[{"data":1,"prerenderedAt":744},["ShallowReactive",2],{"uc-non-emergency-service-request-routing":3,"uc-regulations":542},{"useCase":4,"evidence":202,"blitsAiDeployments":393,"benchmarks":394,"indicative":419,"related":422,"indexability":540,"includeUnpublished":208},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":22,"channels":27,"audience":33,"autonomy":34,"adoptionStage":35,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":50,"macroEstimates":84,"feasibility":85,"implementation":97,"risk":140,"blitsAi":178,"faq":180,"related":190,"datePublished":196,"dateModified":196,"lastVerified":197,"changelog":198,"slug":201},"AI for non emergency service requests and 311 routing","Non emergency service request routing","AI for 311 and non emergency service requests","AI agents answer 311 and non emergency calls, create service cases and route urgent ones. Prepared reports 73% of Galt PD call volume handled before a dispatcher.","published","An AI agent on a city's 311 style phone, chat and messaging channels that answers routine municipal questions, takes service requests such as potholes, missed collections or broken street lights with the right location and details, creates the case in the work order system and routes anything urgent or complex to the right team.",[12,13,14,15],"311 chatbot","municipal service request bot","non emergency line AI","city service request assistant",[17],"government",[19,20,21],"citizen-services","customer-service","case-management",[23,24,25,26],"conversational-agent","voice-agent","classification-and-routing","agentic-workflow",[28,29,30,31,32],"voice","web-chat","whatsapp","mobile-app","sms","customer-facing","supervised-agent","early-adopters","Local government runs dozens of services, and residents contact it about all of them through the\nsame few channels: a 311 or general number and a website. Most contacts are\nroutine (collection days, opening hours, a pothole, a noisy neighbour), but each one needs a person\nto listen, find the right department, type the location and create a case. At the same time\npolice and 911 centres spend dispatcher time on non emergency calls to their ten digit lines,\npulling attention from real emergencies.\n\nMenus and web forms do not fix this: residents do not know which department owns their problem,\nand forms lose the detail (exact location, photos) that crews need.",[],"1. **Listen and classify.** The agent asks what the resident needs and classifies it into the\n   city's service catalogue, or detects that it is actually an emergency and transfers immediately.\n2. **Answer information questions.** Collection days, bylaws or opening hours are answered from\n   the city's content and data, including address specific answers from GIS.\n3. **Capture the request.** For a service request, the agent collects the location (address, map\n   pin or photo), description and contact details, and checks for duplicates nearby.\n4. **Create and route the case.** It creates the case in the work order or CRM system with the\n   right category and priority and tells the resident the reference number.\n5. **Keep the resident updated.** Status updates go back on the same channel until the case closes.\n6. **Hand over.** Complex, sensitive or vulnerable cases go to a contact centre agent with the\n   conversation attached.",[40,41,42,43],"cost-to-serve","customer-experience","speed","inclusion-and-access",[45,46,47,48,49],"contact-deflection","interactions-handled","accuracy","response-time-reduction","customer-satisfaction",{"referenceOrg":51,"inputs":52,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A city of 500,000 residents with 400,000 contacts a year to its 311 service",[53,59,66,72],{"key":54,"label":55,"low":56,"high":56,"unit":57,"note":58},"contacts","311 contacts per year across phone, chat and messaging",400000,"contacts per year","The reference city.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"automatable","Share of contacts that are routine questions or simple service requests",0.5,0.7,"fraction of contacts","Editorial assumption. Prepared's Galt case study says more than 73% of calls to that police department are non emergency, which is a different mix; replace with your own data.",{"key":67,"label":68,"low":69,"high":62,"unit":70,"note":71},"containment","Share of those the agent completes without an agent",0.25,"fraction of automatable contacts","Editorial assumption, conservative against the contact deflection benchmark on this page.