[{"data":1,"prerenderedAt":827},["ShallowReactive",2],{"uc-citizen-information-assistant":3,"uc-regulations":622},{"useCase":4,"evidence":216,"blitsAiDeployments":302,"benchmarks":484,"indicative":519,"related":522,"indexability":620,"includeUnpublished":222},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":22,"channels":27,"audience":32,"autonomy":33,"adoptionStage":34,"problem":35,"problemStats":36,"howItWorks":37,"valueDrivers":38,"kpis":43,"indicativeValue":50,"macroEstimates":85,"feasibility":86,"implementation":98,"risk":144,"blitsAi":189,"faq":191,"related":204,"datePublished":211,"dateModified":211,"lastVerified":211,"changelog":212,"slug":215},"AI assistant for citizen information and government services","Citizen information assistant","Government AI chatbot for citizen information","AI assistants that answer residents from official guidance and hand personal cases to staff. DVLA's bot automates around 20% of its web chat enquiries.","published","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.",[12,13,14,15],"government chatbot","citizen services chatbot","public sector virtual assistant","GOV.UK Chat style assistant",[17],"government",[19,20,21],"citizen-services","customer-service","knowledge-management",[23,24,25,26],"rag-knowledge-assistant","conversational-agent","voice-agent","classification-and-routing",[28,29,30,31],"web-chat","mobile-app","whatsapp","voice","customer-facing","supervised-agent","mainstream","Government information is spread over thousands of pages written by different departments, and\npeople rarely know which agency owns their problem. They phone or visit because they cannot find\nor trust the answer online, so contact centres answer many questions the website already\ncovers: which form, which deadline, which office, what does this letter mean.\nQueues grow at exactly the moments demand spikes, such as tax deadlines, benefit changes or an\nemergency.\n\nKeyword search and scripted FAQ bots only help people who already know the official term.\nGenerative assistants can read a question in everyday words and synthesise an answer across\npages, but a wrong answer from a government channel carries more weight than a wrong answer from\na shop. The work is in grounding, refusal, privacy and a clean route to a human.",[],"1. **Understand the question.** The assistant reads the question in the resident's own words and\n   language, and classifies it (information request, personal case question, urgent need,\n   complaint, out of scope).\n2. **Retrieve from official content only.** It searches an index of approved guidance pages and\n   answers from the retrieved passages, with links to every source, and refuses when the content\n   does not cover the question.\n3. **Protect personal data.** Personal data in the question is detected and masked or the question\n   is rejected; nothing is used to profile the resident.\n4. **Check the answer.** A second check screens the draft for unsupported claims, advice the\n   government cannot give, tone and personal data before it is shown.\n5. **Route what it cannot answer.** Case specific questions go to the authenticated service or a\n   human adviser with the conversation attached; urgent needs are pointed to the right phone line.",[39,40,41,42],"inclusion-and-access","customer-experience","cost-to-serve","speed",[44,45,46,47,48,49],"containment-rate","interactions-handled","users-served","accuracy","customer-satisfaction","time-saved-per-task",{"referenceOrg":51,"inputs":52,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A national agency that receives 2 million phone and chat contacts a year",[53,59,66,73],{"key":54,"label":55,"low":56,"high":56,"unit":57,"note":58},"contacts","Assisted phone and chat contacts per year",2000000,"contacts per year","The reference agency.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"infoShare","Share of contacts that are general information questions",0.3,0.5,"fraction of contacts","Editorial assumption. Replace with your own contact reason analysis.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"containment","Share of those questions the assistant resolves without a human",0.2,0.4,"fraction of information contacts","The low end follows the benchmark on this page (DVLA reports around 20% of web chat enquiries automated by its scripted, non generative bot); the high end is an editorial assumption for a grounded generative assistant. DVLA's figure is a share of all web chat enquiries, not of information questions only, so the two bases differ. Replace with your own pilot data.