--- title: "Human Data & Physical AI Training Data — LATAM" description: "LATAM teams for RLHF, SFT and physical AI training data: teleoperation, egocentric video & robotics annotation. κ ≥ 0.80 QA, US. Book a call in 48h." lang: en json-ld: | [ { "@context": "https://schema.org", "@type": "Organization", "name": "HiresLink", "url": "https://www.hireslink.com", "logo": "https://www.hireslink.com/lovable-uploads/1d2b89d4-b968-4685-94d8-155c92aad496.png", "description": "AI-powered nearshore staffing platform connecting US companies with elite LATAM tech talent. 280+ clients, 48-hour shortlists, 50-60% cost savings.", "sameAs": [ "https://www.linkedin.com/company/hireslink", "https://twitter.com/hireslink" ] }, { "@context": "https://schema.org", "@type": "WebSite", "name": "HiresLink", "url": "https://www.hireslink.com", "potentialAction": { "@type": "SearchAction", "target": "https://www.hireslink.com/blog?q={search_term_string}", "query-input": "required name=search_term_string" } }, { "@context": "https://schema.org", "@graph": [ { "@type": "Organization", "@id": "https://www.hireslink.com/#organization", "name": "HiresLink", "url": "https://www.hireslink.com", "logo": { "@type": "ImageObject", "url": "https://www.hireslink.com/lovable-uploads/1d2b89d4-b968-4685-94d8-155c92aad496.png" }, "description": "Elite LatAm staffing for AI companies, startups, and enterprises. Build high-performance teams with vetted talent in 72 hours.", "contactPoint": { "@type": "ContactPoint", "contactType": "Sales", "availableLanguage": [ "English", "Spanish", "Portuguese" ] } }, { "@type": "Service", "@id": "https://www.hireslink.com/human-data-llm-training/#service", "serviceType": "Human-Generated Data Staffing for LLM Training", "name": "LLM Training Data Annotation Teams", "description": "Dedicated RLHF, SFT, code annotation, and multimodal labeling teams for enterprise AI. Measurable QA guarantees with Cohen's Kappa ≥ 0.80, rapid ramp-up in ≤72 hours, and zero data leakage controls.", "provider": { "@id": "https://www.hireslink.com/#organization" }, "areaServed": { "@type": "Place", "name": "Global" }, "hasOfferCatalog": { "@type": "OfferCatalog", "name": "LLM Training Data Services", "itemListElement": [ { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "RLHF Annotation Teams", "description": "Reinforcement Learning from Human Feedback teams for preference modeling, ranking, and critique loops" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "SFT Data Generation", "description": "Supervised Fine-Tuning teams for instruction-response pairs and taxonomy tagging" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "Code Annotation", "description": "SWE-bench evaluations, UI intents, and function calling annotation" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "Multimodal Annotation", "description": "Audio, vision, speech annotation and safety red-teaming" } } ] } }, { "@type": "BreadcrumbList", "@id": "https://www.hireslink.com/human-data-llm-training/#breadcrumb", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://www.hireslink.com" }, { "@type": "ListItem", "position": 2, "name": "Industries", "item": "https://www.hireslink.com/industries" }, { "@type": "ListItem", "position": 3, "name": "LLM Training Data", "item": "https://www.hireslink.com/human-data-llm-training" } ] }, { "@type": "FAQPage", "@id": "https://www.hireslink.com/human-data-llm-training/#faq", "mainEntity": [ { "@type": "Question", "name": "What is human-generated data for LLMs?", "acceptedAnswer": { "@type": "Answer", "text": "Human-generated data refers to labels, annotations, preferences, and feedback created by real people (not synthetic models) to train, fine-tune, and evaluate large language models. This includes RLHF (Reinforcement Learning from Human Feedback), SFT (Supervised Fine-Tuning), code labeling, multimodal annotation, and more." } }, { "@type": "Question", "name": "How does RLHF staffing work?", "acceptedAnswer": { "@type": "Answer", "text": "We recruit, vet, and onboard dedicated teams of annotators fluent in English (and Spanish/Portuguese as needed). We define your preference rubrics, build golden test sets, train the team, and scale from pilot (10 annotators) to production (400+) while maintaining quality (Cohen's Kappa ≥ 0.80) and security (zero leakage on golden sets)." } }, { "@type": "Question", "name": "What QA guarantees do you offer?", "acceptedAnswer": { "@type": "Answer", "text": "We commit to measurable inter-annotator agreement (Cohen's Kappa ≥ 0.80 for most tasks), dual review on critical labels, spot audits, leakage controls on golden sets, and contract-level clawbacks if we fall short. You get transparency with weekly quality reports." } }, { "@type": "Question", "name": "Do you support code and multimodal annotation?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. We staff code annotation (SWE-bench style evals, UI intents, toolformer traces) and multimodal tasks (audio transcription/labeling, image/video annotation, speech/facial analysis, safety red-teaming). Teams include domain experts (e.g., software engineers for code tasks)." } }, { "@type": "Question", "name": "How do you handle security and compliance?", "acceptedAnswer": { "@type": "Answer", "text": "All annotators undergo security vetting (background checks, NDA signing). We implement SOC-style controls: no local data persistence, VPN-only access, audit logs, role-based permissions. Data stays in your environment or approved cloud buckets. We're GDPR-aware and can align to your compliance requirements." } }, { "@type": "Question", "name": "How much does human data for LLM training cost?", "acceptedAnswer": { "@type": "Answer", "text": "General annotation and data collection start around $18-$22/hr, RLHF preference work and SFT authoring run $23-$30/hr, evaluation and red teaming $25-$34/hr, RL environment engineering $32-$45/hr, and credentialed domain experts $35-$70/hr. A 10-person pilot pod typically lands between $28K and $40K per month fully managed, 50-70% below equivalent US-based staffing." } }, { "@type": "Question", "name": "How fast can you stand up a pilot annotation pod?", "acceptedAnswer": { "@type": "Answer", "text": "48 hours to a shortlist, and a 10-person pilot pod typically running within 7-10 business days. Week one covers rubric alignment and golden-set construction, week two is calibration until inter-annotator agreement clears the threshold, and production throughput starts in week three." } }, { "@type": "Question", "name": "Can you provide native Spanish and Portuguese data at scale?