{ "schema": "5lc-selection-review/1", "sublayer": "data-evaluation", "asOf": "2026-10-20", "status": "researcher recommendation; root challenge pending", "scope": { "geography": "Global", "activity": "Commercial suppliers of model-training data, expert feedback, evaluation and RL environments. Traditional AI data is included; generic BPO and downstream application consulting are excluded.", "period": "Latest disclosed actual period through October 20, 2026; incompatible periods are kept separate." }, "eligibility": { "rule": "Current delivered commercial product/service or documented paying use. Public and private suppliers use identical functional scope. Supplier groups are consolidated by control, while minority stakes do not automatically merge independent businesses.", "unavailable": "Unknown relevant revenue does not imply zero activity or ineligibility." }, "predefinedFunctions": [ { "id": "expert-posttraining", "description": "Human expert data for model post-training and reasoning.", "required": true }, { "id": "multimodal-data", "description": "Industrial-scale speech, vision, physical and multilingual training data.", "required": true }, { "id": "data-software", "description": "Software for preparing, labeling and managing training/evaluation data.", "required": true }, { "id": "evaluation-environments", "description": "Commercial evaluation, test tasks and reinforcement-learning environments.", "required": true }, { "id": "independent-preference-evaluation", "description": "Cross-provider community preference and real-session evaluation sold as an independent service.", "required": true } ], "universe": { "initialFile": "candidate-universe.json", "reconciledCandidates": [ { "entityId": "appen", "name": "Appen", "economicParent": "appen", "eligible": true, "functions": [ "expert-posttraining", "multimodal-data" ], "roleEvidence": "Appen offers long-running multilingual data operations and access to both Chinese and Western model-development markets. Its reported revenue covers generative AI and older data services, so the full company cannot be equated with frontier-model post-training.", "sourceIds": [ "appen:apx-ar25", "appen:apx-h1-call", "appen:apx-home" ], "packetSha256": "bc22fa066a26c4ee0180694b0be25c04505c2ce6ddccfc829eceefee87a128e0", "sourceReviewFile": "models/reviews/appen.json", "sourceReviewHashMatched": true, "currentPacketHomeSublayer": "data-evaluation", "currentSummaryEvidence": { "tagline": "Human training data across global and Chinese AI markets", "description": "Appen supplies training examples, annotation and evaluation for language, speech, vision and physical-AI systems. It operates Appen Global and Appen China with separate delivery platforms. First-half 2026 revenue was $119.9 million: growth in China more than offset contraction in Global. The company reports financials in US dollars despite its Australian listing.", "whyItMatters": "Appen offers long-running multilingual data operations and access to both Chinese and Western model-development markets. Its reported revenue covers generative AI and older data services, so the full company cannot be equated with frontier-model post-training.", "evidence": "company_stated", "sourceIds": [ "appen:apx-home", "appen:apx-h1-call", "appen:apx-ar25" ] } }, { "entityId": "handshake-ai", "name": "Handshake AI", "economicParent": "handshake-ai", "eligible": true, "functions": [ "expert-posttraining", "evaluation-environments" ], "roleEvidence": "Handshake connects model developers with professional and academic expertise. The relevant supply is completed expert work; the much larger parent careers network is a recruitment channel, not an active AI workforce.", "sourceIds": [ "handshake-ai:hs-about", "handshake-ai:hs-ai", "handshake-ai:hs-enterprise", "handshake-ai:hs-skills" ], "packetSha256": "33328142e68280bee0418345969602f54fc4af99c8a5cf0ceeb7e00e706deee8", "sourceReviewFile": "models/reviews/handshake-ai.json", "sourceReviewHashMatched": true, "currentPacketHomeSublayer": "data-evaluation", "currentSummaryEvidence": { "tagline": "Expert data and evaluations drawn from a careers network", "description": "Handshake AI organizes paid expert work for model training and evaluation within the Handshake careers business. Its current fellowship page reports more than 188,000 fellows and $500 million in cumulative payouts. It also offers enterprise model adaptation and agent development. AI Skills Studio launched in September following the Uplimit acquisition. The separate AI business has no disclosed audited accounts.", "whyItMatters": "Handshake connects model developers with professional and academic expertise. The relevant supply is completed expert work; the much larger parent careers network is a recruitment channel, not an active AI workforce.", "evidence": "company_stated", "sourceIds": [ "handshake-ai:hs-ai", "handshake-ai:hs-enterprise", "handshake-ai:hs-skills", "handshake-ai:hs-about" ] } }, { "entityId": "mercor", "name": "Mercor", "economicParent": "mercor", "eligible": true, "functions": [ "expert-posttraining", "evaluation-environments" ], "roleEvidence": "The company connects model development with professional judgement and realistic working environments. Its importance rests on delivered expert work and named evaluation deployments. Network membership, internal evaluation throughput and gross billings describe different aspects of the business and cannot be added together as a score.", "sourceIds": [ "mercor:m-deeptune", "mercor:m-evals", "mercor:m-newsroom", "mercor:tc-faster" ], "packetSha256": "57c2d8c2beb83652f77554d06f384604fb31f1a7c52d89f033bc30b068cdac2f", "sourceReviewFile": "models/reviews/mercor.json", "sourceReviewHashMatched": true, "currentPacketHomeSublayer": "data-evaluation", "currentSummaryEvidence": { "tagline": "Marketplace of professionals hired to train and grade frontier AI", "description": "Mercor supplies professional experts, training tasks and evaluation services to AI developers. Its current website describes a network exceeding five million people and more than $4 million in daily expert payouts. Enterprise cases include Ramp and Cognition. Its June 2026 gross annualised revenue exceeded $2 billion, according to its chief executive as reported by TechCrunch; that measure includes contractor payments and is not retained revenue.", "whyItMatters": "The company connects model development with professional judgement and realistic working environments. Its importance rests on delivered expert work and named evaluation deployments. Network membership, internal evaluation throughput and gross billings describe different aspects of the business and cannot be added together as a score.", "evidence": "company_stated", "sourceIds": [ "mercor:m-newsroom", "mercor:m-evals", "mercor:m-deeptune", "mercor:tc-faster" ], "locator": "Homepage stat counters; Human Data & Evals page; TechCrunch 2026-07-08 and 2026-07-09" } }, { "entityId": "micro1", "name": "micro1", "economicParent": "micro1", "eligible": true, "functions": [ "expert-posttraining", "evaluation-environments" ], "roleEvidence": "Its current role spans expert recruitment, realistic agent-training tasks and enterprise evaluations. The breadth of those delivered services supports inclusion in the candidate universe; private financial disclosures still require explicit accounting and period qualifications.", "sourceIds": [ "micro1:m1-flow", "micro1:m1-home", "micro1:m1-q3" ], "packetSha256": "4bdbac108607d91c3778978ef8bfd3cc8f11d9f50eb1870813b9335a95b8cf05", "sourceReviewFile": "models/reviews/micro1.json", "sourceReviewHashMatched": true, "currentPacketHomeSublayer": "data-evaluation", "currentSummaryEvidence": { "tagline": "AI-interviewer-vetted experts producing training data and evaluations", "description": "micro1 recruits domain experts and combines their work with automated quality checks to supply training data, reinforcement-learning environments and production evaluations. Its October 2026 update reports annualised revenue above $1 billion, a $4 billion Series B valuation and a core team of roughly 180. The revenue definition is not specified and the figure is not a completed year of recognized sales.", "whyItMatters": "Its current role spans expert recruitment, realistic agent-training tasks and enterprise evaluations. The breadth of those delivered services supports inclusion in the candidate universe; private financial disclosures still require explicit accounting and period qualifications.", "evidence": "company_stated", "sourceIds": [ "micro1:m1-home", "micro1:m1-q3", "micro1:m1-flow" ], "locator": "Current company homepage; October 2026 Q3 founder update; Flow product overview" } }, { "entityId": "scale-ai", "name": "Scale AI", "economicParent": "scale-ai", "eligible": true, "functions": [ "expert-posttraining", "multimodal-data", "evaluation-environments" ], "roleEvidence": "Scale’s significance rests on delivered data, published evaluations and named operating deployments. Its minority relationship with a model developer also makes confidentiality and customer independence relevant to other laboratories. Applications revenue, contributor payouts and data-business activity require separate treatment.", "sourceIds": [ "scale-ai:sc-2026", "scale-ai:sc-home", "scale-ai:sc-newceo", "scale-ai:sc-transition", "scale-ai:tc-meta" ], "packetSha256": "b4ab98f53784b8276ed137b83b0633fbc9477cdfdb30ca5249844932cc45edb6", "sourceReviewFile": "models/reviews/scale-ai.json", "sourceReviewHashMatched": true, "currentPacketHomeSublayer": "data-evaluation", "currentSummaryEvidence": { "tagline": "Training data, evaluations and deployed enterprise AI", "description": "Scale supplies human-generated training data, operates the Outlier contributor platform and publishes model evaluations through SEAL. It also builds enterprise and public-sector AI applications. Francis deSouza became chief executive in August 2026; founder Alexandr Wang is chairman. Meta is a minority investor, with a 49% stake reported in June 2025. Current data-business revenue is not separately disclosed.", "whyItMatters": "Scale’s significance rests on delivered data, published evaluations and named operating deployments. Its minority relationship with a model developer also makes confidentiality and customer independence relevant to other laboratories. Applications revenue, contributor payouts and data-business activity require separate treatment.", "evidence": "company_stated", "sourceIds": [ "scale-ai:sc-home", "scale-ai:sc-2026", "scale-ai:sc-newceo", "scale-ai:sc-transition", "scale-ai:tc-meta" ], "locator": "Homepage; Outlier site; CEO letter 2026-01-22; TechCrunch 2025-06-13 and 2025-06-14; CEO appointment post 2026-07-30" } }, { "entityId": "snorkel-ai", "name": "Snorkel", "economicParent": "snorkel-ai", "eligible": true, "functions": [ "expert-posttraining", "data-software", "evaluation-environments" ], "roleEvidence": "It combines data delivery with research on how tasks and grading criteria are constructed. Published benchmarks and named enterprise cases establish concrete activity; they do not by