[{"data":1,"prerenderedAt":647},["ShallowReactive",2],{"uc-audio-and-video-transcription-and-captioning":3,"uc-regulations":436},{"useCase":4,"evidence":199,"blitsAiDeployments":327,"benchmarks":328,"indicative":345,"related":348,"indexability":434,"includeUnpublished":205},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":24,"channels":28,"audience":31,"autonomy":32,"adoptionStage":33,"problem":34,"problemStats":35,"howItWorks":41,"valueDrivers":42,"kpis":48,"indicativeValue":54,"macroEstimates":82,"feasibility":83,"implementation":96,"risk":142,"blitsAi":173,"faq":175,"related":188,"datePublished":193,"dateModified":193,"lastVerified":194,"changelog":195,"slug":198},"AI transcription, subtitles and captions for audio and video","Transcription and captioning","AI transcription and captions for audio and video","AI turns audio and video into timed transcripts, captions and subtitles for an editor to check. Warner Bros. Discovery cut the time to caption a file by 80%.","published","AI that transcribes recorded audio and video, such as podcasts, broadcasts, lessons, interviews and hearings, in several languages, separates the speakers and produces timed transcripts, subtitles and captions for a human editor to check, delivered as files for publishing or the archive.",[12,13,14,15,16],"AI captioning","automatic subtitling","AI subtitle generation","automated closed captions","AI media transcription",[18,19,20],"media-and-entertainment","education","cross-industry",[22,23],"operations","knowledge-management",[25,26,27],"speech-analytics","translation","content-generation",[29,30],"api","internal-tools","back-office","copilot","mainstream","Every organization that publishes audio or video has the same backlog: captions for people who\nare deaf or hard of hearing, subtitles for viewers who speak another language, and searchable\ntranscripts for the archive. Done by hand, captioning one hour of content takes many hours of\ntranscription, timing and translation. Ateme describes up to 15 hours of manual work per hour of\nvideo per language, and external providers that were slow and hard to scale. So organizations\ncaption the flagship content and leave the rest, or subtitle into one language only.\n\nThe gap is growing as content volumes grow and accessibility rules tighten. SVT, Sweden's public\nbroadcaster, says it simply could not caption its local news without automation, because it\npublishes for 21 regional stations several times a day. Speech recognition is now accurate enough\nthat the human role shifts from typing to editing: the machine produces a timed draft with\nspeakers marked, and an editor fixes names, terms and meaning before publication.",[36],{"statement":37,"sourceTitle":38,"sourceUrl":39,"year":40},"The World Health Organization estimates that over 5% of the world's population, or 430 million people, require rehabilitation for disabling hearing loss.","Deafness and hearing loss","https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss",2026,"1. **Ingest the file.** A new recording arrives from the media asset system, learning platform,\n   podcast host or court recording system, and a job starts automatically.\n2. **Transcribe with timings.** Speech recognition produces text with word level timestamps and\n   detects the spoken language, using a custom vocabulary of names and terms. Pacers Sports &\n   Entertainment reduced its transcription error rate by 87% by tuning the model to its own\n   broadcasts.\n3. **Separate speakers.** Diarization marks who spoke when, so transcripts read as a dialogue and\n   captions can show speaker changes.\n4. **Translate and segment.** The text is translated into the target languages and cut into\n   subtitle lines that respect reading speed, line length and shot changes.\n5. **Edit and approve.** An editor reviews the draft in a subtitle editor, corrects names, numbers\n   and meaning, and approves each language before release.\n6. **Deliver files.** The system exports standard formats such as SRT and WebVTT captions and a\n   plain transcript, and returns them to the publishing platform and the archive.\n\nLive captioning, as on arena screens or live broadcasts, uses the same speech models but removes\nthe editor, so accuracy tuning and filters matter even more.",[43,44,45,46,47],"cost-to-serve","speed","inclusion-and-access","compliance","employee-productivity",[49,50,51,52,53],"cost-reduction","processing-time-reduction","error-reduction","accuracy","hours-saved",{"referenceOrg":55,"inputs":56,"formula":77,"currency":78,"period":79,"resultLabel":80,"caveat":81},"A regional broadcaster or publisher that captions 5,000 hours of content a year",[57,63,70],{"key":58,"label":59,"low":60,"high":60,"unit":61,"note":62},"hours","Hours of content captioned per year",5000,"hours of content per year","The reference organization.