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Native LATAM speakers cover Rioplatense, Mexican, Colombian, Chilean and Caribbean Spanish plus Brazilian Portuguese, with locale-aware idiom, register and cultural adequacy review rather than translated English prompts." } }, { "@type": "Question", "name": "What is the difference between AI red teaming and model evaluation?", "acceptedAnswer": { "@type": "Answer", "text": "Model evaluation measures whether the system does what it is supposed to do: accuracy, helpfulness, format compliance and regression against a golden dataset. Red teaming measures what an adversary can make it do: jailbreaks, prompt injection, data exfiltration through tools and multilingual harm coverage. Production teams typically need both, staffed as separate roles." } }, { "@type": "Question", "name": "Who owns the data your team produces?", "acceptedAnswer": { "@type": "Answer", "text": "You do. Every engagement runs on work-for-hire terms that assign all created data, rubrics and derivative artifacts to your company, with consent and provenance records attached for any collected audio, image or video." } }, { "@type": "Question", "name": "Do you replace crowd platforms like Scale, Surge or Mechanical Turk?", "acceptedAnswer": { "@type": "Answer", "text": "Different model. Crowd platforms give elastic throughput from a rotating anonymous pool. We provide a named, dedicated pod that stays on your taxonomy for months, joins your standups and builds context, which is what quality depends on for RLHF, expert reasoning traces and agentic evaluation." } }, { "@type": "Question", "name": "What is physical AI training data?", "acceptedAnswer": { "@type": "Answer", "text": "Physical AI training data is the human-generated data used to train robots, world models and embodied agents: egocentric and third-person video of real interactions, depth and LiDAR captures, teleoperated manipulation trajectories, and paired instruction-demonstration data. Unlike text-only LLM data, it has to be captured in the physical world because no internet corpus contains it." } }, { "@type": "Question", "name": "How much does teleoperation data collection cost per hour?", "acceptedAnswer": { "@type": "Answer", "text": "Managed teleoperation with LATAM operators runs roughly $28–$42/hr depending on rig complexity and whether force/torque feedback is involved. Egocentric video capture and enrichment runs $20–$30/hr, and 3D/LiDAR annotation $26–$38/hr. Pricing is per operator hour, billed monthly, with QA and project management included." } }, { "@type": "Question", "name": "How many demonstrations do I need to train a manipulation policy?", "acceptedAnswer": { "@type": "Answer", "text": "It depends on task horizon and variation, but teams typically start seeing usable behavior around 100–300 clean demonstrations per skill and plateau in the low thousands with sufficient scene, object and lighting diversity. We usually scope a pilot of a few hundred episodes, measure policy success rate, then scale the axes that actually move it." } }, { "@type": "Question", "name": "Can synthetic data replace real robot data?", "acceptedAnswer": { "@type": "Answer", "text": "Not on its own. Simulation gives volume and safety for pre-training, but the visual domain gap and approximated contact physics mean contact-rich and long-tail behavior still fails. The pattern that works is pre-train in simulation, then fine-tune on real captured demonstrations and annotated interaction video." } }, { "@type": "Question", "name": "Who owns the physical AI datasets you collect?", "acceptedAnswer": { "@type": "Answer", "text": "You do. Collection runs on work-for-hire terms that assign the raw captures, derived channels and annotations to your company, with participant consent and provenance records attached to every batch." } }, { "@type": "Question", "name": "What formats do you deliver robotics datasets in?", "acceptedAnswer": { "@type": "Answer", "text": "WebDataset, HDF5, Parquet or RLDS shards delivered to your S3 or GCS buckets, with per-episode metadata, sensor timestamps, checksums and an acceptance report. If your training stack expects a custom schema, we conform to it during the pilot rather than after." } }, { "@type": "Question", "name": "How do you measure quality on video and trajectory data?", "acceptedAnswer": { "@type": "Answer", "text": "Blind golden sets for annotation precision, inter-annotator agreement on action boundaries and affordances (κ ≥ 0.80 target), deterministic replay checks on trajectories, and episode-level success scoring reviewed by a second annotator on anything contested. Metrics are reported weekly per batch." } }, { "@type": "Question", "name": "Can you scale to hundreds of capture hours per month?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Capture, enrichment and annotation are staffed as separate lanes so throughput scales independently: we start with a pilot pod, lock the rubric and capture protocol, then add operators in cohorts while agreement metrics are monitored so quality does not drop as volume grows." } } ] }, { "@type": "WebPage", "@id": "https://www.hireslink.com/human-data-llm-training/#webpage", "url": "https://www.hireslink.com/human-data-llm-training", "name": "LLM Training Data Teams | RLHF & SFT Staffing", "description": "Hire vetted LatAm teams for RLHF, SFT, code annotation, and multimodal labeling. Cohen's Kappa ≥ 0.80 QA guarantees. US time-zone. Scale from 10 to 400+ annotators in weeks.", "isPartOf": { "@id": "https://www.hireslink.com/#website" }, "breadcrumb": { "@id": "https://www.hireslink.com/human-data-llm-training/#breadcrumb" }, "about": { "@id": "https://www.hireslink.com/human-data-llm-training/#service" } } ] } ] --- 1. [Home ](/) 2. [Industries](/industries/startups) 3. LLM Training Data [![HiresLink](/assets/logo-hireslink-white-Dm6TMXTK.png) Hireslink A company from Bait Group ](/) Industries Candidates Services Company [Portal Login](https://clients.hireslink.com) Candidate Apply Trusted by Leading AI Companies • ≤72h Time-to-Ramp • κ ≥ 0.80 Agreement • 0.0% Leakage Your LATAM Hiring Department. # Human Data & Physical AI Training Data — LATAM Teams LATAM teams for RLHF, SFT and physical AI training data — teleoperation, egocentric video and robotics annotation. Cohen's Kappa ≥ 0.80 QA, US time zones, from $18/hr. Scale from a 10-person pilot to 400+ specialists with zero data leakage. - 48-hour shortlists - Unlimited staffing replacements - Transparent pricing - LATAM hiring + HR support ≤72h shortlists Fully compliant Culture-first matches Build Your Annotation TeamSee QA Guarantees ## Why AI Companies Choose HiresLink for Human Data ### Time-to-ramp Recruit, vet, and onboard annotators in ≤72 hours. ≤72h ### Throughput Multiplier 4.7× faster output vs. generic crowdsourcing platforms. 4.7× faster ### Agreement Quality Cohen's Kappa ≥ 0.80 with contract-level guarantees. κ ≥ 0.80 ### Revision Reduction −48% average handle time with dual review and QA gates. −48% AHT ### Zero Leakage 0.0% data leakage on golden sets with SOC-style controls. 