themselves quantify market share or revenue from each service.", "sourceIds": [ "snorkel-ai:sn-cases", "snorkel-ai:sn-company", "snorkel-ai:sn-research", "snorkel-ai:sn-series-e" ], "packetSha256": "b223a6d90ef94b8cebed7d2e8d75d410c470528a014574cc0f9e7c0d24e8283f", "sourceReviewFile": "models/reviews/snorkel-ai.json", "sourceReviewHashMatched": true, "currentPacketHomeSublayer": "data-evaluation", "currentSummaryEvidence": { "tagline": "Stanford-born data lab selling expert datasets and RL environments", "description": "Snorkel develops expert datasets, reinforcement-learning environments and model evaluations, building on its Stanford research in programmatic labelling. It also develops specialized enterprise agents. In September 2026 it reported a $375 million annualised revenue run rate and $350 million Series E financing. Its public research includes professional-work, coding and computer-use benchmarks.", "whyItMatters": "It combines data delivery with research on how tasks and grading criteria are constructed. Published benchmarks and named enterprise cases establish concrete activity; they do not by themselves quantify market share or revenue from each service.", "evidence": "company_stated", "sourceIds": [ "snorkel-ai:sn-company", "snorkel-ai:sn-series-e", "snorkel-ai:sn-research", "snorkel-ai:sn-cases" ], "locator": "About page; Series E post (2026-09-22); TechCrunch 2026-09-22" } }, { "entityId": "surge-ai", "name": "Surge AI", "economicParent": "surge-ai", "eligible": true, "functions": [ "expert-posttraining", "evaluation-environments" ], "roleEvidence": "Surge contributes both the examples used in model training and the evaluations used to assess professional tasks. Named collaborations establish its role; benchmark capability, provider commentary and undated revenue references are not interchangeable measures of adoption.", "sourceIds": [ "surge-ai:s-anthropic", "surge-ai:s-iconiq", "surge-ai:s-mai", "surge-ai:s-products" ], "packetSha256": "3ee76befa21f7b29757580b4bf4902a3d1582693f08541de5a9ea37d91fe5a98", "sourceReviewFile": "models/reviews/surge-ai.json", "sourceReviewHashMatched": true, "currentPacketHomeSublayer": "data-evaluation", "currentSummaryEvidence": { "tagline": "Expert training data, reinforcement-learning environments and model evaluation", "description": "Surge supplies specialist human data, post-training work and evaluations to model developers. Current primary accounts describe projects with Anthropic and Microsoft, alongside public professional-work benchmarks. Edwin Chen remains chief executive. Its scale is substantial but current financial disclosure is limited: the latest inspected interview refers to revenue above a billion dollars without a clear accounting period.", "whyItMatters": "Surge contributes both the examples used in model training and the evaluations used to assess professional tasks. Named collaborations establish its role; benchmark capability, provider commentary and undated revenue references are not interchangeable measures of adoption.", "evidence": "company_stated", "sourceIds": [ "surge-ai:s-products", "surge-ai:s-anthropic", "surge-ai:s-mai", "surge-ai:s-iconiq" ] } }, { "entityId": "taskus", "name": "TaskUs", "economicParent": "taskus", "eligible": true, "functions": [ "expert-posttraining", "multimodal-data" ], "roleEvidence": "TaskUs provides repeatable delivery operations for data annotation, model review and physical-AI support. The AI Services business is the relevant scale measure for this sublayer; company-wide revenue and employee counts include substantial unrelated outsourcing work.", "sourceIds": [ "taskus:tu-ai", "taskus:tu-proxy26", "taskus:tu-q2-26", "taskus:tu-q2-deck" ], "packetSha256": "fbe9d3fafe742cbef3a1b4441d5844d6dc8611127f7317fab95acb5249ca3bde", "sourceReviewFile": "models/reviews/taskus.json", "sourceReviewHashMatched": true, "currentPacketHomeSublayer": "data-evaluation", "currentSummaryEvidence": { "tagline": "AI data and evaluation within a digital-services operator", "description": "TaskUs supplies human training data, model evaluation and safety work alongside customer support and content moderation. Its AI Services business generated approximately $244 million in the twelve months to June 2026, around one-fifth of company revenue. Its other service lines are Digital Customer Experience and Trust & Safety. The company remains Nasdaq-listed and has a dual-class voting structure.", "whyItMatters": "TaskUs provides repeatable delivery operations for data annotation, model review and physical-AI support. The AI Services business is the relevant scale measure for this sublayer; company-wide revenue and employee counts include substantial unrelated outsourcing work.", "evidence": "company_stated", "sourceIds": [ "taskus:tu-q2-deck", "taskus:tu-ai", "taskus:tu-q2-26", "taskus:tu-proxy26" ] } }, { "entityId": "telus-digital", "name": "TELUS Digital", "economicParent": "telus", "eligible": true, "functions": [ "expert-posttraining", "multimodal-data" ], "roleEvidence": "Its role combines multilingual human data with automotive and robotics field collection. Whole-company outsourcing sales, Fuel iX token counts and planned video collection are separate measures; none substitutes for delivered AI-data revenue.", "sourceIds": [ "telus-digital:td-acq", "telus-digital:td-ai-data", "telus-digital:td-fuelix", "telus-digital:td-nelsonhall", "telus-digital:td-physical" ], "packetSha256": "950a6971dffa3fe0385394fc0fc45eb955b1d9607f259cca2515e56bbe3981fd", "sourceReviewFile": "models/reviews/telus-digital.json", "sourceReviewHashMatched": true, "currentPacketHomeSublayer": "data-evaluation", "currentSummaryEvidence": { "tagline": "Training