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"costPerHour","Current cost of captioning one hour of content",150,600,"USD per hour of content","Editorial assumption covering in house or outsourced transcription, timing and review in one language. Replace with your own rates.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"costReduction","Share of captioning cost removed",0.3,0.5,"fraction of cost","Conservative against the benchmark on this page (Google Cloud reports a 50% reduction in overall costs for Warner Bros. Discovery's AI captioning tool), because editing time remains.","hours * costPerHour * costReduction","USD","per year","Captioning cost avoided","Covers the existing captioning volume only. It leaves out the cost of running the speech models and the editing tool, the value of content that is captioned or subtitled for the first time, extra languages, and the audience and compliance benefits.",[],{"complexity":84,"complexityNote":85,"dataPrerequisites":86,"integrations":91},"low","Speech recognition, diarization and machine translation are mature and available from many providers. The work is in the workflow around them: getting files in and out of the media or learning platform, a glossary of names and terms, an editing step that editors actually like, and output formats that the players accept.",[87,88,89,90],"Access to the source audio or video files, in a quality good enough for speech recognition","A glossary of names, places, brands and technical terms per programme, course or court","House style for captions and subtitles (line length, reading speed, speaker labels, sound descriptions)","A sample of manually captioned content to measure accuracy before and after",[92,93,94,95],"Media asset management system, learning platform, podcast host or recording system","Subtitle or transcript editor for human review","Publishing platform or video player that accepts SRT or WebVTT files","Archive or search index for transcripts",{"steps":97,"guardrails":116,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":129},[98,101,104,107,110,113],{"title":99,"detail":100},"Measure your baseline first","Take a sample of content across programme types, speakers and languages, caption it the current way and record the time and cost. Without this baseline, no one can say whether the AI draft saves time once editing is counted.",{"title":102,"detail":103},"Compare speech engines on your own audio","Run the same sample through several speech recognition engines and compare word error rate, especially on names, accents, dialects and overlapping speech. Results on vendor benchmarks rarely match results on your material.",{"title":105,"detail":106},"Build the glossary and tune","Feed names and domain terms to the engine as custom vocabulary or a correction step. Pacers Sports & Entertainment tuned its model with its own broadcasts and name lists and pushed the error rate far below its original target.",{"title":108,"detail":109},"Put the editor at the centre","Give editors a tool that shows the draft with timings, speaker labels and low confidence words highlighted, and measure editing time per hour of content. The editor, not the model, signs off what is published.",{"title":111,"detail":112},"Add languages one at a time","Start with same language captions, then add subtitle languages where audience data shows demand, each with a native speaker review of a sample before going live.",{"title":114,"detail":115},"Automate delivery","Trigger jobs when files arrive and return approved SRT, WebVTT and transcript files to the publishing platform and archive automatically, so nothing depends on manual uploads.",[117,118,119,120,121],"A human editor approves every caption and subtitle file before publication","Custom vocabulary and a correction step for names, places and terms","Low confidence words and segments flagged for the editor, not silently published","Profanity and sensitive word checks on output, especially for content aimed at children","Recordings and transcripts of identifiable people processed and stored under the organization's retention rules","Editors review and correct every file before it is published, with extra attention to names, numbers, quotes and anything legally sensitive. For translated subtitles, a native speaker checks a sample per language and programme type. For live captioning, where no editor can intervene, a producer monitors the output and can switch captions off.",[124,125,126,127,128],"Word error rate per programme type, language and speaker profile, on a monthly sample","Editing time per hour of content, compared with the manual baseline","Cost per hour of captioned content, including model and editing costs","Share of published content with captions and with subtitles, per language","Errors reported by viewers or learners after publication",[130,133,136,139],{"title":131,"detail":132},"Offensive or embarrassing misrecognition","The engine hears an innocent word as an