0.0% leakage 55% Cost Savings ≤72h Time to Shortlist 98% 90-day Retention See How It Works Strategic Partnership ## Why ML Leaders Choose HiresLink for LLM Training Data We are your strategic partner in Latin America, building high-performance teams that scale with your vision ### Contract-Level QA We commit to measurable agreement targets (Cohen's Kappa ≥ 0.80) with clawbacks if we fall short. Your data quality is our responsibility, backed by SLAs. ### Full People-Ops Payroll, compliance, PTO, training, and free replacements—all included. You focus on model architecture; we handle the human workforce. ### Infra-Agnostic Works with Hugging Face, PyTorch, Label Studio, Scale AI, or your custom annotation tools. We adapt to your ML pipeline, not the other way around. ### LatAm Advantage US time-zone overlap, English-fluent, tri-lingual squads (EN/ES/PT) at competitive rates. Cultural alignment + technical depth. Ready to build your LATAM advantage? Partner With Us ## End-to-End Human Data Services for LLMs Comprehensive solutions designed specifically for your industry needs ### ### RLHF (Reinforcement Learning from Human Feedback) Dedicated pods for preference modeling, ranking, and critique loops. Pairwise comparison ranking for model output evaluation Multi-turn dialogue preference annotation Critique and refinement loops for iterative improvement Reward model training data generation Safety and alignment preference labeling Key Metrics Tracked: Inter-annotator agreement Preference consistency Edge case coverage ### ### SFT (Supervised Fine-Tuning) Teams Instruction tuning, taxonomy tagging, and rubric execution at scale. High-quality instruction-response pair creation Domain-specific taxonomy development and tagging Rubric-based annotation with golden set calibration Multi-lingual data generation (EN/ES/PT) Prompt engineering and template refinement Key Metrics Tracked: Label accuracy Coverage completeness Time per unit ### ### Code & UI Labeling SWE-bench style evaluations, UI intents, and toolformer traces. Code generation task annotation with test case validation UI/UX intent labeling for multimodal models Tool use and function calling trace collection Bug detection and code review annotation Repository-level context understanding tasks Key Metrics Tracked: Functional correctness Edge case handling Tool invocation accuracy ### ### Multimodal Annotation Audio, vision, speech, and safety red-teaming for comprehensive model training. Image and video content annotation with bounding boxes Audio transcription and speech recognition labeling Facial analysis and emotion detection datasets Safety red-teaming for harmful content detection Cross-modal alignment verification Key Metrics Tracked: Multi-modal consistency Safety coverage Annotation precision ### Our Platform Advanced tools for seamless collaboration ![Annotation quality dashboard](/assets/platform-screen-1-DHnL_MUN.png) ![Human data operations platform](/assets/platform-screen-2-Iv3aNtnh.png) ![LLM training data pipeline](/assets/services-hero-2-Blr3QWCU.png) Ready to get started? Start Building Your Team ## Critical Roles We Staff for LLM Training ### RLHF Annotators Preference ranking, pairwise comparison, reward model data generation Stack: Ranking UI Preference Models Safety Guidelines Bilingual (EN/ES) Mid to Senior ### SFT Specialists Instruction-response pairs, taxonomy tagging, rubric execution Stack: Label Studio Annotation Tools Quality Rubrics Domain Expertise Mid to Senior ### Code Annotators SWE-bench evals, UI intents, function calling, toolformer traces Stack: Python/JS GitHub Test Frameworks Code Review Senior ### Multimodal Experts Audio/vision labeling, speech analysis, safety red-teaming Stack: Vision Tools Audio Software Safety Protocols Cross-modal Mid to Senior Get Candidate Matches ## From Pilot to Production—Our Human Data Playbook 1 ### Recruit & Vet(≤72 hours) We source bilingual annotators and subject-matter experts (EN/ES/PT) from our vetted LatAm network and screen them within 72 hours. 2 ### Playbooks & Onboarding(3-5 days) Build custom annotation rubrics, golden test sets, leakage controls, and calibration workflows tailored to your LLM task. 3 ### Scale & Manage(1-2 weeks) Ramp from pilot (10 annotators) to production (400+) with US time-zone coverage, sprint cadences, and agile iteration. 4 ### Quality & Security(Continuous) Maintain Cohen's Kappa ≥ 0.80, dual review on critical tasks, spot audits, and SOC-style security controls with zero data leakage. 5 ### Iterate & Optimize(Ongoing) Weekly QA reports, A/B test rubric changes, refresh golden sets, and optimize cost per labeled unit while maintaining quality targets. ![Human data annotation team collaboration](/assets/services-hero-1-BYG6mcbY.png) ![RLHF and SFT specialists](/assets/services-team-1-Cb-dkzPZ.jpg) ![Multimodal annotation workforce](/assets/services-team-2-COk2xXZq.jpg) See How It WorksStart Building Your Team ## The Proven Solution for World-Class Businesses ## What Our Clients Say Real results from companies using AI-powered hiring > "Hireslink's AI matching increased our team efficiency by 60%. The personality filtering helped us find developers who truly fit our culture." SC Sarah Chen CTO, TechFlow > "We hired 5 developers in 2 weeks instead of 3 months. The AI predictions were spot-on for both technical skills and work style." MR Marcus Rodriguez VP Engineering, DataCorp > "The quality of Latin American talent is exceptional. AI matching helped us build a diverse, high-performing remote team." JK Jennifer Kim Head of Product, InnovateLabs > "98% match accuracy isn't just a number - it's the difference between hiring success and costly mistakes. Hireslink delivers." DT David Thompson CEO, ScaleUp ## Ready to Transform Your Hiring? Join 200+ companies saving 55% on talent costs. Get matched with perfect candidates in ≤72h. Build Your Annotation TeamDownload Success Guide No Setup Fees ≤72h Guarantee Cancel Anytime Three ways to work with us ## Choose how you want to hire. Same network, same vetting bar. The difference is who employs the person and who runs the HR layer. ### Staffing & HR Management For companies building a team. HiresLink manages the hiring and HR/operational layer month to month. - Qualified candidates in 48 hours after the role brief - Unlimited free replacements for active staffing clients - Onboarding, payroll coordination, vacations and performance support - Lower of $800/month or a 25% management fee - $500 kickoff, credited to your first invoice [Build My Team](/services/hires-staffing) ### Headhunting / Direct Hire For companies making one specific key hire. You employ the person directly. One-time fee, no recurring management fee. - Shortlist in 7–10 business days - 20% of first-year salary - $600 kickoff, credited to your first placement invoice - 90-day replacement (junior / semi-senior), 120 days (senior & managerial) - Sourcing, vetting and interview coordination [Find My Hire](/services/headhunting-pro) ### Staff Augmentation For companies adding capacity to an existing team. Vetted LATAM specialists plug into the team and processes you already run. - Add engineers and specialists to your existing team - You direct the day-to-day work - Scale the team up or down as the roadmap changes - Open salary benchmarks before you commit [Add LATAM Talent](/services/staff-augmentation) ## Production-Grade Quality Metrics Real KPIs from our human-data operations—measured, reported, and guaranteed Time-to-ramp ≤72h Throughput/hr 4.7× Agreement κ ≥ 0.80 Revision rate −48% Cost per labeled unit Ready to achieve these metrics? Build Your Team NowDiscuss Your Project ## Compare Human-Data Providers See how HiresLink stacks up against other RLHF/SFT staffing options for enterprise LLM training