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"costPerContact","Cost of an agent handled contact",4,7,"USD per contact","Editorial assumption. Replace with your own fully loaded cost.","contacts * automatable * containment * costPerContact","USD","per year","Agent handled contact cost avoided","Gross contact cost only. It leaves out better case data for crews, fewer duplicate reports, dispatcher time protected on police lines and the cost of integration and running the agent.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":92},"medium","Answering questions is simple; creating good cases needs integration with the work order or CRM system, a clean service catalogue, location capture and duplicate checks, and a safe transfer path for emergencies.",[89,90,91],"The city's service request catalogue with categories, owners and priorities","Municipal content and address level data (collection schedules, zoning, service areas)","Historic 311 cases to train and test classification",[93,94,95,96],"311 CRM or work order system (case creation and status)","GIS and address lookup","Telephony, web chat and messaging channels","Contact centre platform for handover and emergency transfer",{"steps":98,"guardrails":114,"humanInTheLoop":120,"kpisToInstrument":121,"failureModes":127},[99,102,105,108,111],{"title":100,"detail":101},"Clean the service catalogue first","Agree the categories, required fields and owning team for each request type; the agent can only route as well as the catalogue allows.",{"title":103,"detail":104},"Start with the top ten requests","Launch with the highest volume information questions and two or three simple request types, such as missed collections and potholes.",{"title":106,"detail":107},"Build the emergency exit","Define phrases and signals that trigger an immediate transfer to 911 or the dispatcher, and test them with real transcripts, as Galt's agent transfers genuine emergencies at once.",{"title":109,"detail":110},"Capture location well","Use address validation, map pins or photos (TAMM's assistant helps fill in a report from a photo) so crews can find the problem the first time.",{"title":112,"detail":113},"Close the loop","Send status updates and closure notices on the channel the resident used, and measure repeat reports.",[115,116,117,118,119],"Immediate transfer on any sign of emergency, with a tested phrase list and a low threshold","Case creation only in defined categories with required fields validated","Answers only from city content and data, with refusal outside scope","Personal data minimised and masked in logs","Clear AI disclosure and a way to reach a person during office hours","Contact centre agents take handovers, complaints and sensitive cases; supervisors review samples of contained conversations and misrouted cases every week; department owners approve changes to categories and priorities.",[122,123,124,125,126],"Containment per request type and share of calls transferred as emergencies","Routing accuracy (cases moved to another department after creation)","Share of cases with a valid location and complete required fields","Time from report to case creation and to resolution","Duplicate reports and repeat contacts on the same issue",[128,131,134,137],{"title":129,"detail":130},"A missed emergency","A caller on a non emergency line describes an emergency in vague words. Keep the transfer threshold low and review every transferred and non transferred call with emergency keywords.",{"title":132,"detail":133},"Wrong department, lost case","Misrouted cases bounce between teams. Measure reassignment and fix the catalogue.",{"title":135,"detail":136},"Accuracy only where content exists","Barnet's transparency record gives the Ami model an average of 90% correct answers, but only for queries it has content for (a model figure, not a measured pilot result); questions outside that content fail. Track unanswered questions, not only accuracy.",{"title":138,"detail":139},"Reports without follow up","Residents report, hear nothing and call again. Send status updates.",{"euAiAct":141,"regulations":144,"guidance":151,"controls":167,"incidents":173},{"tier":142,"basis":143},"limited","A 311 assistant must disclose that it is AI (Article 50). It is not high risk while it only informs and creates service cases. If it evaluates or classifies emergency calls or sets dispatch priority for police, fire or medical services, it falls under Annex III point 5(d) and becomes high risk.",[145,146,147,148,149,150],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001","uk-gdpr","uk-atrs",[152,158,162],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","Residents must be informed that they are interacting