offensive one, and it reaches the screen. Prevent with sensitive word checks and editor review, above all for children's content.",{"title":134,"detail":135},"Names and terms mangled","People, places and technical terms are transcribed wrongly, which undermines trust and can be defamatory. Maintain a glossary per programme and check names first in review.",{"title":137,"detail":138},"Review that becomes a rubber stamp","Editors under time pressure approve drafts without reading them. Track editing time and sample published files for errors.",{"title":140,"detail":141},"Captions that do not fit the screen","Correct text that is badly timed or too long to read. Enforce reading speed and line length rules in the segmentation step.",{"euAiAct":143,"regulations":146,"guidance":150,"controls":162,"incidents":168},{"tier":144,"basis":145},"context-dependent","Transcription and captioning are not listed in Annex III and are not a prohibited practice under Article 5, so the tier depends on how captions are published. Article 50(4) requires deployers to disclose AI generated or manipulated text published to inform the public on matters of public interest, such as news captions, unless it has undergone human review or editorial control and someone holds editorial responsibility, so the editor step keeps most deployments outside this duty. The provider duty to mark output in Article 50(2) does not apply where the system does not substantially alter the input or its semantics, which fits same language transcription better than translated subtitles. Unreviewed news captions or subtitles should therefore be disclosed as automatic.",[147,148,149],"eu-ai-act","gdpr","eu-accessibility-act",[151,157],{"title":152,"issuer":153,"region":154,"url":155,"note":156},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","Paragraph 4 sets the disclosure duty for AI generated text published to inform the public on matters of public interest and exempts content under human review or editorial control; paragraph 2 exempts systems that do not substantially alter the input or its semantics.",{"title":158,"issuer":159,"region":154,"url":160,"note":161},"Guidelines 02/2021 on virtual voice assistants","European Data Protection Board","https://www.edpb.europa.eu/our-work-tools/our-documents/guidelines/guidelines-022021-virtual-voice-assistants_en","Final version of July 2021 on processing voice data in voice assistants; its analysis of voice recordings as personal data, and when they become biometric data, also applies to recordings and transcripts of identifiable speakers.",[163,164,165,166,167],"Documented editorial sign off per published file","Glossary and custom vocabulary with an owner per programme or course","Retention and access rules for recordings and transcripts of identifiable people","Monthly accuracy sample with word error rate reported per language","Labelling of automatic captions where no human review takes place",[169],{"title":170,"url":171,"note":172},"YouTube's Captions Insert Explicit Language in Kids' Videos","https://www.wired.com/story/youtubes-captions-insert-explicit-language-kids-videos/","Researchers found that automatic captions on videos from top children's channels contained inappropriate words the speakers never said, a risk for any unreviewed captioning.",{"howToBuild":174},"On Blits.ai this is an **agentic workflow** triggered through an API token or on a schedule\nwhen a new file arrives. The first step uses the **self hosted transcription and speaker\ndiarization** option, with word level timestamps and automatic language detection, processed on\nBlits.ai infrastructure, or one of the other **speech to text** providers the platform supports; the\n**speech to text quality check** compares providers on word error rate with your own samples. An\nagent step corrects names and terms against a glossary in the **knowledge base**, and **machine\ntranslation** or the chosen model produces the subtitle languages.\n\nA **custom function** in the JavaScript sandbox turns the timed segments into SRT or WebVTT, and\nthe **file generation** tool returns the files. **Human in the loop confirmation** holds each\nfile until an editor approves it, and the run history keeps a full audit trail with downloadable\nrun data. **PII masking** at the gateway can redact personal data before text reaches an external model, the platform is **model\nagnostic**, and **EU and UAE data residency** keeps recordings of identifiable people in region.",[176,179,182,185],{"question":177,"answer":178},"How much does AI captioning save?","Published results are large but come from vendors. Google Cloud reports that Warner Bros. Discovery's AI captioning tool delivered a 50% reduction in overall costs and cut the time to caption a file by 80%. Your saving depends on how much editing your content needs, so measure editing time per hour on a sample first.",{"question":180,"answer":181},"Can