Feature HiresLink Generic Crowdsourcing Traditional Staffing Specialized in LLM Human Data (RLHF/SFT/Annotation) Contract-Level QA Guarantees (Cohen's Kappa ≥ 0.80) Zero Data Leakage on Golden Sets Rapid Ramp (10→400+ annotators in weeks) Full HR + Payroll + Compliance Multimodal Expertise (Audio/Vision/Code) US Time-Zone Coverage Infra-Agnostic (HuggingFace, Label Studio, Custom) #### Specialized in LLM Human Data (RLHF/SFT/Annotation) HiresLink Yes Generic Crowdsourcing No Traditional Staffing No #### Contract-Level QA Guarantees (Cohen's Kappa ≥ 0.80) HiresLink Yes Generic Crowdsourcing No Traditional Staffing Partial #### Zero Data Leakage on Golden Sets HiresLink Yes Generic Crowdsourcing No Traditional Staffing Partial #### Rapid Ramp (10→400+ annotators in weeks) HiresLink Yes Generic Crowdsourcing Partial Traditional Staffing No #### Full HR + Payroll + Compliance HiresLink Yes Generic Crowdsourcing No Traditional Staffing Yes #### Multimodal Expertise (Audio/Vision/Code) HiresLink Yes Generic Crowdsourcing Partial Traditional Staffing No #### US Time-Zone Coverage HiresLink Yes Generic Crowdsourcing Partial Traditional Staffing Yes #### Infra-Agnostic (HuggingFace, Label Studio, Custom) HiresLink Yes Generic Crowdsourcing No Traditional Staffing No Comparison based on typical market offerings. Features may vary by provider. See the difference for yourself Get Started with HiresLink ## Deep Dive: LLM Training Best Practices Learn how to design production-grade human-data operations for enterprise AI ![RLHF annotation workflow](/assets/services-hero-1-BYG6mcbY.png) ### RLHF vs SFT ### RLHF vs SFT: When to use each for enterprise LLMs **Reinforcement Learning from Human Feedback (RLHF)** and **Supervised Fine-Tuning (SFT)** are complementary approaches to improving LLMs with human-generated data. Understanding when to deploy each can dramatically impact model performance, safety, and alignment. Key Insight Use SFT when you need the model to follow specific instructions or formats. Deploy RLHF when you want to learn nuanced preferences, avoid harmful outputs, or optimize for subjective quality metrics. #### Supervised Fine-Tuning (SFT) SFT trains your LLM on high-quality input-output pairs. You provide examples of desired behavior: a prompt and the "correct" response. This works exceptionally well when: - You have clear, well-defined tasks (code generation, data extraction, translation) - The "correct" answer is objective or follows strict formatting rules - You need rapid iteration with thousands of labeled examples #### RLHF (Reinforcement Learning from Human Feedback) RLHF trains a reward model that captures human preferences. Annotators rank or rate multiple model outputs, teaching the system what "good" looks like in subjective scenarios. RLHF excels when: - Quality is subjective (conversational tone, helpfulness, creativity) - You need to reduce harmful, biased, or off-brand outputs - The task involves trade-offs (accuracy vs. brevity, safety vs. informativeness) #### The Enterprise Playbook: Combining Both 1. **Start with SFT** to teach basic instruction-following 2. **Layer RLHF on top** to fine-tune preferences and reduce edge cases 3. **Iterate continuously** as user feedback and business needs evolve At HiresLink, we staff teams for both: SFT annotators who create clean instruction-response pairs, and RLHF specialists who perform pairwise comparisons. Our QA ensures high agreement (κ ≥ 0.80) whether you're running SFT, RLHF, or both in parallel. Learn more about our [AI specialists](/candidates/ai-specialists) and how we help [SaaS companies](/industries/saas) and [AI startups](/industries/startups) scale their annotation operations. ![Quality assurance dashboard](/assets/platform-screen-1-DHnL_MUN.png) ### QA Design ### Designing QA for human-generated data: Kappa, golden sets, leakage control Quality assurance for human-generated data isn't optional—it's the foundation of reliable LLM training. Poor QA leads to noisy labels, downstream hallucinations, and wasted compute. Here's how to build production-grade QA that scales. #### 1\. Cohen's Kappa: Measure Inter-Annotator Agreement Cohen's Kappa (κ) quantifies agreement between annotators, accounting for random chance: - **κ ≥ 0.80**: Excellent agreement (production-ready) - **0.60–0.79**: Moderate (needs rubric refinement) - **< 0.60**: Poor (task too ambiguous or training inadequate) Track Kappa weekly per task type. If agreement drops, pause labeling and diagnose: rubric unclear? Need retraining? Edge cases emerging? #### 2\. Golden Sets: Calibrate and Audit A golden set is curated labeled examples with known "correct" answers. Use them to: - **Onboard new annotators**: Must hit 85%+ accuracy before production - **Spot-check work**: Inject golden examples into queues (10-15% of tasks) - **A/B test changes**: Measure impact on golden-set accuracy before rollout Best Practice Build your golden set iteratively. Start with 100-200 examples covering common scenarios and edge cases. Refresh quarterly as your model evolves. #### 3\. Leakage Control: Protect Model Integrity Data leakage—when annotators memorize or copy from training/test sets—corrupts evaluation. Mitigation: - **Blind golden sets**: Annotators don't know which tasks are audits - **No local persistence**: Data lives in secure cloud environments only - **Rotation & access controls**: Limit exposure; rotate across task types - **Audit trails**: Log every label with timestamps, IPs, session metadata #### 4\. Dual Review and Adjudication For high-stakes labels (safety, compliance, golden-set creation), implement dual review: two independent annotators label the same task. If they agree, finalize. If not, a senior reviewer adjudicates. #### 5\. Cost vs. Quality Trade-offs - **High-risk** (safety, medical/legal): Dual review, expert annotators, κ ≥ 0.85 - **Moderate-risk** (instruction tuning): Single review + spot checks, κ ≥ 0.75 - **Low-risk** (bulk pretraining data): Acceptance sampling, κ ≥ 0.65 At HiresLink, we design QA workflows tailored to your risk profile and budget. Contract-level guarantees ensure predictable quality with transparency—weekly Kappa reports, golden-set dashboards, and leakage audits included. ## Anonymized Client Results Real outcomes from AI companies building production LLMs with human-generated data ![Annotation team achieving 4.7x throughput](/assets/services-team-2-COk2xXZq.jpg) 4.7× Throughput Growth 1,000 tasks in 8 days (code + UI annotation) −48% Average Handle Time Reduction +76% approval rate in multimodal annotation pipeline with dual review 30→120 RLHF Pods Scaled in 7 Days 0.0% leakage on golden sets, κ = 0.83 maintained throughout ramp ### Want Results Like These? Join leading AI companies building production LLMs with measurable quality guarantees Start Your Pilot TeamSchedule a Consultation AI training data services ## The human data catalog for LLM and agent teams Ten AI data services staffed from Latin America, each with defined deliverables, a quality metric written into the contract, and a bilingual team working your hours. Pick one service or run several in the same