data, evaluation and enterprise AI within TELUS", "description": "TELUS Digital collects and annotates training data, operates expert evaluation workflows and builds AI-enabled customer-service systems. TELUS acquired its remaining minority shares in October 2025. The AI data business reported about 77 clients and 115,000 active contributors in August 2026, within a larger registered community. Its revenue is not separately disclosed in the inspected current materials.", "whyItMatters": "Its role combines multilingual human data with automotive and robotics field collection. Whole-company outsourcing sales, Fuel iX token counts and planned video collection are separate measures; none substitutes for delivered AI-data revenue.", "evidence": "company_stated", "sourceIds": [ "telus-digital:td-acq", "telus-digital:td-nelsonhall", "telus-digital:td-ai-data", "telus-digital:td-physical", "telus-digital:td-fuelix" ] } }, { "entityId": "turing", "name": "Turing", "economicParent": "turing", "eligible": true, "functions": [ "expert-posttraining", "evaluation-environments" ], "roleEvidence": "Turing links model-training work to enterprise deployment experience. Current products and case studies establish commercial activity, while benchmark task counts and registered experts do not establish current sales or market share.", "sourceIds": [ "turing:tu-bordoli", "turing:tu-cases", "turing:tu-company", "turing:tu-frontier" ], "packetSha256": "f2f3dd68443de90aca4a15c40b82762834637839d9e9182eca07460d15e950ad", "sourceReviewFile": "models/reviews/turing.json", "sourceReviewHashMatched": true, "currentPacketHomeSublayer": "data-evaluation", "currentSummaryEvidence": { "tagline": "Expert training data, reinforcement-learning environments and enterprise AI", "description": "Turing supplies coding, scientific and enterprise-work data to model laboratories and builds agent systems for enterprise customers. It combines a recruited expert network with reusable tasks, reinforcement-learning environments and public evaluations. Its current site claims a network of more than five million experts; the active paid subset is not disclosed. The latest inspected revenue run-rate remains a historical March2025 figure.", "whyItMatters": "Turing links model-training work to enterprise deployment experience. Current products and case studies establish commercial activity, while benchmark task counts and registered experts do not establish current sales or market share.", "evidence": "company_stated", "sourceIds": [ "turing:tu-company", "turing:tu-bordoli", "turing:tu-frontier", "turing:tu-cases" ] } }, { "entityId": "arena-ai", "name": "Arena", "economicParent": "arena-ai", "eligible": true, "functions": [ "independent-preference-evaluation", "evaluation-environments" ], "roleEvidence": "Model developers need evidence about behavior outside fixed tests. Arena supplies preference and task evidence across competing models. Its commercial evaluation business and community participation support this role; leaderboard scores do not themselves measure a model vendor’s market share.", "sourceIds": [ "arena-ai:about", "arena-ai:alignment", "arena-ai:b", "arena-ai:method" ], "packetSha256": "9419f74ee19c0783c40c2496a15748961b3e99cfb86a49d7fc930bae032bef5c", "sourceReviewFile": "models/reviews/arena-ai.json", "sourceReviewHashMatched": true, "currentPacketHomeSublayer": "data-evaluation", "currentSummaryEvidence": { "tagline": "Human-preference data and commercial model evaluation", "description": "Arena converts user comparisons of AI models into evaluation data, public leaderboards and commercial analytics. The company grew out of Chatbot Arena and was incorporated in 2025. It reported more than $100 million annualized revenue in October 2026. Agent Arena extends the approach to longer tasks, while an alignment index examines failures in real user sessions.", "whyItMatters": "Model developers need evidence about behavior outside fixed tests. Arena supplies preference and task evidence across competing models. Its commercial evaluation business and community participation support this role; leaderboard scores do not themselves measure a model vendor’s market share.", "evidence": "company_stated", "sourceIds": [ "arena-ai:about", "arena-ai:b", "arena-ai:method", "arena-ai:alignment" ] } }, { "entityId": "innodata", "name": "Innodata", "economicParent": "innodata", "eligible": true, "functions": [ "expert-posttraining", "multimodal-data" ], "roleEvidence": "AI data engineering drives current growth; group financials also include medical and communications software.", "sourceIds": [ "innodata:q2", "innodata:filing" ] }, { "entityId": "labelbox", "name": "Labelbox", "economicParent": "labelbox", "eligible": true, "functions": [ "expert-posttraining", "data-software", "evaluation-environments" ], "roleEvidence": "Current data platform, Alignerr experts, RL environments and operational-data licensing; relevant revenue undisclosed.", "sourceIds": [ "labelbox:e", "labelbox:prism" ] }, { "entityId": "toloka", "name": "Toloka", "economicParent": "toloka", "eligible": true, "functions": [ "expert-posttraining", "multimodal-data", "evaluation-environments" ], "roleEvidence": "Domain-specialist contributors, training data and RL/evaluation offerings. Mindrift is the recruiting platform, not another supplier.", "sourceIds": [ "toloka:home" ] }, { "entityId": "invisible-technologies", "name": "Invisible", "economicParent": "invisible-technologies", "eligible": true, "functions": [ "expert-posttraining", "multimodal-data", "evaluation-environments", "data-software" ], "roleEvidence": "Expert-led data and enterprise AI platform; public company revenue is not separated into data and deployment services.", "sourceIds": [ "invisible:home" ] }, { "entityId": "imerit", "name": "EXL / iMerit", "economicParent": "exl", "eligible": true, "functions": [ "expert-posttraining", "multimodal-data", "evaluation-environments" ], "roleEvidence": "Acquisition completed August 3; iMerit remains a named operating business within EXL.", "sourceIds": [ "exl:imerit", "imerit:home" ] }, { "entityId": "prolific", "name": "Prolific", "economicParent": "prolific", "eligible": true, "functions": [ "expert-posttraining", "evaluation-environments" ], "roleEvidence": "Commercial human-participant research platform with AI-specific expert tasks; aggregate volume includes academic and other research.", "sourceIds": [ "prolific:2025", "prolific:pool" ] }, { "entityId": "braintrust", "name": "Braintrust", "economicParent": "braintrust", "eligible": true, "functions": [ "data-software", "evaluation-environments" ], "roleEvidence": "Paid evaluation/observability software with production customer cases. 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"innodata:q2" ] }, { "name": "TaskUs AI share of reconstructed group TTM", "formula": "244 / (1183.547 + 615.121 - 571.878) * 100", "value": 19.889304607960614, "unit": "percent", "period": "TTM June 2026", "sourceIds": [ "taskus:tu-q2-26", "taskus:tu-fy25", "taskus:tu-q2-deck" ] } ], "selectedIds": [ "mercor", "surge-ai", "handshake-ai", "micro1", "scale-ai", "snorkel-ai", "telus-digital", "taskus", "appen", "arena-ai" ], "selectedReasons": { "mercor": "Large disclosed current gross expert-data run rate and expert payouts; actual work, not financing.", "surge-ai": "Large reported historical data revenue and fresh named-lab evaluation work; current period-undefined $3B reference is not scored.", "handshake-ai": "Disclosed gross run rate, paid fellow activity and current lab services.", "micro1": "Current company revenue disclosure and commercial expert/RL data workflows; gross/net ambiguity remains explicit.", "scale-ai": "Delivered robotics data plus broad evaluation and enterprise-data operations. Bookings are not revenue and Meta minority ownership is not independent parent duplication.", "snorkel-ai": "Current revenue disclosure alongside programmatic data software and expert data services.", "arena-ai": "Adds a distinct paid cross-provider community-evaluation function missing from the prior set.", "telus-digital": "Large explicitly active contributor base and delivered-label scale. Unknown AI revenue does not exclude it.", "taskus": "Material separately disclosed AI-service revenue and managed production capacity; whole BPO revenue excluded.", "appen": "Substantial current training-data business, including disclosed Chinese demand and multilingual production; traditional AI training remains in scope." }, "functionCoverage": { "expert-posttraining": [ "appen", "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "taskus", "telus-digital" ], "multimodal-data": [ "appen", "scale-ai", "taskus", "telus-digital" ], "data-software": [ "snorkel-ai" ], "evaluation-environments": [ "arena-ai", "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai" ], "independent-preference-evaluation": [ "arena-ai" ] }, "alternativeCutoffSets": [ { "name": "frontier-expert emphasis", "remove": [ "appen" ], "add": [ "turing" ], "assumption": "Current broad lab coverage is given priority over China/multilingual production representation; no imputed revenue.", "selectedIds": [ "mercor", "surge-ai", "handshake-ai", "micro1", "scale-ai", "snorkel-ai", "telus-digital", "taskus", "arena-ai", "turing" ], "retained": 9, "jaccard": 0.8181818181818182, "allFunctionsCovered": true, "functionCoverage": { "expert-posttraining": [ "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "taskus", "telus-digital", "turing" ], "multimodal-data": [ "scale-ai", "taskus", "telus-digital" ], "data-software": [ "snorkel-ai" ], "evaluation-environments": [ "arena-ai", "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "turing" ], "independent-preference-evaluation": [ "arena-ai" ] } }, { "name": "software/data-licensing emphasis", "remove": [ "taskus" ], "add": [ "labelbox" ], "assumption": "Platform customers and operational-data licensing warrant an extra software representative.", "selectedIds": [ "mercor", "surge-ai", "handshake-ai", "micro1", "scale-ai", "snorkel-ai", "telus-digital", "appen", "arena-ai", "labelbox" ], "retained": 9, "jaccard": 0.8181818181818182, "allFunctionsCovered": true, "functionCoverage": { "expert-posttraining": [ "appen", "handshake-ai", "labelbox", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "telus-digital" ], "multimodal-data": [ "appen", "scale-ai", "telus-digital" ], "data-software": [ "labelbox", "snorkel-ai" ], "evaluation-environments": [ "arena-ai", "handshake-ai", "labelbox", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai" ], "independent-preference-evaluation": [ "arena-ai" ] } }, { "name": "industrial AI-growth emphasis", "remove": [ "taskus" ], "add": [ "innodata" ], "assumption": "AI-driven growth and customer concentration justify inclusion despite undisclosed relevant revenue share.", "selectedIds": [ "mercor", "surge-ai", "handshake-ai", "micro1", "scale-ai", "snorkel-ai", "telus-digital", "appen", "arena-ai", "innodata" ], "retained": 