-stage delivery pipeline with escalation paths and iteration loops.

Dedicated project management

Every platinum program has a dedicated project manager accountable for quality, speed and delivery. The PM is the single throat to choke for the client and the single point of clarity for the rater team.

Tooling and human support, together

Our raters work inside a labeling environment pairing automated quality signals with embedded human support. The signature feature is a “Chat with a manager” button inside the task UI: with one click, a rater can pull their manager (L+1) or a senior manager (L+2) directly into the task.

The alternative, leaving raters to guess on hard items, has a documented cost: in content moderation, hate-speech inter-annotator agreement can fall to Cohen's κ = 0.11 without expert escalation; in adversarial safety red-teaming, generic crowd raters miss entire harm categories that expert-matched annotators detect.

Privacy and security architecture

  • Private cloud and on-premise deployment: client data never leaves their network.
  • Full air-gap compatibility for classified or pre-release programs.
  • Entirely LLM-free pipeline option available through “Ask a peer” and “Ask a manager” buttons, with no model calls required.
  • Full audit trail: every action logged with user, timestamp and immutable record.

LLM-Copilot rating as an option

Every Platina rater is certified to work both with and without LLM assistance. The research on synthetic data underscores why this matters: unverified synthetic data leads to model collapse, while human-verified synthetic data prevents it.

06

Quality assurance: platinum labels mean platinum labels

Quality assurance at Platina is not a post-hoc audit function. It is woven into every step from hiring through delivery.

We provide layered QA, with:

  • Pre-delivery calibration exams on every program, repeated at regular intervals rather than only at onboarding.
  • Live coaching sessions where senior reviewers work through contested items so calibration gets stronger, not weaker, over time.
  • L+1 and L+2 managerial review available inline through the “Chat with a manager” channel.
  • Blind double-rating and adjudication on a sampled fraction of every batch, with disagreement rates reported to both the PM and the client. Annotation quality is directly linked to downstream mAP in object detection.
  • Final program-level sign-off by the dedicated PM before any batch ships.
From the research In radiology, label quality differences of 0.015–0.04 F1 points propagate to measurable AUC improvements in downstream image classifiers. In LLM coding, curated high-quality training data enables a 1.3B model to achieve 50.6% pass@1 on HumanEval, matching models 5–25× larger. In adversarial safety, expert-matched red teamers catch harm categories that generic crowd workers miss entirely.
07

The PlatinaAI data platform

The PlatinaAI data platform is our own proprietary annotation platform. Built from scratch, it embeds our evidence-based best practices and SOTA research, with LLM and human co-pilots. Ask a Peer, Ask a Manager and Ask AI Copilot are available at every step of the workflow.

Optional if clients prefer their own annotation platform.

01Project management & infrastructure

Project management

  • Multi-project support across teams with role-based access
  • Project lifecycle tracking: draft, active, paused, completed, archived
  • Project cloning: duplicate configs, workflows and label schemas

Workflow design (DAG-based)

  • Drag-and-drop workflow builder, not just linear annotate-then-review
  • Multi-stage pipelines with branching logic and conditional routing
  • Stage types: staging, auto-label, annotation, review, audit, done
  • Outcome-based transitions: approved, rejected, rework, escalated

Access control

  • Admin: full platform control (model config, quality thresholds, team management)
  • Project Lead: workflow config, task assignment, monitoring
  • Expert Annotator: label/model management, escalation, overrides
  • Reviewer: approve, reject, rework, provide feedback
  • Annotator: pull tasks, annotate, submit
  • Multi-level role assignment, org-wide or project-specific

Config-as-code

  • Export entire workflow configurations as YAML
  • Version-control your annotation pipelines
  • Replicate setups across projects and teams: infrastructure-as-code for data operations
02Annotation capabilities

Multi-modal annotation

  • Images: bounding boxes, polygons, keypoints, semantic masks
  • Video: per-frame annotation with temporal tracking, HLS adaptive streaming
  • Medical imaging: DICOM & NIfTI, 3D volume rendering, slice navigation, RECIST rulers
  • LiDAR / 3D point clouds: cuboids, per-point segmentation, ego-pose awareness