with an AI system.",{"title":159,"issuer":154,"region":155,"url":160,"note":161},"Annex III, high risk AI systems referred to in Article 6(2)","https://artificialintelligenceact.eu/annex/3/","Point 5(d) covers classification of emergency calls and dispatch priority, the boundary a non emergency line must not cross silently.",{"title":163,"issuer":164,"region":155,"url":165,"note":166},"Algorithmic Transparency Recording Standard Hub","Government Digital Service","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Recommended for local government in the UK; Barnet and Newcastle councils publish records.",[168,169,170,171,172],"AI disclosure and a published description of what the agent can and cannot do","Tested emergency transfer path, reviewed after every change","Access control and retention limits on conversation logs and location data","Weekly review of misrouted cases with department owners","Accessibility testing for voice and chat channels",[174],{"title":175,"url":176,"note":177},"NYC's AI chatbot tells businesses to break the law","https://themarkup.org/news/2024/03/29/nycs-ai-chatbot-tells-businesses-to-break-the-law","In tests by The Markup, New York City's business chatbot told landlords and business owners that illegal practices, such as refusing housing vouchers or taking workers' tips, were allowed; the same risk applies to municipal information on 311 channels.",{"howToBuild":179},"On Blits.ai this is an **AI agent** grounded in a **knowledge base** of municipal content, with\n**custom functions** that look up address data and create cases in the city's CRM or work order\nsystem through REST calls. Service request journeys run as **flows** with slot filling for\nlocation and details, validation blocks and a receive attachment block for photos; a condition\non emergency language triggers an immediate **redirect call** or **agent handover**.\n\nThe same agent answers on **voice, web chat, WhatsApp and SMS**, with streaming speech\nrecognition and call transfer on the phone and language detection for multilingual residents.\n**Guardrails** and **PII masking** protect residents' data, **test suites** run conversation\nsets that include emergency phrases before each release, and **analytics** show top intents,\nflow statistics and unanswered questions per channel.",[181,184,187],{"question":182,"answer":183},"How much of a non emergency line can AI handle?","It varies by call mix. Prepared, the vendor, reports that 73% of Galt Police Department's call volume is now handled before it reaches a dispatcher, in a department where more than 73% of calls are non emergency. Microsoft reports that Kelowna's assistant answers 80% of snowplow calls correctly, so measure by request type rather than for the line as a whole.",{"question":185,"answer":186},"Does AI on a non emergency line fall under the EU AI Act high risk rules?","Not while it only answers questions and creates service cases; the Article 50 duty to disclose that residents are talking to AI still applies. It becomes high risk under Annex III point 5(d) if it evaluates or classifies emergency calls or sets dispatch priority for emergency services, so keep emergency detection as a simple transfer to a human dispatcher who decides, and document that design choice.",{"question":188,"answer":189},"What volumes do city assistants handle?","Google reports more than 30,000 conversations a month on Rio de Janeiro's 1746 chatbot, and Microsoft reports more than 20,000 conversations for Montgomery County's Monty 2.0 since its beta. Start small and scale with the catalogue.",[191,192,193,194,195],"citizen-information-assistant","emergency-call-triage-support","public-service-translation","correspondence-triage-and-routing","first-line-contact-centre-agent","2026-09-27","2026-09-26",[199],{"date":196,"note":200},"First published","non-emergency-service-request-routing",[203,236,259,286,316,343,369],{"title":204,"useCases":205,"organization":206,"vendors":210,"summary":217,"stage":218,"year":219,"channels":220,"languages":222,"metrics":224,"outcomeDisclosed":208,"sources":225,"verification":231,"grade":233,"id":234,"organizationSlug":235},"Newcastle City Council: AI routing, agent prompts and call analytics in the contact centre",[201],{"name":207,"anonymized":208,"country":209,"region":155,"industry":17},"Newcastle City Council",false,"GB",[211,214],{"name":212,"role":213},"Amazon Web Services (Amazon Connect, Amazon Q in Connect, Contact Lens)","platform",{"name":215,"role":216},"PwC","integrator","Newcastle City Council replaced legacy telephony with Amazon Connect, which