pod. ### RLHF & Preference Optimization Pairwise comparisons, rankings and critique loops that turn human judgement into reward-model training data for PPO and DPO pipelines. Deliverables - Pairwise preference datasets with rationale fields - Multi-turn dialogue rankings - Reward model training splits + held-out evals **QA:** Inter-annotator agreement κ ≥ 0.80 $26–36/hr [Hire rlhf & preference optimization specialists](/candidates/llm-specialists/rlhf) ### Supervised Fine-Tuning (SFT) Data Instruction-response pair creation, taxonomy tagging and rubric execution for domain-specific fine-tuning in English, Spanish and Portuguese. Deliverables - Instruction-response pairs with golden-set calibration - Domain taxonomies and labeling guidelines - Rubric scoring with dual review on high-stakes items **QA:** Label accuracy ≥ 95% on golden sets $23–33/hr [Hire supervised fine-tuning (sft) data specialists](/candidates/llm-specialists/sft) ### Data Collection & Creation Net-new human-generated data: prompts, long-form writing, speech recordings, screen and device capture, and scenario scripting for edge cases your logs never contain. Deliverables - Prompt and response corpora written to spec - Audio, image and video capture with consent records - Scenario libraries for rare and adversarial cases **QA:** Spec compliance ≥ 97% at acceptance sampling $18–28/hr [Hire data collection & creation specialists](/candidates/llm-specialists/data-collection) ### Model Safety & Red Teaming Adversarial testing of deployed and pre-release models: jailbreaks, prompt injection, harmful content probing and agentic misuse scenarios. Deliverables - Attack taxonomies with reproducible prompts - Severity-scored findings and mitigation notes - Regression suites for each patched failure mode **QA:** Attack coverage across 12+ harm categories $28–40/hr [Hire model safety & red teaming specialists](/candidates/llm-specialists/red-teaming) ### Model Evaluation & Benchmarking Human evals, LLM-as-judge calibration, golden datasets and regression suites so every prompt or model change is measured instead of guessed. Deliverables - Golden datasets with versioning - Human eval rubrics and calibration sets - Release-gate scorecards per model version **QA:** Judge-human correlation tracked per release $25–35/hr [Hire model evaluation & benchmarking specialists](/candidates/llm-specialists/model-evaluation) ### Multilingual & Localization Data Native Spanish, Portuguese and English data creation, translation review, and locale-aware safety labeling for models serving the Americas. Deliverables - Native-quality ES/PT/EN datasets - Locale-specific safety and toxicity labels - Cultural adequacy review of model outputs **QA:** Native reviewer sign-off on 100% of batches $20–30/hr [Hire multilingual & localization data specialists](/candidates/llm-specialists/multilingual-data) ### Code & Agentic Traces SWE-bench style task annotation, tool-calling traces, UI intents and agent trajectories labeled by engineers who read and run the code. Deliverables - Repository-level task annotations with test validation - Tool-invocation and function-calling traces - Agent trajectory scoring with failure taxonomies **QA:** Functional correctness verified by test runs $30–45/hr [Hire code & agentic traces specialists](/candidates/developers/llm) ### Multimodal Annotation Vision, audio and video labeling: bounding boxes, segmentation, transcription, diarization and cross-modal alignment checks. Deliverables - Bounding boxes, polygons and segmentation masks - Transcription and speaker diarization - Cross-modal alignment verification **QA:** Annotation precision audited on blind golden sets $18–30/hr [Hire multimodal annotation specialists](/candidates/ai-specialists/computer-vision) ### RL Environments Task environments, simulators and verifiable reward functions for training and evaluating agents on real software workflows. Deliverables - Containerized task environments with deterministic setup - Verifiable reward functions and scoring harnesses - Difficulty-graded task suites with reference solutions **QA:** Deterministic reproduction on 100% of tasks $32–48/hr [Hire rl environments specialists](/candidates/llm-specialists/rl-environments) ### Robotics & Teleoperation Data Managed teleoperation sessions, manipulation trajectories and force/torque traces for training and evaluating real-world robot policies. Deliverables - Teleoperated demonstrations with observation-action pairs - Episode success scoring and failure taxonomies - Sensor-synced trajectories delivered as HDF5 or RLDS **QA:** Deterministic replay checks on every episode $28–42/hr [Hire robotics & teleoperation data specialists](/human-data-llm-training#physical-ai-stack) ### Egocentric Video & World Model Data First-person capture with depth, segmentation, hand pose and action boundaries — the interaction video world models and VLA systems cannot scrape. Deliverables - Wearable-camera capture with consent records - Depth, segmentation and hand-pose enrichment - Action-boundary and affordance annotation **QA:** Annotation agreement κ ≥ 0.80 on action boundaries $20–30/hr [Hire egocentric video & world model data specialists](/human-data-llm-training#physical-ai) ### Domain Expert Networks Licensed and credentialed professionals — clinicians, lawyers, accountants, engineers — writing and reviewing data where a generalist annotator cannot. Deliverables - Expert-written Q&A and reasoning traces - Expert adjudication of contested labels - Domain rubric design and annotator training **QA:** Credential verification on every expert $35–70/hr [Hire domain expert networks specialists](/candidates/llm-specialists/domain-experts) ## Datasets and modalities we staff for Which profile executes each data type, what it is typically used for, and what it costs per hour with a dedicated LATAM team. AI training data modalities, use cases, staffing profile and LATAM hourly rates Modality Typical use cases Who executes it LATAM rate Text Instruction tuning, preference data, summarization, safety labels SFT specialists, RLHF annotators $18–33/hr Code SWE-bench tasks, code review labels, unit-test generation Software engineers annotating part-time or full-time $30–45/hr Agent traces Tool calling, browser and terminal trajectories, failure taxonomies LLM engineers, RL environment engineers $32–48/hr Audio & speech Transcription, diarization, accent coverage, TTS reference recordings Bilingual transcriptionists, linguists $16–26/hr Image Bounding boxes, segmentation, OCR ground truth, caption quality Multimodal annotators, CV-adjacent reviewers $16–28/hr Video Action recognition, temporal segmentation, safety review Multimodal annotators with QA training $18–30/hr Tabular & documents Invoice/contract extraction ground truth, structured-output evals Domain experts (finance, legal, health) $22–70/hr ## Three ways to engage Most teams start with a managed project to prove quality, then convert to a dedicated pod once the taxonomy stabilizes. ### Dedicated pod Ongoing programs with evolving guidelines and confidential data A named team of annotators plus a QA lead and a project