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"costPerContact","Cost of a human handled contact",4,8,"USD per contact","Editorial assumption for a blended phone and chat contact in the public sector. Replace with your own fully loaded cost.","contacts * infoShare * containment * costPerContact","USD","per year","Human handled contact cost avoided","Gross avoided cost only. It leaves out the cost of building and running the assistant, content maintenance, the value of 24/7 access and the extra demand an easier channel can create.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":93},"low","A grounded information assistant touches no transactional systems, so the build is mostly content and evaluation work: cleaning the guidance corpus, deciding what it must refuse and testing accuracy by topic. Complexity rises once it reads personal case data or books services.",[90,91,92],"A current, owned corpus of official guidance with publication and review dates","Contact reason data from the contact centre to choose the first topics","A set of real questions with approved ideal answers for evaluation",[94,95,96,97],"Content management system or publishing feed, so the index updates when guidance changes","Contact centre or webchat platform for handover with the transcript","Web, app, messaging and telephony channels","Analytics for unanswered questions and feedback",{"steps":99,"guardrails":118,"humanInTheLoop":124,"kpisToInstrument":125,"failureModes":131},[100,103,106,109,112,115],{"title":101,"detail":102},"Choose topics by volume and harm","Start with high volume, low harm information topics (opening hours, which form, how to apply) and keep personal case questions, legal advice and urgent needs out of scope at first.",{"title":104,"detail":105},"Clean and filter the content","Index only official pages, exclude documents likely to contain personal data, and chunk by heading so answers can cite the exact section. Connect the index to publishing so it updates daily.",{"title":107,"detail":108},"Write the refusal and routing rules","Decide what the assistant must never do (give personal advice, guess eligibility, answer outside the content) and where each type of refused question goes.",{"title":110,"detail":111},"Build an evaluation set before launch","Collect real questions with ideal answers per topic, add adversarial and jailbreak prompts, and score groundedness, accuracy and completeness on every change.",{"title":113,"detail":114},"Pilot with a capped group","Run a limited test (the GOV.UK Chat transparency record describes a test with up to 2,000 users over four weeks), review transcripts, then widen channel by channel.",{"title":116,"detail":117},"Measure unanswered questions, not only usage","Track refusals, handovers and repeat contacts per topic and feed gaps back to the content owners.",[119,120,121,122,123],"Answers only from retrieved official content, with a link to every source and a refusal when retrieval finds nothing relevant","Personal data detection on input, with masking or rejection before text reaches a model","A second check on every answer for unsupported claims, advice and tone before display","Clear AI disclosure and a visible route to a human or the official phone line","No decisions about eligibility, entitlement or enforcement","Content owners approve the corpus and the topics in scope; a service team reviews samples of conversations every week, with extra review for refusals and complaints. Advisers take every handover with the transcript, and changes to scope go through the same sign off as a change to published guidance.",[126,127,128,129,130],"Share of questions answered from content, refused and handed over, per topic","Accuracy and groundedness on a weekly human reviewed sample","Repeat contact on the same topic within seven days","Satisfaction and feedback on answers, compared with search and phone","Contact centre volume on the topics in scope",[132,135,138,141],{"title":133,"detail":134},"Confident answers that break the law","New York City's MyCity chatbot was reported in 2024 to tell businesses they could do things that are illegal. Ground strictly, refuse outside the content and test legal edge cases before launch.",{"title":136,"detail":137},"Stale guidance in the index","The assistant repeats last year's rules after a policy change. Tie the index to the publishing pipeline and show the source date.",{"title":139,"detail":140},"Personal questions answered generically","Residents ask about their own case and get a generic answer that sounds personal. Detect case questions and route them to the authenticated service.",{"title":142,"detail":143},"Usage mistaken for success","High conversation numbers can hide