AI captions be published without a human check?","For live events there is often no human in the loop before the caption appears, and organizations such as Pacers Sports & Entertainment rely on tuned models and moderation filters. For recorded content, an editor should check names, numbers and sensitive words, and under the EU AI Act human editorial review also removes the duty to label published text as AI generated.",{"question":183,"answer":184},"How is this different from AI meeting notes?","Meeting tools produce summaries and action items for participants. This use case produces a complete, timed transcript and caption files for publication or the archive, where every word and its timing matter and an editor signs off.",{"question":186,"answer":187},"Which accessibility rules apply?","In the EU, the European Accessibility Act requires services that give access to audiovisual media, such as players and apps, to transmit subtitles for the deaf and hard of hearing with adequate quality and in sync with sound and video. Automation is how broadcasters such as SVT caption content they could not caption by hand.",[189,190,191,192],"meeting-summarization-and-action-items","public-service-translation","court-and-case-file-summarization","call-quality-and-compliance-monitoring","2026-09-27","2026-09-26",[196],{"date":193,"note":197},"First published","audio-and-video-transcription-and-captioning",[200,228,260,280,305],{"title":201,"useCases":202,"organization":203,"vendors":207,"summary":211,"stage":212,"year":213,"channels":214,"languages":215,"metrics":216,"outcomeDisclosed":217,"sources":218,"verification":223,"grade":225,"id":226,"organizationSlug":227},"Ateme: automated multilingual subtitle generation for broadcasters and streaming platforms",[198],{"name":204,"anonymized":205,"country":206,"region":154,"industry":18},"Ateme",false,"FR",[208],{"name":209,"role":210},"Google Cloud (Vertex AI, Gemini)","platform","Ateme, a French video compression and delivery company, added a subtitle step to its file transcoding platform: after transcoding, Gemini on Vertex AI transcribes the audio, spots timecodes and generates subtitles in the requested languages, and a script converts them to SRT for the client's workflow. Ateme says a job that took up to 15 hours of manual work per hour of video now takes minutes and costs less than a dollar per hour of content.","production",2025,[29],[],[],true,[219],{"url":220,"title":221,"publisher":222},"https://cloud.google.com/customers/ateme","Ateme customer story | Google Cloud","Google Cloud",{"level":224,"checkedAt":194},"source-verified","C","ateme-multilingual-subtitle-generation",null,{"title":229,"useCases":230,"organization":231,"vendors":235,"summary":238,"stage":212,"year":213,"channels":239,"languages":242,"metrics":245,"outcomeDisclosed":217,"sources":254,"verification":258,"grade":225,"id":259,"organizationSlug":227},"Pacers Sports & Entertainment: custom speech model for live arena and app captions",[198],{"name":232,"anonymized":205,"country":233,"region":234,"industry":18},"Pacers Sports & Entertainment","US","north-america",[236],{"name":237,"role":210},"Microsoft (Azure AI Foundry, Azure AI Speech)","Pacers Sports & Entertainment trained a custom speech model on hundreds of hours of its own game broadcasts, with lists of player, coach and official names, to caption live announcers for fans who are deaf, hard of hearing or do not speak English, on arena screens and in its mobile apps. English came first, then Spanish, and Microsoft reports the team is adding 12 more languages. It is the live variant of the job: captions are delivered in real time, and Microsoft reports built in moderation filters that help keep inappropriate or misinterpreted content off the screen. The system now captions Pacers, Fever and All Star games at Gainbridge Fieldhouse.",[240,241],"mobile-app","kiosk",[243,244],"en","es",[246],{"kpi":51,"value":247,"unit":248,"qualifier":249,"baseline":250,"claimant":251,"quote":252,"sourceUrl":253},87,"percent","exact","Transcription error rate of the speech to text model before it was tuned to the Pacers broadcast style","vendor","By tailoring the model to the Pacers’ broadcast style, they reduced the speech-to-text transcription error rate by 87%—a level of precision that made it possible to extend captioning from mobile apps to arena screens with confidence.","https://www.microsoft.com/en/customers/story/23957-pacers-sports-and-entertainment-azure-ai-foundry",[255],{"url":253,"title":256,"publisher":257},"Indiana Pacers use Azure AI Foundry to create first live arena captioning service","Microsoft Customer Stories",{"level":224,"checkedAt":194},"pacers-sports-and-entertainment-live-arena-captions",{"title":261,"useCases":262,"organization":263,"vendors":266,"summary":269,"stage":212,"year":270,"channels":271,"languages":272,"metrics":273,"outcomeDisclosed":205,"sources":274,"verification":278,"grade":225,"id":279,"organizationSlug":227},"Comeen: multilingual subtitles for internal communication videos",[198],{"name":264,"anonymized":205,"country":206,"region":154,"industry":265},"Comeen","technology",[267],{"name":268,"role":210},"Google Cloud (Vertex AI, Gemini, Speech to Text)","Comeen, a workplace software company, launched automatic multilingual subtitles for videos shown on its clients' digital signage at the end of 2024. Employees upload a video in their usual tools and receive subtitles in 40 languages, produced by a multi step AI workflow that the company says makes them usable as is. It replaces a process that used several providers over several days.",2024,[30],[],[],[275],{"url":276,"title":277,"publisher":222},"https://cloud.google.com/customers/comeen","Comeen case study | Google Cloud",{"level":224,"checkedAt":194},"comeen-multilingual-video-subtitles",{"title":281,"useCases":282,"organization":283,"vendors":285,"summary":288,"stage":212,"year":270,"channels":289,"languages":290,"metrics":291,"outcomeDisclosed":217,"sources":300,"verification":303,"grade":225,"id":304,"organizationSlug":227},"Warner Bros. Discovery: AI captioning tool on Vertex AI",[198],{"name":284,"anonymized":205,"country":233,"region":234,"industry":18},"Warner Bros. Discovery",[286],{"name":287,"role":210},"Google Cloud (Vertex AI)","Warner Bros. Discovery built an AI captioning tool on Vertex AI. Google Cloud reports that it delivered a 50% reduction in overall costs and cut the time to caption a file by 80% compared with manual captioning. No detail on languages, volumes or the review step is published.",[29],[],[292,297],{"kpi":49,"value":293,"unit":248,"qualifier":249,"baseline":294,"claimant":251,"quote":295,"sourceUrl":296},50,"Overall costs before the AI captioning tool","Warner Bros. Discovery built an AI captioning tool with Vertex AI, delivering a 50% reduction in overall costs and an 80% reduction in the time it takes to manually caption a file without the use of machine learning.","https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",{"kpi":50,"value":298,"unit":248,"qualifier":249,"baseline":299,"claimant":251,"quote":295,"sourceUrl":296},80,"Manual captioning of a file without machine learning",[301],{"url":296,"title":302,"publisher":222},"Real world gen AI use cases from the world's leading organizations",{"level":224,"checkedAt":194},"warner-bros-discovery-ai-captioning-tool",{"title":306,"useCases":307,"organization":308,"vendors":311,"summary":314,"stage":315,"year":316,"channels":317,"languages":318,"metrics":320,"outcomeDisclosed":205,"sources":321,"verification":325,"grade":225,"id":326,"organizationSlug":227},"SVT (Sveriges Television): automated transcription and closed captions for regional news",[198],{"name":309,"anonymized":205,"country":310,"region":154,"industry":18},"Sveriges Television (SVT)","SE",[312],{"name":313,"role":210},"Microsoft (Azure AI Speech, Speech Studio)","SVT, Sweden's public broadcaster, transcribes its video content and generates closed captions automatically with Azure speech services, in production since 2021. The broadcaster says it could not caption its local news otherwise, because it publishes for 21 regional stations at the same time several times a day, and that feedback from viewers with hearing loss is mostly positive.","scaled",2021,[29],[319],"sv",[],[322],{"url":323,"title":324,"publisher":257},"https://www.microsoft.com/en/customers/story/1533261355300735842-sveriges-television-ab-media-entertainment-azure","Swedish Television improves public news accessibility with AI",{"level":224,"checkedAt":194},"sveriges-television-automated-closed-captions",1,[329,335,340],{"kpi":49,"label":330,"unit":248,"aggregate":217,"higherIsBetter":217,"n":327,"nUpTo":331,"median":293,"min":293,"max":293,"byClaimant":332,"vendorOnly":217,"points":333},"Cost reduction",0,{"organization":331,"vendor":327,"regulator":331,"independent":331},[334],{"evidenceId":304,"organization":284,"value":293,"qualifier":249,"claimant":251,"grade":225,"pooled":217},{"kpi":50,"label":336,"unit":248,"aggregate":217,"higherIsBetter":217,"n":327,"nUpTo":331,"median":298,"min":298,"max":298,"byClaimant":337,"vendorOnly":217,"points":338},"Cycle time reduction",{"organization":331,"vendor":327,"regulator":331,"independent":331},[339],{"evidenceId":304,"organization":284,"value":298,"qualifier":249,"claimant":251,"grade":225,"pooled":217},{"kpi":51,"label":341,"unit":248,"aggregate":217,"higherIsBetter":217,"n":327,"nUpTo":331,"median":247,"min":247,"max":247,"byClaimant":342,"vendorOnly":217,"points":343},"Error reduction",{"organization":331,"vendor":327,"regulator":331,"independent":331},[344],{"evidenceId":259,"organization":232,"value":247,"qualifier":249,"claimant":251,"grade":225,"pooled":217},{"low":346,"high":347},225000,1500000,[349,368,391,407],{"slug":189,"title":350,"shortTitle":351,"definition":352,"status":9,"industries":353,"functions":356,"patterns":357,"audience":359,"autonomy":32,"adoptionStage":33,"evidenceCount":360,"publicEvidenceCount":360,"organizations":361,"bestGrade":367,"headline":227,"lastVerified":193,"indexable":217},"AI