manager, working your hours, in your tooling, reporting weekly agreement metrics. **Pricing:** Hourly per seat, monthly invoicing ### Managed project Fixed-scope datasets with a defined volume and deadline We scope the taxonomy, build the golden set, staff the team and deliver the dataset against an agreed acceptance criteria. **Pricing:** Per project or per accepted unit ### Staff augmentation Teams that already run their own annotation platform and QA We source, vet and place individual specialists who report directly to your leads. You manage the workflow; we handle contracts and payroll. **Pricing:** Hourly per specialist ## Compliance and data governance How we handle your prompts, model outputs and collected data across every engagement. ### NDAs before access Every contributor signs an NDA and is named on the project. No anonymous crowd, no subcontracting without your approval. ### No local persistence Prompts, outputs and datasets live in your environment or a controlled cloud workspace. Nothing is stored on personal devices. ### Scoped access and rotation Access is limited to the task types each contributor needs, and rotated as programs change to reduce exposure. ### Audit trails Every label carries a timestamp, contributor ID and session metadata so you can reconstruct how any datapoint was produced. ### PII handling PII-bearing tasks are redacted or routed to a restricted pod with explicit handling rules agreed before work starts. ### Ownership and provenance Work-for-hire terms assign all created data to you, with consent and provenance records for collected audio, image and video. Requirements vary by industry. If you need specific contractual controls for GDPR, CCPA or client-imposed security terms, we align them before the pod starts — see [how our staff augmentation contracts work](/services/staff-augmentation). ## On this page - [Physical AI & embodied data](#physical-ai) - [The physical AI data stack](#physical-ai-stack) - [Synthetic vs. real](#sim-vs-real) - [Capture & delivery pipeline](#physical-ai-pipeline) - [Sensor modalities & rates](#sensor-modalities) - [Compliance for physical data](#physical-ai-compliance) Physical AI training data ## Physical AI & embodied data: the half of the training set that cannot be scraped Language models learned from a corpus that already existed. Physical AI — robots, world models and embodied agents — has no equivalent. There is no internet-scale archive of a hand rotating a wrench, a gripper failing to seat a connector, or a mobile base negotiating a cluttered warehouse aisle with a person walking into frame. That data has to be captured, on purpose, by people. This is why physical AI programs stall on data rather than on architecture. Teams have the policy code and the simulator; what they lack is a steady supply of real egocentric video, teleoperated demonstrations, depth and force traces, and the human judgment to label what actually happened in each episode. We staff that supply from Latin America, in your time zone, under the same QA discipline we run for [RLHF](/candidates/llm-specialists/rlhf) and [supervised fine-tuning](/candidates/llm-specialists/sft). The work spans four layers — perception, world modeling, policy learning and language grounding — plus the capture pipeline and compliance controls that make video-based datasets usable in production. Closely related roles live on our [RL environment engineers](/candidates/llm-specialists/rl-environments) and [computer vision engineers](/candidates/ai-specialists/computer-vision) pages. ## The physical AI data stack Each layer of an embodied system consumes a different kind of human data, executed by a different profile. ### Perception What is in the scene, and where is it? Egocentric and third-person video with depth, semantic and instance segmentation, 3D bounding boxes and object state labels. - Frame-level semantic + instance masks - 3D boxes with orientation and occlusion flags - Depth and RGB-D alignment checks Executed by: Multimodal annotators with CV review training ### World modeling What happens next if the robot acts? Long-horizon video of real physical interactions — contact, deformation, slippage, tool use — with optical flow and action labels. - Action-boundary segmentation on continuous video - Optical flow and motion-consistency review - Outcome labels for success, failure and recovery Executed by: Video annotators + QA leads ### Policy learning Which action should the robot take right now? Manipulation trajectories, teleoperation demonstrations and paired observation-action sequences with reward or success signals. - Teleoperated demonstrations at target episode counts - Observation-action pairs synced to sensor timestamps - Success scoring and failure taxonomies per episode Executed by: Teleoperators and RL environment engineers ### Language grounding (VLA) How does an instruction map to a physical action? Video-caption pairs, natural-language instructions followed by demonstration, and step-level task decompositions. - Instruction → demonstration pairs in EN/ES/PT - Step decomposition with affordance labels - Caption quality and grounding verification Executed by: Bilingual annotators + domain reviewers ## Synthetic vs. real: where simulation stops working IsaacSim, MuJoCo and PyBullet are indispensable for volume and safety — and insufficient on their own. ### Visual domain gap Rendered scenes in IsaacSim, MuJoCo or PyBullet miss the sensor noise, motion blur, lighting variance and lens artifacts a real camera produces, so perception stacks trained purely in sim degrade the moment they see a real frame. ### Contact physics is approximated Friction, compliance, deformation and slippage are simplified in every simulator. Contact-rich tasks — insertion, folding, pouring, cable routing — are exactly where policies trained only in simulation break. ### The long tail is not simulated Cluttered counters, half-open drawers, unexpected humans in frame, objects that are wet, bent or mislabeled. Nobody writes those scenarios into a simulator; they get captured. ### The working pattern: pre-train sim, fine-tune real Simulation buys volume and safety for the base policy. Real human-captured demonstrations and annotated interaction video are what close the sim2real gap. Our teams supply the real half of that pipeline. ## Capture, enrichment and delivery pipeline From an empty dataset to shards your training job can read, with a human in the loop at every stage. ### 1\. Capture Net-new data recorded to your spec, because logs from a model that has never touched the physical world do not exist. - Wearable and head-mounted camera capture - Managed teleoperation sessions on your rig or ours - Scripted scenario capture in controlled settings - Consent, release and provenance records per session ### 2\. Enrichment Raw footage becomes trainable signal through derived channels and human verification of every automated output. - Monocular depth estimation with human verification - Semantic and instance segmentation - Hand and body pose tracking - Optical flow, temporal alignment and captioning ### 3\. Annotation Judgment-heavy labeling