AI Copilot at every step

  • Batch pre-labeling: AI generates initial annotations before any human touches them
  • In-canvas click-to-segment: one click generates the full segmentation mask instantly
  • Temporal propagation: annotate one frame, AI tracks the object across all frames
  • Medical volume segmentation: AI-assisted 3D annotation for DICOM volumes
  • 3D object detection: AI suggests bounding cuboids in LiDAR point clouds

Intelligent sampling & prioritization

  • Priority-based queue management: system selects items by model uncertainty and impact
  • Configurable schemes: random, timestamp-based, priority-score-based
  • Every label improves the model; the improved model picks smarter samples; quality compounds

Export & interoperability

  • COCO, YOLO, KITTI, nuScenes, CSV, or custom schema
  • Plugs directly into existing ML training pipelines; no conversion scripts
03Monitoring, QA & rater support

Quality monitoring & consensus

  • Parallel multi-annotator consensus: N annotators label the same sample independently
  • Automatic IoU-based agreement detection: geometric agreement measured to the pixel
  • Where they agree, the label is confirmed; where they disagree, an expert is called in
  • Expert override workflow with full audit trail
  • Auto-relabeling for flagged samples

Data visualization & dashboards

  • Per-project: tasks by stage, completion velocity, turnaround time
  • Per-annotator: submission rate, acceptance rate, rework ratio, quality score
  • Annotator agreement matrices
  • Time-series progress tracking and bottleneck identification
  • Organization-level analytics across all projects

Annotation versioning & audit trail

  • Full version history: create, update, delete, restore on every annotation
  • Immutable audit logging: every action logged with user, timestamp, old and new values
  • Exportable for regulatory compliance
  • Rework cycles with reviewer feedback attached to each rejection

Rater support tools

  • Ask a Peer: consult another expert inline without leaving the task
  • Ask a Manager (L+1/L+2): one-click escalation with full task context
  • Ask AI Copilot: optional LLM-assisted suggestions, enabled per client policy
  • Real-time auto and human feedback loops baked into every workflow stage
08

How we compare

The table below summarizes what makes Platina's approach unique, and our promise to our clients.

DimensionOur promise
Expert profilePhDs, MDs, JDs and senior professionals hired against a role specification.
Beyond labelingWe provide bespoke data services, including adversarial testing, vulnerability research, data collection optimized for maximum informativeness (tuned to tasks like few-shot learning), and more.
Language coverageExperts in high-resource and long-tail languages, including regional dialects.
Rater retentionActive mentorship, career tracks, belonging; low voluntary churn.
Manager escalationBuilt into the rater UI with L+1 and L+2 escalation on any task.
LLM workflowRaters certified with or without the client's or Platina's LLM.
Dedicated PMOne accountable PM per project, owning quality, speed and delivery.
Calibration cadenceRegular exams, live coaching, and rater-manager 1:1s.
Ex-Google leadershipManagement layer with Googleyness operating norms.
Quality ceilingExceeds golden benchmarks; SME-level judgment.

Ready to discuss a platinum data engagement?

To request a calibration pilot on a defined expert domain, please contact your PlatinaData AI account lead.

Request a pilotGet in touch

Grounded in published research

The claims throughout this white paper are grounded in peer-reviewed research and published studies from leading laboratories, universities and standards bodies.

A. Data quality as the ceiling on model performance (LLM post-training)

  1. 1
    Ouyang et al. (2022). Training Language Models to Follow Instructions with Human Feedback (InstructGPT). NeurIPS 35. arXiv:2203.02155. Cited for the finding that carefully screened RLHF annotators reduced closed-domain hallucination from 41% to 21%.
    https://arxiv.org/abs/2203.02155
  2. 2
    Touvron et al. (Meta AI, 2023). Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv:2307.09288. Cited for replacing millions of crowd-sourced SFT examples with 27,540 high-quality vendor annotations.
    https://arxiv.org/abs/2307.09288
  3. 3
    Zhou et al. (2023). LIMA: Less Is More for Alignment. NeurIPS 36. arXiv:2305.11206. Cited for showing 1,000 carefully curated expert-level examples (no RLHF) matched or beat GPT-4 on 43% of prompts.
    https://arxiv.org/abs/2305.11206
  4. 8
    Gunasekar et al. (Microsoft Research, 2023). Textbooks Are All You Need (phi-1). arXiv:2306.11644. Cited for a 1.3B-parameter model trained on curated data achieving 50.6% pass@1 on HumanEval, matching models 5–25× larger.
    https://arxiv.org/abs/2306.11644
  5. 24
    Shumailov et al. (2024). AI Models Collapse When Trained on Recursively Generated Data. Nature, 631(8022), 755–759. Cited for the finding that unverified synthetic data leads to model collapse, while human-verified data prevents it.
    https://doi.org/10.1038/s41586-024-07566-y
  6. 7
    Coste et al. (2024). Reward Model Ensembles Help Mitigate Overoptimization. ICLR. arXiv:2310.02743. Cited for the principle that reward-model accuracy on RLHF preference data is bounded by the noise floor of the underlying annotations.
    https://arxiv.org/abs/2310.02743