routes resident calls and chats to the right team; Amazon Lex may also power menus for self service and triage. During live contacts, Amazon Q in Connect suggests approved knowledge and responses to agents; Contact Lens transcribes calls and surfaces topics and quality signals for supervisors. The council states that all outputs are advisory and do not decide eligibility, enforcement or case outcomes.","production",2026,[28,29,221],"agent-desktop",[223],"en",[],[226],{"url":227,"title":228,"publisher":229,"date":230},"https://www.gov.uk/algorithmic-transparency-records/newcastle-city-council-aws-contact-centre-services-amazon-q-and-contact-lens","Newcastle City Council: AWS Contact Centre Services (Amazon Q and Contact Lens)","GOV.UK (Algorithmic Transparency Recording Standard)","2026-08-13",{"level":232,"checkedAt":197},"source-verified","B","newcastle-city-council-contact-centre-ai",null,{"title":237,"useCases":238,"organization":239,"vendors":241,"summary":246,"stage":247,"year":248,"channels":249,"languages":250,"metrics":251,"outcomeDisclosed":208,"sources":252,"verification":257,"grade":233,"id":258,"organizationSlug":235},"Barnet Council: Ami chatbot for council tax, benefits and waste enquiries",[201],{"name":240,"anonymized":208,"country":209,"region":155,"industry":17},"London Borough of Barnet",[242,244],{"name":243,"role":213},"Conversations By Ami Ltd",{"name":245,"role":216},"Capita PLC","Barnet Council piloted Ami on its website to signpost residents 24/7 to the right page or service in a few areas: council tax, housing benefits, waste and recycling, schools and pest control. Ami only uses council approved content, can move the resident straight to the relevant page, watches for signs that a resident needs more support and, in office hours, connects them to an agent in the Amazon Connect contact centre that Capita runs for the council. The pilot expected about 30,000 chats over six months; no decisions are made about residents.","pilot",2025,[29],[223],[],[253],{"url":254,"title":255,"publisher":229,"date":256},"https://www.gov.uk/algorithmic-transparency-records/barnet-council-ami-chatbot","Barnet Council: Ami Chatbot","2025-01-28",{"level":232,"checkedAt":197},"barnet-council-ami-chatbot",{"title":260,"useCases":261,"organization":262,"vendors":266,"summary":272,"stage":273,"year":248,"channels":274,"languages":275,"metrics":276,"outcomeDisclosed":208,"sources":277,"verification":283,"grade":284,"id":285,"organizationSlug":235},"Abu Dhabi Government: TAMM AI assistant for government services",[191,201],{"name":263,"anonymized":208,"country":264,"region":265,"industry":17},"Abu Dhabi Government (TAMM)","AE","middle-east",[267,270],{"name":268,"role":269},"Microsoft (Azure OpenAI Service)","model-provider",{"name":271,"role":213},"G42 (Compass 2.0)","TAMM is Abu Dhabi's single platform for about 950 government services from many entities, from car registration and visa renewals to traffic fines. The platform, including its AI assistant, is powered by Azure OpenAI Service and G42 Compass 2.0 (which also gives access to the Arabic JAIS model). The assistant answers questions about processes, shows the status of a user's requests and speaks several languages. A photo reporting feature lets residents photograph a problem such as a pothole or a broken traffic light; the assistant helps fill in the report and updates the reporter on the repair.","scaled",[31,29],[],[],[278],{"url":279,"title":280,"publisher":281,"date":282},"https://news.microsoft.com/source/emea/features/tamm-app-abu-dhabi-government-services/","Consider it done: How TAMM is transforming government services in Abu Dhabi with AI","Microsoft Source EMEA","2025-02-06",{"level":232,"checkedAt":197},"C","abu-dhabi-tamm-ai-assistant",{"title":287,"useCases":288,"organization":289,"vendors":293,"summary":296,"stage":218,"year":248,"channels":297,"languages":298,"metrics":300,"outcomeDisclosed":309,"sources":310,"verification":314,"grade":284,"id":315,"organizationSlug":235},"Rio de Janeiro: 1746 citizen service chatbot for urban maintenance requests",[201],{"name":290,"anonymized":208,"country":291,"region":292,"industry":17},"Rio de Janeiro City Data Office (Escritório de Dados)","BR","latin-america",[294],{"name":295,"role":213},"Google Cloud (Dialogflow)","Rio de Janeiro's City Data Office (Escritório de Dados) uses Dialogflow to run the chatbot of 1746, the city's citizen service, which handles urban maintenance requests and municipal enquiries, the kind of work a 311 line does elsewhere. Google reports that it cut the citizen response time