where automation still fails, run against a golden set and a documented rubric. - Action boundaries on continuous video - Affordance and object-state labeling - Episode quality scoring and failure taxonomy - Dual review on high-stakes and contested items ### 4\. Delivery Datasets shipped in the format your training stack already reads, with integrity checks and documentation. - WebDataset, HDF5, Parquet or RLDS shards - Delivery to your S3 / GCS buckets - Checksums and per-batch acceptance reports - Datasheet describing collection, consent and limits ## Sensor modalities we staff Rates are per specialist hour from Latin America, fully managed, in US time zones. Modality Use cases Profile Starting at RGB video (third-person) Scene understanding, action recognition, benchmark evaluation Multimodal annotators $18–28/hr Egocentric video (wearable) Hand-object interaction, first-person world models, VLA grounding Capture collaborators + annotators $20–30/hr Depth / RGB-D 3D boxes, grasp planning ground truth, occlusion reasoning CV-adjacent reviewers $22–32/hr LiDAR & point clouds Navigation, mapping, outdoor and warehouse robotics Senior 3D annotators $26–38/hr IMU & proprioception Motion segmentation, gait and manipulation state estimation Robotics-literate annotators $24–34/hr Force / torque Contact-rich manipulation, insertion and assembly policies Teleoperators with robotics background $28–42/hr Ambient audio Event detection, failure cues, multimodal alignment Bilingual audio annotators $16–26/hr ## Compliance for physical-world data Video and sensor capture carries obligations that text annotation never does. ### Capture consent Every recorded participant signs a release covering the recording, its intended use in model training and downstream redistribution rights. Records are delivered with the dataset. ### Face and plate blurring Bystander faces, license plates, screens and documents can be blurred at delivery, with a human pass verifying that redaction actually landed on every frame. ### PII inside video Video carries PII that text pipelines never see — homes, addresses, badges, children. We scope capture locations up front and review batches against an agreed exclusion list. ### Retention and chain of custody Data stays in your buckets or an approved environment, with role-based access, audit logs, no local persistence and an agreed retention and deletion window. ## Explore the human data for LLMs cluster Every AI training data service we staff from Latin America — RLHF, supervised fine-tuning, data collection, red teaming, evaluation, multilingual data, agentic traces and domain expert networks — plus the roles and salary benchmarks behind them. [ ### RLHF & Preference Optimization Pairwise comparisons, rankings and critique loops that turn human judgement into reward-model training data for PPO and DPO pipelines. $26–36/hr · Inter-annotator agreement κ ≥ 0.80 ](/candidates/llm-specialists/rlhf)[ ### Supervised Fine-Tuning (SFT) Data Instruction-response pair creation, taxonomy tagging and rubric execution for domain-specific fine-tuning in English, Spanish and Portuguese. $23–33/hr · Label accuracy ≥ 95% on golden sets ](/candidates/llm-specialists/sft)[ ### Data Collection & Creation Net-new human-generated data: prompts, long-form writing, speech recordings, screen and device capture, and scenario scripting for edge cases your logs never contain. $18–28/hr · Spec compliance ≥ 97% at acceptance sampling ](/candidates/llm-specialists/data-collection)[ ### Model Safety & Red Teaming Adversarial testing of deployed and pre-release models: jailbreaks, prompt injection, harmful content probing and agentic misuse scenarios. $28–40/hr · Attack coverage across 12+ harm categories ](/candidates/llm-specialists/red-teaming)[ ### Model Evaluation & Benchmarking Human evals, LLM-as-judge calibration, golden datasets and regression suites so every prompt or model change is measured instead of guessed. $25–35/hr · Judge-human correlation tracked per release ](/candidates/llm-specialists/model-evaluation)[ ### Multilingual & Localization Data Native Spanish, Portuguese and English data creation, translation review, and locale-aware safety labeling for models serving the Americas. $20–30/hr · Native reviewer sign-off on 100% of batches ](/candidates/llm-specialists/multilingual-data)[ ### Code & Agentic Traces SWE-bench style task annotation, tool-calling traces, UI intents and agent trajectories labeled by engineers who read and run the code. $30–45/hr · Functional correctness verified by test runs ](/candidates/developers/llm)[ ### Multimodal Annotation Vision, audio and video labeling: bounding boxes, segmentation, transcription, diarization and cross-modal alignment checks. $18–30/hr · Annotation precision audited on blind golden sets ](/candidates/ai-specialists/computer-vision)[ ### RL Environments Task environments, simulators and verifiable reward functions for training and evaluating agents on real software workflows. $32–48/hr · Deterministic reproduction on 100% of tasks ](/candidates/llm-specialists/rl-environments)[ ### Robotics & Teleoperation Data Managed teleoperation sessions, manipulation trajectories and force/torque traces for training and evaluating real-world robot policies. $28–42/hr · Deterministic replay checks on every episode ](/human-data-llm-training#physical-ai-stack)[ ### Egocentric Video & World Model Data First-person capture with depth, segmentation, hand pose and action boundaries — the interaction video world models and VLA systems cannot scrape. $20–30/hr · Annotation agreement κ ≥ 0.80 on action boundaries ](/human-data-llm-training#physical-ai)[ ### Domain Expert Networks Licensed and credentialed professionals — clinicians, lawyers, accountants, engineers — writing and reviewing data where a generalist annotator cannot. $35–70/hr · Credential verification on every expert ](/candidates/llm-specialists/domain-experts) ### Related pages - [Hire LLM specialists](/candidates/llm-specialists) - [Physical AI training data (robotics, teleoperation)](/human-data-llm-training#physical-ai) - [Hire AI specialists](/candidates/ai-specialists) - [Hire LLM stack developers](/candidates/developers/llm) - [Hire machine learning engineers](/candidates/ai-specialists/machine-learning) - [Hire computer vision engineers](/candidates/ai-specialists/computer-vision) - [Staff augmentation](/services/staff-augmentation) - [AI / ML engineer salaries in LATAM](/latam-salaries/ai-ml-engineer/argentina) - [Data engineer salaries in LATAM](/latam-salaries/data-engineer/argentina) - [AI startups & SaaS hiring](/industries/saas) - [Nearshore teams for startups](/industries/startups) ## Frequently Asked Questions Everything you need to know about human-data staffing for LLM training ### What is human-generated data for LLMs? Human-generated data refers to labels, annotations, preferences, and feedback created by real people (not synthetic models) to train, fine-tune, and evaluate large language models. This includes RLHF (Reinforcement Learning from Human Feedback), SFT (Supervised Fine-Tuning), code labeling, multimodal annotation, and more. ### How