9, "jaccard": 0.8181818181818182, "allFunctionsCovered": true, "functionCoverage": { "expert-posttraining": [ "appen", "handshake-ai", "innodata", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "telus-digital" ], "multimodal-data": [ "appen", "innodata", "scale-ai", "telus-digital" ], "data-software": [ "snorkel-ai" ], "evaluation-environments": [ "arena-ai", "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai" ], "independent-preference-evaluation": [ "arena-ai" ] } }, { "name": "evaluation-environment emphasis", "remove": [ "appen" ], "add": [ "toloka" ], "assumption": "Active expert evaluation and RL environments are emphasized over legacy/multilingual data.", "selectedIds": [ "mercor", "surge-ai", "handshake-ai", "micro1", "scale-ai", "snorkel-ai", "telus-digital", "taskus", "arena-ai", "toloka" ], "retained": 9, "jaccard": 0.8181818181818182, "allFunctionsCovered": true, "functionCoverage": { "expert-posttraining": [ "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "taskus", "telus-digital", "toloka" ], "multimodal-data": [ "scale-ai", "taskus", "telus-digital", "toloka" ], "data-software": [ "snorkel-ai" ], "evaluation-environments": [ "arena-ai", "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "toloka" ], "independent-preference-evaluation": [ "arena-ai" ] } }, { "name": "unknown-private-scale challenge", "remove": [ "taskus", "appen" ], "add": [ "invisible-technologies", "imerit" ], "assumption": "Two commercially active suppliers with unknown comparable revenue occupy the disputed production seats; unknown is never converted to zero.", "selectedIds": [ "mercor", "surge-ai", "handshake-ai", "micro1", "scale-ai", "snorkel-ai", "telus-digital", "arena-ai", "invisible-technologies", "imerit" ], "retained": 8, "jaccard": 0.6666666666666666, "allFunctionsCovered": true, "functionCoverage": { "expert-posttraining": [ "handshake-ai", "imerit", "invisible-technologies", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "telus-digital" ], "multimodal-data": [ "imerit", "invisible-technologies", "scale-ai", "telus-digital" ], "data-software": [ "invisible-technologies", "snorkel-ai" ], "evaluation-environments": [ "arena-ai", "handshake-ai", "imerit", "invisible-technologies", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai" ], "independent-preference-evaluation": [ "arena-ai" ] } }, { "name": "China direct-supplier emphasis", "remove": [ "appen" ], "add": [ "dataocean-ai" ], "assumption": "Domestic listed training-data supplier substitutes for Appen; no geographic quota or cross-currency ranking is imposed.", "selectedIds": [ "mercor", "surge-ai", "handshake-ai", "micro1", "scale-ai", "snorkel-ai", "telus-digital", "taskus", "arena-ai", "dataocean-ai" ], "retained": 9, "jaccard": 0.8181818181818182, "allFunctionsCovered": true, "functionCoverage": { "expert-posttraining": [ "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "taskus", "telus-digital" ], "multimodal-data": [ "dataocean-ai", "scale-ai", "taskus", "telus-digital" ], "data-software": [ "snorkel-ai" ], "evaluation-environments": [ "arena-ai", "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai" ], "independent-preference-evaluation": [ "arena-ai" ] } } ], "sensitivity": { "stableWithinDeclaredScenarios": [ "arena-ai", "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "telus-digital" ], "interpretation": "Scenario survival is not a probability or statistical confidence interval. The scenarios vary editorial function emphasis and unknown-data treatment, not measured facts.", "weightTests": "No numeric weights fitted because inputs do not support an additive score. Reweighting functions is represented by the explicit alternative sets; all retain required function coverage.", "estimateTests": "Removing period-undefined Surge revenue and using micro1 August gross/net range does not erase documented current commercial roles. It prevents a precise financial ordering.", "denominatorTests": "Registered workers, active contributors, sessions and annual labels remain separate. No summed global revenue denominator or coverage percentage is reported.", "universeTests": "Expanded universe introduces Arena and viable alternatives Labelbox, Innodata, Toloka, Invisible, EXL/iMerit, DataOcean, Prolific, Braintrust and Uber. Argilla eligibility remains qualified.", "scopeRelaxationScenarios": [ { "name": "Broader commercial evaluation scope", "classification": "Supplemental scope-relaxation test; not a change to the base recommendation", "removed": [ "arena-ai" ], "added": [ "labelbox" ], "selectedIds": [ "mercor", "surge-ai", "handshake-ai", "micro1", "scale-ai", "snorkel-ai", "telus-digital", "taskus", "appen", "labelbox" ], "requiredFunctions": [ "expert-posttraining", "multimodal-data", "data-software", "evaluation-environments" ], "functionCoverage": { "expert-posttraining": [ "appen", "handshake-ai", "labelbox", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "taskus", "telus-digital" ], "multimodal-data": [ "appen", "scale-ai", "taskus", "telus-digital" ], "data-software": [ "labelbox", "snorkel-ai" ], "evaluation-environments": [ "handshake-ai", "labelbox", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai" ] }, "relaxedRequirement": "A standalone independent community-preference evaluation venue is no longer mandatory; commercial evaluation environments remain required.", "assumption": "Treat preference evaluation as one type of commercial evaluation rather than a separate compulsory publishing category. Labelbox already has inspected commercial