from 30 minutes to 5 minutes. The only public source found is a one paragraph entry in Google's customer list.",[],[299],"pt",[301],{"kpi":46,"value":302,"unit":303,"qualifier":304,"period":305,"claimant":306,"quote":307,"sourceUrl":308},30000,"count","at-least","conversations per month","vendor","The conversational AI reduces citizen response time from 30 minutes to 5 minutes across more than 30,000 monthly conversations.","https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",true,[311],{"url":308,"title":312,"publisher":313},"Real world gen AI use cases from the world's leading organizations","Google Cloud",{"level":232,"checkedAt":197},"rio-de-janeiro-1746-citizen-service-chatbot",{"title":317,"useCases":318,"organization":319,"vendors":323,"summary":326,"stage":218,"year":327,"channels":328,"languages":329,"metrics":330,"outcomeDisclosed":309,"sources":338,"verification":341,"grade":284,"id":342,"organizationSlug":235},"Galt Police Department: AI triage of non emergency calls and assistive 911 call taking",[201,192],{"name":320,"anonymized":208,"country":321,"region":322,"industry":17},"Galt Police Department","US","north-america",[324],{"name":325,"role":213},"Prepared","Galt Police Department serves 26,000 residents with eight dispatch staff, who handle nearly 30,000 calls a year, more than 73% of them non emergency. Since 2024 an AI agent from Prepared answers the ten digit non emergency line, works out what the caller needs, resolves it or routes it to the right resource, and transfers any genuine emergency immediately. On 911 calls, dispatchers get a live transcript, an AI summary and key details highlighted as the call happens. During a shooting in Galt, the agent handled the incoming non emergency calls in the background while dispatchers managed the response.",2024,[28,221],[223],[331],{"kpi":45,"value":332,"unit":333,"qualifier":334,"period":335,"claimant":306,"quote":336,"sourceUrl":337},73,"percent","exact","share of call volume handled before reaching a dispatcher","With 73% of call volume now handled before it reaches a dispatcher's headset, the calls that do come through are the ones that genuinely need a human.","https://www.prepared911.com/case-studies/galt-pd-assistive-ai",[339],{"url":337,"title":340,"publisher":325},"Galt PD: Making the Unmanageable Manageable with Assistive AI",{"level":232,"checkedAt":197},"galt-police-department-non-emergency-call-triage",{"title":344,"useCases":345,"organization":346,"vendors":348,"summary":352,"stage":218,"year":327,"channels":353,"languages":354,"metrics":355,"outcomeDisclosed":309,"sources":363,"verification":367,"grade":284,"id":368,"organizationSlug":235},"Montgomery County, Maryland: Monty 2.0 constituent chatbot",[191,201,193],{"name":347,"anonymized":208,"country":321,"region":322,"industry":17},"Montgomery County Government",[349,351],{"name":350,"role":213},"Zammo.ai",{"name":268,"role":269},"Montgomery County first launched Monty to relieve its 311 hotline during the pandemic, with 20 topics, and retired it when demand fell. Monty 2.0, built with Zammo.ai on Azure OpenAI Service and Azure AI Search, answers questions on more than 3,000 topics, with automatic translation into 140 languages, from the county's own knowledge base, and uses the county's geographic data to give address specific answers such as trash pickup days. It went through a seven month beta with a constituent focus group before the full launch in late 2024.",[29],[223],[356,361],{"kpi":46,"value":357,"unit":303,"qualifier":304,"period":358,"claimant":306,"quote":359,"sourceUrl":360},20000,"since the beta deployment","Since its beta deployment, Monty 2.0 has facilitated more than 20,000 constituent conversations, achieving a 50% customer satisfaction rate and reducing unanswered queries from 35%–45% to just 10%–15%.","https://www.microsoft.com/en/customers/story/23066-montgomery-county-azure-open-ai-service",{"kpi":49,"value":362,"unit":333,"qualifier":334,"period":358,"claimant":306,"quote":359,"sourceUrl":360},50,[364],{"url":360,"title":365,"publisher":366},"Montgomery County revolutionizes constituent experiences with an AI chatbot powered by Microsoft Azure OpenAI Service","Microsoft Customer Stories",{"level":232,"checkedAt":196},"montgomery-county-monty-chatbot",{"title":370,"useCases":371,"organization":372,"vendors":375,"summary":378,"stage":218,"year":379,"channels":380,"languages":381,"metrics":382,"outcomeDisclosed":309,"sources":388,"verification":391,"grade":284,"id":392,"organizationSlug":235},"City of Kelowna: AI answers for the 311 line and permit