does RLHF staffing work? We recruit, vet, and onboard dedicated teams of annotators fluent in English (and Spanish/Portuguese as needed). We define your preference rubrics, build golden test sets, train the team, and scale from pilot (10 annotators) to production (400+) while maintaining quality (Cohen's Kappa ≥ 0.80) and security (zero leakage on golden sets). ### What QA guarantees do you offer? We commit to measurable inter-annotator agreement (Cohen's Kappa ≥ 0.80 for most tasks), dual review on critical labels, spot audits, leakage controls on golden sets, and contract-level clawbacks if we fall short. You get transparency with weekly quality reports. ### Do you support code and multimodal annotation? Yes. We staff code annotation (SWE-bench style evals, UI intents, toolformer traces) and multimodal tasks (audio transcription/labeling, image/video annotation, speech/facial analysis, safety red-teaming). Teams include domain experts (e.g., software engineers for code tasks). ### How do you handle security and compliance? All annotators undergo security vetting (background checks, NDA signing). We implement SOC-style controls: no local data persistence, VPN-only access, audit logs, role-based permissions. Data stays in your environment or approved cloud buckets. We're GDPR-aware and can align to your compliance requirements. ### How much does human data for LLM training cost? General annotation and data collection start around $18–$22/hr, RLHF preference work and SFT authoring run $23–$30/hr, evaluation and red teaming $25–$34/hr, RL environment engineering $32–$45/hr, and credentialed domain experts (clinicians, attorneys, CPAs) $35–$70/hr. A 10-person pilot pod typically lands between $28K and $40K per month fully managed, which is 50–70% below equivalent US-based staffing. ### How fast can you stand up a pilot annotation pod? 48 hours to a shortlist, and a 10-person pilot pod typically running within 7–10 business days. Week one covers rubric alignment and golden-set construction, week two is calibration until inter-annotator agreement clears the threshold, and production throughput starts in week three. ### Can you provide native Spanish and Portuguese data at scale? Yes. Native LATAM speakers cover Rioplatense, Mexican, Colombian, Chilean and Caribbean Spanish plus Brazilian Portuguese, with locale-aware idiom, register and cultural adequacy review rather than translated English prompts. See our multilingual AI data specialists for scope and rates. ### What is the difference between AI red teaming and model evaluation? Model evaluation measures whether the system does what it is supposed to do — accuracy, helpfulness, format compliance, regression against a golden dataset. Red teaming measures what an adversary can make it do — jailbreaks, prompt injection, data exfiltration through tools, and multilingual harm coverage. Most teams shipping to production need both, and we staff them as separate roles because the mindset differs. ### Who owns the data your team produces? You do. Every engagement runs on work-for-hire terms that assign all created data, rubrics and derivative artifacts to your company, with consent and provenance records attached for any collected audio, image or video. ### Do you replace crowd platforms like Scale, Surge or Mechanical Turk? Different model. Crowd platforms give you elastic throughput from a rotating anonymous pool. We give you a named, dedicated pod that stays on your taxonomy for months, joins your standups, and builds context — which is what quality depends on for RLHF, expert reasoning traces and agentic evaluation. Many teams keep a crowd vendor for high-volume simple labeling and use us for the judgment-heavy layer. ### What is physical AI training data? Physical AI training data is the human-generated data used to train robots, world models and embodied agents: egocentric and third-person video of real interactions, depth and LiDAR captures, teleoperated manipulation trajectories, and paired instruction-demonstration data. Unlike text-only LLM data, it has to be captured in the physical world because no internet corpus contains it. ### How much does teleoperation data collection cost per hour? Managed teleoperation with LATAM operators runs roughly $28–$42/hr depending on rig complexity and whether force/torque feedback is involved. Egocentric video capture and enrichment runs $20–$30/hr, and 3D/LiDAR annotation $26–$38/hr. Pricing is per operator hour, billed monthly, with QA and project management included. ### How many demonstrations do I need to train a manipulation policy? It depends on task horizon and variation, but teams typically start seeing usable behavior around 100–300 clean demonstrations per skill and plateau in the low thousands with sufficient scene, object and lighting diversity. We usually scope a pilot of a few hundred episodes, measure policy success rate, then scale the axes that actually move it. ### Can synthetic data replace real robot data? Not on its own. Simulation gives volume and safety for pre-training, but the visual domain gap and approximated contact physics mean contact-rich and long-tail behavior still fails. The pattern that works is pre-train in simulation, then fine-tune on real captured demonstrations and annotated interaction video. ### Who owns the physical AI datasets you collect? You do. Collection runs on work-for-hire terms that assign the raw captures, derived channels and annotations to your company, with participant consent and provenance records attached to every batch. ### What formats do you deliver robotics datasets in? WebDataset, HDF5, Parquet or RLDS shards delivered to your S3 or GCS buckets, with per-episode metadata, sensor timestamps, checksums and an acceptance report. If your training stack expects a custom schema, we conform to it during the pilot rather than after. ### How do you measure quality on video and trajectory data? Blind golden sets for annotation precision, inter-annotator agreement on action boundaries and affordances (κ ≥ 0.80 target), deterministic replay checks on trajectories, and episode-level success scoring reviewed by a second annotator on anything contested. Metrics are reported weekly per batch. ### Can you scale to hundreds of capture hours per month? Yes. Capture, enrichment and annotation are staffed as separate lanes so throughput scales independently: we start with a pilot pod, lock the rubric and capture protocol, then add operators in cohorts while agreement metrics are monitored so quality does not drop as volume grows. ### Ready to Scale Your Human Data Operations? Recruit, train, and manage vetted LatAm annotation teams with measurable QA guarantees. Get started in ≤72 hours. Build Your Annotation TeamTalk to Our Team ≤72h to first shortlist κ ≥ 0.80 QA guarantee Zero data leakage ### AI Staffing Insights Weekly trends on AI hiring and LATAM tech talent. ![Hireslink](https://ajrfrtiexzvxvomfrjnp.supabase.co/storage/v1/object/public/Images/Logos/hireslink_logo.jpeg)Hireslink AI-powered staffing connecting you with the top 3% of pre-vetted LATAM talent. 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