B. Benchmark and evaluation data quality

  1. 25
    Gema et al. (2024). Are We Done with MMLU? (MMLU-Redux). arXiv:2406.04127. Cited for finding that 9% of questions in a widely used LLM benchmark contained annotation errors, and that correcting them changes model rankings.
    https://arxiv.org/abs/2406.04127

C. Clinical and medical imaging annotation

  1. 12
    Jain et al. (2021). Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation. arXiv:2104.00793. Cited for evidence that label-quality differences propagate to measurable AUC differences in downstream image classifiers.
    https://arxiv.org/abs/2104.00793
  2. 13
    Hansen et al. (2022). Inter- and Intra-Observer Agreement When Using a Diagnostic Labeling Scheme for Annotating Findings on Chest X-rays. Diagnostics, 13(1), 90. Cited for expert radiologists reaching inter-observer agreement (Randolph's κ) up to 0.99.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776917/

D. Object detection and perception annotation quality

  1. 17
    Agnew et al. (2024). Quantifying the Effects of Ground Truth Annotation Quality on Object Detection and Instance Segmentation Performance. IEEE Access, 11. Cited for the direct link between annotation quality and downstream mAP in object detection.
    https://doi.org/10.1109/ACCESS.2023.3290618

E. Crowd vs. expert annotation

  1. 20
    Snow et al. (2008). Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks. EMNLP 2008. Cited as the foundational study showing averaged non-experts can match a single expert on simple, objective tasks only.
    https://aclanthology.org/D08-1027/

F. Content moderation, toxicity, and annotator subjectivity

  1. 21
    Goyal et al. (2022). Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation. ACM CSCW. arXiv:2205.00501. Cited for annotator-identity effects creating large accuracy gaps in toxicity classification across rater groups.
    https://arxiv.org/abs/2205.00501
  2. 22
    Sap et al. (2022). Annotators with Attitudes: How Annotator Beliefs and Identities Bias Toxic Language Detection. NAACL 2022. Cited for hate-speech inter-annotator agreement falling to Cohen's κ near 0.11 on contested items without expert escalation.
    https://aclanthology.org/2022.naacl-main.431/
  3. 28
    Barbarestani et al. (2024). A Study of Annotation Methods for Inappropriate Language. TRAC @ LREC-COLING 2024. Cited for the finding that defining inappropriate language is highly subjective, making expert and calibrated annotators essential.
    https://aclanthology.org/2024.trac-1.11/

G. Adversarial AI safety and red-teaming

  1. 29
    Weidinger et al. (Google DeepMind, 2024). STAR: A SocioTechnical Approach to Red Teaming Language Models. EMNLP 2024. arXiv:2406.11757. Cited for demographically-matched annotators raising in-group hate-speech detection to 50% vs 41% for out-group raters (p<0.01).
    https://aclanthology.org/2024.emnlp-main.1200/

H. Autonomous driving perception annotation

  1. 27
    Saeeda et al. (2025). Data Annotation Quality Problems in AI-Enabled Perception System Development. arXiv:2511.16410. Multi-organisation case study across six companies. Cited for the finding that annotation quality problems cascade directly into unsafe model behaviours.
    https://arxiv.org/abs/2511.16410
  2. 30
    Liu et al. (2024). A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook. IEEE Transactions on Intelligent Vehicles, 9(11). arXiv:2401.01454. Survey of 265 autonomous driving datasets establishing that expert-driven annotation pipelines are essential.
    https://arxiv.org/abs/2401.01454