meeting summarization and action items","Meeting summaries and action items","AI that summarizes internal and operational meetings, such as team, project, board and case meetings: it transcribes an online or in person meeting with the participants' knowledge and produces a summary, decisions and action items with owners and dates for the organizer to check and share. It is the general purpose tool; client advice meetings and sales calls, which feed a regulated record or a sales pipeline, have their own pages.",[20,354,265,355],"government","professional-services",[23,22],[358,25],"summarization","employee-facing",5,[362,363,364,365,366],"U.S. Department of Labor","Ministry of Justice","Softcat","Trace3","Government Digital Service","B",{"slug":190,"title":369,"shortTitle":370,"definition":371,"status":9,"industries":372,"functions":373,"patterns":376,"audience":379,"autonomy":32,"adoptionStage":380,"evidenceCount":381,"publicEvidenceCount":381,"organizations":382,"bestGrade":367,"headline":227,"lastVerified":194,"indexable":217},"AI translation and interpretation for multilingual public services","Public service translation","AI that translates government content, documents and conversations between officials and the public, in writing and in real time speech, so people can use public services in their own language, with human translators and interpreters reviewing what carries legal or safety weight.",[354],[374,375,22],"citizen-services","customer-service",[26,377,25,378],"conversational-agent","document-processing","customer-facing","early-adopters",8,[383,384,385,386,387,388,389,390],"Baltimore City 911 (Emergency Communications)","Delaware County","European Commission","Federal Emergency Management Agency","Internal Revenue Service","Madrid Destino","Montgomery County Government","U.S. Department of State (Bureau of Consular Affairs)",{"slug":191,"title":392,"shortTitle":393,"definition":394,"status":9,"industries":395,"functions":396,"patterns":399,"audience":359,"autonomy":32,"adoptionStage":380,"evidenceCount":401,"publicEvidenceCount":401,"organizations":402,"bestGrade":367,"headline":227,"lastVerified":193,"indexable":217},"AI for court and case file summarization","Case file summarization","AI that condenses court filings, case files, evidence recordings and earlier decisions into structured summaries, chronologies and draft case reports with references to the source pages, so that judges, prosecutors, tribunal staff and government lawyers find what matters faster, while the person responsible reads the underlying material and makes every legal judgment.",[354],[397,398],"legal","case-management",[358,378,400],"rag-knowledge-assistant",4,[403,404,405,406],"Crown Prosecution Service","U.S. Department of Justice","Gemeente Amsterdam","Supremo Tribunal Federal",{"slug":192,"title":408,"shortTitle":409,"definition":410,"status":9,"industries":411,"functions":417,"patterns":419,"audience":31,"autonomy":421,"adoptionStage":380,"evidenceCount":360,"publicEvidenceCount":360,"organizations":422,"bestGrade":225,"headline":428,"lastVerified":193,"indexable":217},"AI quality and compliance monitoring of every customer interaction","Call quality and compliance","Automated quality assurance that transcribes and scores every customer interaction, voice and chat, against the organization's own rubric, checking required disclosures and script adherence, flagging conduct and mis selling risk, and surfacing coaching opportunities, instead of the small sample a human QA team can review.",[20,412,413,414,415,416],"banking","insurance","energy-and-utilities","telecommunications","retail-and-ecommerce",[375,418,22],"regulatory-compliance",[25,420,358],"classification-and-routing","supervised-agent",[423,424,425,426,427],"British Gas","Central Bank","DoorDash","Oportun","VitalityHealth",{"kpi":429,"label":430,"unit":248,"n":327,"nUpTo":331,"kind":431,"value":432,"qualifier":433,"claimant":251,"organization":423,"vendorReported":217},"quality-score-uplift","Quality score