abandonment. Measure unanswered questions and repeat contacts. Microsoft's case study on Montgomery County's Monty 2.0 reports a drop in the share of unanswered queries from between 35% and 45% to between 10% and 15%, next to its conversation count.",{"euAiAct":145,"regulations":148,"guidance":155,"controls":178,"incidents":184},{"tier":146,"basis":147},"limited","An information assistant must tell people they are interacting with AI (Article 50). It is not high risk as long as it does not evaluate eligibility for public assistance benefits or services (Annex III point 5(a)); an assistant that starts to pre assess eligibility should be reassessed.",[149,150,151,152,153,154],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001","uk-gdpr","uk-atrs",[156,162,167,172],{"title":157,"issuer":158,"region":159,"url":160,"note":161},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","People must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":163,"issuer":164,"region":159,"url":165,"note":166},"AI Playbook for the UK Government","UK Government","https://www.gov.uk/government/publications/ai-playbook-for-the-uk-government","Guidance for civil servants and people working in government organisations on using AI, including generative AI, safely, effectively and securely.",{"title":168,"issuer":169,"region":159,"url":170,"note":171},"Algorithmic Transparency Recording Standard Hub","Government Digital Service","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory for all UK government departments and for arm's length bodies that deliver public or frontline services; the GOV.UK Chat, DVLA and FCDO records on this page were published under it.",{"title":173,"issuer":174,"region":175,"url":176,"note":177},"M-25-21: Accelerating Federal Use of AI through Innovation, Governance, and Public Trust","US Office of Management and Budget","north-america","https://www.whitehouse.gov/wp-content/uploads/2025/02/M-25-21-Accelerating-Federal-Use-of-AI-through-Innovation-Governance-and-Public-Trust.pdf","Requires US federal agencies to inventory their AI use cases at least annually and to apply minimum risk management practices to high impact AI.",[179,180,181,182,183],"AI disclosure and a statement that answers may be wrong, with links to the official source","Entry in the public AI or algorithm register (for example ATRS in the UK or the Dutch algorithm register)","Data protection impact assessment covering question logs and retention","Red teaming for jailbreaks and legal edge cases before launch and after model changes","Retention limits on conversation logs and access control for reviewers",[185],{"title":186,"url":187,"note":188},"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 March 2024 The Markup found New York City's MyCity business chatbot giving answers that contradicted local law on housing, worker rights and consumer protection, for example that bosses could take workers' tips.",{"howToBuild":190},"On Blits.ai this is an **AI agent** grounded in a **knowledge base** that holds only official\nguidance, ingested from documents and **crawled websites** that can be recrawled, and retrieved\nwith **hybrid search**, so every answer can cite its source. **Guardrails** check input and\noutput, the platform default blocks prompt injection, and **PII masking** at the gateway removes\npersonal data before text reaches a model. Fixed journeys such as \"which office do I need\"\nrun as a **flow**, with language detection and translation blocks for multilingual service.\n\nThe same agent serves **web chat, WhatsApp and voice**, reaches a mobile app through the **REST\nor WebSocket API channel**, and **human handover** passes the conversation to the contact\ncentre. **Test suites** replay real questions with ideal answers on every content or model\nchange, **monitors** check live answers on a schedule, and **analytics** break down untrained\nquestions and unexpected answers. The platform is **model agnostic** and runs\nin **EU and UAE data residency** regions, which matters for public sector hosting rules.",[192,195,198,201],{"question":193,"answer":194},"Can a government chatbot use generative AI safely?","Yes, if it answers only from official content and shows its sources. GOV.UK Chat retrieves from GOV.UK pages, rejects questions that contain common personal data patterns and runs every answer through a second model check; FCDO's consular triage goes further and only picks from approved templates, so users never see generated text.",{"question":196,"answer":197},"What share of questions can it handle?","Reported figures are modest but real. DVLA automates around 20% of web chat enquiries, and