evaluation, data-software and expert-workflow evidence.", "sourceIds": [ "labelbox:e", "labelbox:prism" ], "allRequiredFunctionsCovered": true, "limitation": "This is an alternative editorial scope. It does not measure Labelbox as economically larger than Arena. A full current Labelbox dossier would be required before making this alternative the published selection." } ], "constraintForcedMembership": { "function": "independent-preference-evaluation", "soleCandidate": "arena-ai", "explanation": "Arena is mandatory within the base functional definition because it is the only universe candidate assigned this narrowly specified function. Its survival in the original scenarios follows from that editorial minimum, not independent quantitative evidence that it must be in every defensible Top 10." }, "stableIncludingScopeRelaxation": [ "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "telus-digital" ] }, "changesVersusPrior": { "added": [ "arena-ai" ], "removed": [ "turing" ], "retained": [ "appen", "handshake-ai", "mercor", "micro1", "scale-ai", "snorkel-ai", "surge-ai", "taskus", "telus-digital" ], "removedDisposition": "Turing remains fully researched in the directory and in the frontier-expert alternative set; exclusion is a space/coverage choice, not evidence of commercial weakness." }, "limitations": [ "This is not an economically identified global ordinal Top 10. At least two production positions remain materially uncertain.", "The candidate search is deliberately broader than the prior roster but cannot establish an exhaustive private-company universe.", "Company-reported customer and contributor counters use different definitions; third-party verification is incomplete.", "AI-only revenue is unavailable for several large suppliers, including TELUS, Labelbox, Innodata and EXL/iMerit. Their importance is not assigned a fabricated point estimate." ], "refresh": { "daily": "Monitor all candidates and selected suppliers for material changes; no automatic daily reranking.", "eventTriggers": [ "Closed change of control", "New actual paid product or service", "Latest relevant revenue or delivered-activity disclosure", "Material contract loss or product exit", "Changed metric definitions or evidence correction" ], "cutoff": "Recompute after material evidence and at quarterly review; disclose alternatives if uncertainty overlaps. Ordinary close-call changes require evidence sustained across reviews; factual eligibility or ownership errors are corrected immediately." }, "packetCalculations": [ { "entityId": "appen", "calculation": { "name": "China revenue share", "formula": "76.2/119.9*100", "result": 63.55296080066722, "unit": "percent" }, "sourceReviewFile": "models/reviews/appen.json", "sourceReviewCalculationIndex": 0, "packetSha256": "bc22fa066a26c4ee0180694b0be25c04505c2ce6ddccfc829eceefee87a128e0", "selectionUse": "Audit trail for disclosed facts; not an additive importance score." }, { "entityId": "appen", "calculation": { "name": "Global revenue share", "formula": "43.7/119.9*100", "result": 36.44703919933278, "unit": "percent" }, "sourceReviewFile": "models/reviews/appen.json", "sourceReviewCalculationIndex": 1, "packetSha256": "bc22fa066a26c4ee0180694b0be25c04505c2ce6ddccfc829eceefee87a128e0", "selectionUse": "Audit trail for disclosed facts; not an additive importance score." }, { "entityId": "appen", "calculation": { "name": "FY2025 reported growth", "formula": "(230.8/234.3-1)*100", "result": -1.4938113529662789, "unit": "percent" }, "sourceReviewFile": "models/reviews/appen.json", "sourceReviewCalculationIndex": 2, "packetSha256": "bc22fa066a26c4ee0180694b0be25c04505c2ce6ddccfc829eceefee87a128e0", "selectionUse": "Audit trail for disclosed facts; 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not an additive importance score." }, { "entityId": "micro1", "calculation": { "name": "Implied net share in August report", "formula": "150/500 to 200/500", "result": [ 0.3, 0.4 ], "meaning": "Shows inconsistency with 60–70% retention wording; not an accepted margin estimate." }, "sourceReviewFile": "models/reviews/micro1.json", "sourceReviewCalculationIndex": 0, "packetSha256": "4bdbac108607d91c3778978ef8bfd3cc8f11d9f50eb1870813b9335a95b8cf05", "selectionUse": "Audit trail for disclosed facts; not an additive importance score." }, { "entityId": "taskus", "calculation": { "name": "Company TTM revenue crosscheck", "formula": "1183.547 + 615.121 - 571.878", "result": 1226.79, "unit": "USD millions", "note": "FY2025 plus H12026 less H12025; deck total rounds to1227M." }, "sourceReviewFile": "models/reviews/taskus.json", "sourceReviewCalculationIndex": 0, "packetSha256": "fbe9d3fafe742cbef3a1b4441d5844d6dc8611127f7317fab95acb5249ca3bde", "selectionUse": "Audit trail for disclosed facts; not an additive importance score." }, { "entityId": "taskus", "calculation": { "name": "AI mix crosscheck", "formula": "244 / 1226.79 * 100", "result": 19.889304607960614, "unit": "percent", "note": "19.89% consistent with rounded20%; direct244M is authoritative." }, "sourceReviewFile": "models/reviews/taskus.json", "sourceReviewCalculationIndex": 1, "packetSha256": "fbe9d3fafe742cbef3a1b4441d5844d6dc8611127f7317fab95acb5249ca3bde", "selectionUse": "Audit trail for disclosed facts; not an additive importance score." }, { "entityId": "taskus", "calculation": { "name": "Outstanding shares", "formula": "35528558 + 55032694", "result": 90561252, "unit": "shares", "note": "March1 total; founder beneficial ownership includes additional rights so no naive equity percentage is