uplift","reported",10,"approximately",{"indexable":217,"reasons":435},[],[437,442,447,455,462,468,475,482,490,497,504,510,517,524,530,535,542,547,553,559,565,571,576,581,586,593,599,604,610,617,623,629,636,641],{"id":147,"label":438,"issuer":153,"region":154,"url":439,"description":440,"useCases":441,"indexable":217},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":148,"label":443,"issuer":153,"region":154,"url":444,"description":445,"useCases":446,"indexable":217},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":448,"label":449,"issuer":450,"region":451,"url":452,"description":453,"useCases":454,"indexable":217},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":456,"label":457,"issuer":458,"region":234,"url":459,"description":460,"useCases":461,"indexable":217},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":463,"label":464,"issuer":153,"region":154,"url":465,"description":466,"useCases":467,"indexable":217},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":469,"label":470,"issuer":471,"region":154,"url":472,"description":473,"useCases":474,"indexable":217},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":476,"label":477,"issuer":478,"region":154,"url":479,"description":480,"useCases":481,"indexable":217},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":483,"label":484,"issuer":485,"region":486,"url":487,"description":488,"useCases":489,"indexable":217},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":491,"label":492,"issuer":493,"region":486,"url":494,"description":495,"useCases":496,"indexable":217},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":498,"label":499,"issuer":500,"region":451,"url":501,"description":502,"useCases":503,"indexable":217},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":505,"label":506,"issuer":507,"region":234,"url":508,"description":509,"useCases":503,"indexable":217},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":511,"label":512,"issuer":513,"region":154,"url":514,"description":515,"useCases":516,"indexable":217},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":518,"label":519,"issuer":520,"region":451,"url":521,"description":522,"useCases":523,"indexable":217},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":525,"label":526,"issuer":153,"region":154,"url":527,"description":528,"useCases":529,"indexable":217},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":531,"label":532,"issuer":153,"region":154,"url":533,"description":534,"useCases":529,"indexable":217},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":536,"label":537,"issuer":538,"region":234,"url":539,"description":540,"useCases":541,"indexable":217},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":149,"label":543,"issuer":153,"region":154,"url":544,"description":545,"useCases":546,"indexable":217},"European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":548,"label":549,"issuer":550,"region":234,"url":551,"description":552,"useCases":546,"indexable":217},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":554,"label":555,"issuer":556,"region":451,"url":557,"description":558,"useCases":546,"indexable":217},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":560,"label":561,"issuer":153,"region":154,"url":562,"description":563,"useCases":564,"indexable":217},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":566,"label":567,"issuer":568,"region":234,"url":569,"description":570,"useCases":564,"indexable":217},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":572,"label":573,"issuer":485,"region":486,"url":574,"description":575,"useCases":432,"indexable":217},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":577,"label":578,"issuer":153,"region":154,"url":579,"description":580,"useCases":432,"indexable":217},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":582,"label":583,"issuer":153,"region":154,"url":584,"description":585,"useCases":432,"indexable":217},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":587,"label":588,"issuer":589,"region":154,"url":590,"description":591,"useCases":592,"indexable":217},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":594,"label":595,"issuer":596,"region":234,"url":597,"description":598,"useCases":381,"indexable":217},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":600,"label":601,"issuer":153,"region":154,"url":602,"description":603,"useCases":381,"indexable":217},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":605,"label":606,"issuer":153,"region":154,"url":607,"description":608,"useCases":609,"indexable":217},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",6,{"id":611,"label":612,"issuer":613,"region":614,"url":615,"description":616,"useCases":360,"indexable":217},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":618,"label":619,"issuer":620,"region":154,"url":621,"description":622,"useCases":401,"indexable":217},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":624,"label":625,"issuer":626,"region":154,"url":627,"description":628,"useCases":401,"indexable":217},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":630,"label":631,"issuer":632,"region":486,"url":633,"description":634,"useCases":635,"indexable":217},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":637,"label":638,"issuer":153,"region":154,"url":639,"description":640,"useCases":635,"indexable":217},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":642,"label":643,"issuer":644,"region":234,"url":645,"description":646,"useCases":635,"indexable":217},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598306739]