questions",[201],{"name":373,"anonymized":208,"country":374,"region":322,"industry":17},"City of Kelowna","CA",[376,377],{"name":350,"role":213},{"name":268,"role":269},"Kelowna, a city of nearly 150,000 in British Columbia, uses Zammo.ai on Azure to answer calls and chats to its 311 non emergency line about property taxes, landfill rules, utilities and snowplowing, the last using live GPS data from the plows. With a provincial housing grant it also built a quick reference tool that returns the zoning bylaws and documents for a given address, and plans to extend it so the assistant walks applicants through the permit process up to the point where a city planner takes over. The city is creating an online registry of the data sources the system uses.",2023,[28,29],[223],[383],{"kpi":47,"value":384,"unit":333,"qualifier":334,"period":385,"claimant":306,"quote":386,"sourceUrl":387},80,"snowplow schedule calls","In 80 percent of cases, AI has already proven that it can deliver correct responses to people who call in about the snow.","https://www.microsoft.com/en/customers/story/1641166525350342888-city-of-kelowna-government-azure-open-ai-service",[389],{"url":387,"title":390,"publisher":366},"The City of Kelowna increases and speeds access to its services with Azure AI",{"level":232,"checkedAt":197},"city-of-kelowna-311-ai-assistant",0,[395,403,409,414],{"kpi":46,"label":396,"unit":303,"aggregate":208,"higherIsBetter":309,"n":397,"nUpTo":393,"median":398,"min":357,"max":302,"byClaimant":399,"vendorOnly":309,"points":400},"Interactions handled",2,25000,{"organization":393,"vendor":397,"regulator":393,"independent":393},[401,402],{"evidenceId":315,"organization":290,"value":302,"qualifier":304,"claimant":306,"grade":284,"pooled":309},{"evidenceId":368,"organization":347,"value":357,"qualifier":304,"claimant":306,"grade":284,"pooled":309},{"kpi":47,"label":404,"unit":333,"aggregate":309,"higherIsBetter":309,"n":405,"nUpTo":393,"median":384,"min":384,"max":384,"byClaimant":406,"vendorOnly":309,"points":407},"Accuracy",1,{"organization":393,"vendor":405,"regulator":393,"independent":393},[408],{"evidenceId":392,"organization":373,"value":384,"qualifier":334,"claimant":306,"grade":284,"pooled":309},{"kpi":45,"label":410,"unit":333,"aggregate":309,"higherIsBetter":309,"n":405,"nUpTo":393,"median":332,"min":332,"max":332,"byClaimant":411,"vendorOnly":309,"points":412},"Contact deflection",{"organization":393,"vendor":405,"regulator":393,"independent":393},[413],{"evidenceId":342,"organization":320,"value":332,"qualifier":334,"claimant":306,"grade":284,"pooled":309},{"kpi":49,"label":415,"unit":333,"aggregate":309,"higherIsBetter":309,"n":405,"nUpTo":393,"median":362,"min":362,"max":362,"byClaimant":416,"vendorOnly":309,"points":417},"Customer satisfaction",{"organization":393,"vendor":405,"regulator":393,"independent":393},[418],{"evidenceId":368,"organization":347,"value":362,"qualifier":334,"claimant":306,"grade":284,"pooled":309},{"low":420,"high":421},200000,980000,[423,446,465,481,502],{"slug":191,"title":424,"shortTitle":425,"definition":426,"status":9,"industries":427,"functions":428,"patterns":430,"audience":33,"autonomy":34,"adoptionStage":432,"evidenceCount":433,"publicEvidenceCount":434,"organizations":435,"bestGrade":233,"headline":442,"lastVerified":196,"indexable":309},"AI assistant for citizen information and government services","Citizen information assistant","An AI assistant that answers residents' and businesses' questions about government services in plain language, grounded only in official guidance with links to the source, points them to the right online service or office, and hands anything personal, urgent or outside its content to a human with the context attached.",[17],[19,20,429],"knowledge-management",[431,23,24,25],"rag-knowledge-assistant","mainstream",11,9,[263,436,437,438,439,164,440,441,347],"Driver and Vehicle Licensing Agency","Estonian Information System Authority (RIA)","Foreign, Commonwealth and Development Office","Gemeente Tilburg","Government of the City of Buenos Aires","Madrid Destino",{"kpi":47,"label":404,"unit":333,"n":405,"nUpTo":393,"kind":443,"value":444,"qualifier":304,"claimant":445,"organization":438,"vendorReported":208},"reported",76,"organization",{"slug":192,"title":447,"shortTitle":448,"definition":449,"status":9,"industries":450,"functions":452,"patterns":454,"audience":459,"autonomy":460,"adoptionStage":35,"evidenceCount":461,"publicEvidenceCount":461,"organizations":462,"bestGrade":233,"headline":235,"lastVerified":196,"indexable":309},"AI support for emergency call triage (112 and 911)","Emergency