computed." }, "sourceReviewFile": "models/reviews/taskus.json", "sourceReviewCalculationIndex": 2, "packetSha256": "fbe9d3fafe742cbef3a1b4441d5844d6dc8611127f7317fab95acb5249ca3bde", "selectionUse": "Audit trail for disclosed facts; not an additive importance score." } ], "relationshipEvidence": [ { "entityId": "appen", "relation": "customers", "evidence": { "name": "Model developers in China and Global markets", "entityId": null, "detail": "Management describes new and expanding model-development projects, without naming current customers or disclosing individual contract values.", "status": "current", "evidence": "company_stated", "sourceIds": [ "appen:apx-h1-call" ] }, "packetSha256": "bc22fa066a26c4ee0180694b0be25c04505c2ce6ddccfc829eceefee87a128e0", "selectionUse": "Role and delivery context only. Reciprocal supplier/customer descriptions are the same relationship, not independent measured activity." }, { "entityId": "appen", "relation": "customers", "evidence": { "name": "Google", "entityId": "alphabet", "detail": "Historical contract termination in 2024 is explicitly excluded from management’s adjusted FY2025 growth comparison.", "status": "historical", "evidence": "filing", "sourceIds": [ "appen:apx-fy25-release" ] }, "packetSha256": "bc22fa066a26c4ee0180694b0be25c04505c2ce6ddccfc829eceefee87a128e0", "selectionUse": "Role and delivery context only. Reciprocal supplier/customer descriptions are the same relationship, not independent measured activity." }, { "entityId": "appen", "relation": "customers", "evidence": { "name": "Historical enterprise and technology clients", "entityId": null, "detail": "The annual report names Microsoft, Amazon, Pinterest, Salesforce, Oracle and Adobe in its history since 1996. It does not establish that every relationship remains current.", "status": "historical", "evidence": "filing", "sourceIds": [ "appen:apx-ar25" ] }, "packetSha256": "bc22fa066a26c4ee0180694b0be25c04505c2ce6ddccfc829eceefee87a128e0", "selectionUse": "Role and delivery context only. Reciprocal supplier/customer descriptions are the same relationship, not independent measured activity." }, { "entityId": "appen", "relation": "suppliers", "evidence": { "name": "Contributor network", "entityId": null, "detail": "Current website describes 1M+ vetted contributors across 170+ countries and 235+ languages; the annual report describes a broader self-reported crowd across 200+ countries/territories and 500+ languages. Active paid participation is undisclosed.", "status": "current", "evidence": "company_stated", "sourceIds": [ "appen:apx-about", "appen:apx-ar25" ] }, "packetSha256": "bc22fa066a26c4ee0180694b0be25c04505c2ce6ddccfc829eceefee87a128e0", "selectionUse": "Role and delivery context only. Reciprocal supplier/customer descriptions are the same relationship, not independent measured activity." }, { "entityId": "arena-ai", "relation": "customers", "evidence": { "name": "OpenAI", "entityId": "openai", "detail": "Felicis identifies the lab as using Arena evaluation data. This supports a use relationship, not a disclosed contract amount or proof that every model submission is paid.", "status": "current", "evidence": "company_stated", "sourceIds": [ "arena-ai:felicis" ] }, "packetSha256": "9419f74ee19c0783c40c2496a15748961b3e99cfb86a49d7fc930bae032bef5c", "selectionUse": "Role and delivery context only. Reciprocal supplier/customer descriptions are the same relationship, not independent measured activity." }, { "entityId": "arena-ai", "relation": "customers", "evidence": { "name": "Google", "entityId": "alphabet", "detail": "Felicis identifies the lab as using Arena evaluation data. This supports a use relationship, not a disclosed contract amount or proof that every model submission is paid.", "status": "current", "evidence": "company_stated", "sourceIds": [ "arena-ai:felicis" ] }, "packetSha256": "9419f74ee19c0783c40c2496a15748961b3e99cfb86a49d7fc930bae032bef5c", "selectionUse": "Role and delivery context only. Reciprocal supplier/customer descriptions are the same relationship, not independent measured activity." }, { "entityId": "arena-ai", "relation": "customers", "evidence": { "name": "Anthropic", "entityId": "anthropic", "detail": "Felicis identifies the lab as using Arena evaluation data. This supports a use relationship, not a disclosed contract amount or proof that every model submission is paid.", "status": "current", "evidence": "company_stated", "sourceIds": [ "arena-ai:felicis" ] }, "packetSha256": "9419f74ee19c0783c40c2496a15748961b3e99cfb86a49d7fc930bae032bef5c", "selectionUse": "Role and delivery context only. Reciprocal supplier/customer descriptions are the same relationship, not independent measured activity." }, { "entityId": "arena-ai", "relation": "suppliers", "evidence": { "name": "External model providers", "entityId": null, "detail": "The privacy policy confirms third-party language, image and other models process submitted content. Supplier-level spend, donated capacity and hosting contracts are undisclosed.", "status": "current", "evidence": "company_stated", "sourceIds": [ "arena-ai:privacy" ] }, "packetSha256": "9419f74ee19c0783c40c2496a15748961b3e99cfb86a49d7fc930bae032bef5c", "selectionUse": "Role and delivery context only. Reciprocal supplier/customer descriptions are the same relationship, not independent measured activity." }, { "entityId": "arena-ai", "relation": "partners", "evidence": { "name": "Hugging Face", "entityId": "hugging-face", "detail": "Arena links its public evaluation datasets on the Hub. 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