call triage support","AI that supports emergency call takers and dispatchers during 112 and 911 calls, with live transcription, translation, summaries, location cues and alerts for critical conditions such as cardiac arrest, while the call taker keeps every triage and dispatch decision.",[17,451],"healthcare",[19,453],"operations",[455,456,457,458],"speech-analytics","translation","summarization","prediction-and-scoring","employee-facing","assist",3,[463,464,320],"Baltimore City 911 (Emergency Communications)","Copenhagen Emergency Medical Services",{"slug":193,"title":466,"shortTitle":467,"definition":468,"status":9,"industries":469,"functions":470,"patterns":471,"audience":33,"autonomy":473,"adoptionStage":35,"evidenceCount":474,"publicEvidenceCount":474,"organizations":475,"bestGrade":233,"headline":235,"lastVerified":197,"indexable":309},"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.",[17],[19,20,453],[456,23,455,472],"document-processing","copilot",8,[463,476,477,478,479,441,347,480],"Delaware County","European Commission","Federal Emergency Management Agency","Internal Revenue Service","U.S. Department of State (Bureau of Consular Affairs)",{"slug":194,"title":482,"shortTitle":483,"definition":484,"status":9,"industries":485,"functions":489,"patterns":490,"audience":491,"autonomy":34,"adoptionStage":432,"segment":491,"evidenceCount":492,"publicEvidenceCount":492,"organizations":493,"bestGrade":233,"headline":500,"lastVerified":196,"indexable":309},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI that sorts inbound correspondence before anyone answers it: it takes every inbound letter, email, upload and secure message into one intake, identifies what it is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right team or workflow, replacing the manual sorting desk.",[486,487,488,17],"cross-industry","banking","insurance",[453,20,21],[25,472,457],"back-office",6,[494,495,496,497,498,499],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":47,"label":404,"unit":333,"n":405,"nUpTo":393,"kind":443,"value":501,"qualifier":334,"claimant":306,"organization":498,"vendorReported":309},91,{"slug":195,"title":503,"shortTitle":504,"definition":505,"status":9,"industries":506,"functions":512,"patterns":513,"audience":33,"autonomy":34,"adoptionStage":432,"segment":514,"evidenceCount":515,"publicEvidenceCount":516,"organizations":517,"bestGrade":233,"headline":535,"lastVerified":196,"indexable":309},"AI agent for first line contact centre service","First line contact centre","An AI agent that answers the first line of inbound customer contact on phone, chat and messaging, resolves general and routine questions end to end in the customer's own language, and routes everything complex, sensitive or regulated to the right human team with the context attached.",[486,487,507,508,509,510,511],"payments","telecommunications","travel-and-hospitality","retail-and-ecommerce","wealth-and-asset-management",[20],[23,24,431,25],"front-office",25,18,[518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534],"Air India","Airbnb","Bank of America","Bank of the Philippine Islands","BT Group","Commonwealth Bank of Australia","Ingka Group","JetBlue","Klarna","Lufthansa Group","Mobily","NatWest Group","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany","Vodafone",{"kpi":536,"label":537,"unit":333,"n":76,"nUpTo":393,"kind":538,"value":539,"qualifier":334,"claimant":235,"organization":235,"vendorReported":208},"containment-rate","Containment rate","median",47,{"indexable":309,"reasons":541},[],[543,548,553,560,566,572,578,584,592,598,605,611,616,623,629,634,641,647,653,659,664,670,676,681,686,692,698,703,708,715,721,727,733,738],{"id":145,"label":544,"issuer":154,"region":155,"url":545,"description":546,"useCases":547,"indexable":309},"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":146,"label":549,"issuer":154,"region":155,"url":550,"description":551,"useCases":552,"indexable":309},"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":148,"label":554,"issuer":555,"region":556,"url":557,"description":558,"useCases":559,"indexable":309},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":147,"label":561,"issuer":562,"region":322,"url":563,"description":564,"useCases":565,"indexable":309},"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":567,"label":568,"issuer":154,"region":155,"url":569,"description":570,"useCases":571,"indexable":309},"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":149,"label":573,"issuer":574,"region":155,"url":575,"description":576,"useCases":577,"indexable":309},"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":579,"label":580,"issuer":581,"region":155,"url":582,"description":583,"useCases":539,"indexable":309},"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.",{"id":585,"label":586,"issuer":587,"region":588,"url":589,"description":590,"useCases":591,"indexable":309},"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":593,"label":594,"issuer":595,"region":588,"url":596,"description":597,"useCases":515,"indexable":309},"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.",{"id":599,"label":600,"issuer":601,"region":556,"url":602,"description":603,"useCases":604,"indexable":309},"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":606,"label":607,"issuer":608,"region":322,"url":609,"description":610,"useCases":604,"indexable":309},"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":150,"label":612,"issuer":613,"region":155,"url":165,"description":614,"useCases":615,"indexable":309},"UK Algorithmic Transparency Recording Standard","UK Government","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":617,"label":618,"issuer":619,"region":556,"url":620,"description":621,"useCases":622,"indexable":309},"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":624,"label":625,"issuer":154,"region":155,"url":626,"description":627,"useCases":628,"indexable":309},"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":630,"label":631,"issuer":154,"region":155,"url":632,"description":633,"useCases":628,"indexable":309},"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":635,"label":636,"issuer":637,"region":322,"url":638,"description":639,"useCases":640,"indexable":309},"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":642,"label":643,"issuer":154,"region":155,"url":644,"description":645,"useCases":646,"indexable":309},"eu-accessibility-act","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":648,"label":649,"issuer":650,"region":322,"url":651,"description":652,"useCases":646,"indexable":309},"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":654,"label":655,"issuer":656,"region":556,"url":657,"description":658,"useCases":646,"indexable":309},"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":660,"label":661,"issuer":154,"region":155,"url":662,"description":663,"useCases":433,"indexable":309},"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.",{"id":665,"label":666,"issuer":667,"region":322,"url":668,"description":669,"useCases":433,"indexable":309},"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":671,"label":672,"issuer":587,"region":588,"url":673,"description":674,"useCases":675,"indexable":309},"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.",10,{"id":677,"label":678,"issuer":154,"region":155,"url":679,"description":680,"useCases":675,"indexable":309},"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":682,"label":683,"issuer":154,"region":155,"url":684,"description":685,"useCases":675,"indexable":309},"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":687,"label":688,"issuer":689,"region":155,"url":690,"description":691,"useCases":434,"indexable":309},"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.",{"id":693,"label":694,"issuer":695,"region":322,"url":696,"description":697,"useCases":474,"indexable":309},"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":699,"label":700,"issuer":154,"region":155,"url":701,"description":702,"useCases":474,"indexable":309},"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":704,"label":705,"issuer":154,"region":155,"url":706,"description":707,"useCases":492,"indexable":309},"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.",{"id":709,"label":710,"issuer":711,"region":265,"url":712,"description":713,"useCases":714,"indexable":309},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":716,"label":717,"issuer":718,"region":155,"url":719,"description":720,"useCases":75,"indexable":309},"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":722,"label":723,"issuer":724,"region":155,"url":725,"description":726,"useCases":75,"indexable":309},"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":728,"label":729,"issuer":730,"region":588,"url":731,"description":732,"useCases":461,"indexable":309},"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.",{"id":734,"label":735,"issuer":154,"region":155,"url":736,"description":737,"useCases":461,"indexable":309},"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":739,"label":740,"issuer":741,"region":322,"url":742,"description":743,"useCases":461,"indexable":309},"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.",1790598300720]