FCDO's triage picked the correct response for 76% to 81% of historic enquiries in testing. Treat any number as topic specific and measure it on your own questions.",{"question":199,"answer":200},"Is a citizen information chatbot high risk under the EU AI Act?","Not by default. It carries the Article 50 transparency duty. It becomes high risk when it evaluates eligibility for public assistance benefits or services, which is why eligibility questions should route to the official service.",{"question":202,"answer":203},"Should every agency build its own assistant?","Not necessarily. Estonia's Bürokratt is one assistant, developed under the Information System Authority, that about 20 public bodies and websites use, from the state portal to the Tax and Customs Board. A shared platform means each agency does not have to build, secure and evaluate its own assistant.",[205,206,207,208,209,210],"benefits-eligibility-and-application-assistant","non-emergency-service-request-routing","tax-questions-and-filing-assistant","public-service-translation","immigration-and-visa-application-assistant","permit-and-licence-application-processing","2026-09-27",[213],{"date":211,"note":214},"First published","citizen-information-assistant",[217,246,269,323,345,375,400,431,457],{"title":218,"useCases":219,"organization":220,"vendors":224,"summary":228,"stage":229,"year":230,"channels":231,"languages":232,"metrics":234,"outcomeDisclosed":222,"sources":235,"verification":240,"grade":243,"id":244,"organizationSlug":245},"Estonia: Bürokratt, a shared virtual assistant for public sector organizations",[215],{"name":221,"anonymized":222,"country":223,"region":159,"industry":17},"Estonian Information System Authority (RIA)",false,"EE",[225],{"name":226,"role":227},"Estonian Information System Authority","in-house","Bürokratt is a virtual assistant for citizens that understands everyday Estonian and is available around the clock. Its development is managed by the Information System Authority, and it is built for local authorities and government agencies to manage enquiries, automate frequently asked questions and provide customer support. The authority lists about 20 organizations and websites that use it, including the state portal eesti.ee, the Estonian Tax and Customs Board, the Estonian Health Insurance Fund, the Police and Border Guard Board, the Consumer Protection and Technical Regulatory Authority and a legal information bot of the Ministry of Justice and Digital Affairs.","production",2026,[28],[233],"et",[],[236],{"url":237,"title":238,"publisher":226,"date":239},"https://www.ria.ee/en/state-information-system/artificial-intelligence/burokratt","Bürokratt","2026-05-20",{"level":241,"checkedAt":242},"source-verified","2026-09-26","B","estonian-information-system-authority-burokratt",null,{"title":247,"useCases":248,"organization":249,"vendors":252,"summary":256,"stage":229,"year":230,"channels":257,"languages":258,"metrics":260,"outcomeDisclosed":222,"sources":261,"verification":267,"grade":243,"id":268,"organizationSlug":245},"Gemeente Tilburg: Vragen.AI answers on the municipal website",[215],{"name":250,"anonymized":222,"country":251,"region":159,"industry":17},"Gemeente Tilburg","NL",[253],{"name":254,"role":255},"Swis (Vragen.ai)","platform","The municipality of Tilburg uses Vragen.AI, an AI search and answer function supplied by Swis, on its websites such as Tilburg Helpt, registered in the Dutch national algorithm register with a start date of September 2026. Answers come only from information already on the connected sites and always cite their source; personal data that residents type by mistake is detected and removed, and questions are not stored to help returning visitors or passed on to the AI companies. Staff do not watch conversations live but review the answers to improve the website. The register names the spreading of wrong information as a major risk. Several other Dutch municipalities (for example Doetinchem and Wijchen) register similar website chatbots grounded in their own pages.",[28],[259],"nl",[],[262],{"url":263,"title":264,"publisher":265,"date":266},"https://algoritmes.overheid.nl/nl/algoritme/gm0855/44521279/vragenai","Vragen.AI, Gemeente Tilburg","Algoritmeregister van de Nederlandse overheid","2026-09-24",{"level":241,"checkedAt":242},"gemeente-tilburg-vragen-ai",{"title":270,"useCases":271,"organization":272,"vendors":275,"summary":281,"stage":282,"year":283,"channels":284,"languages":285,"metrics":287,"outcomeDisclosed":312,"sources":313,"verification":321,"grade":243,"id":322,"organizationSlug":245},"DVLA: natural language IVR and web chat bot in the contact centre",[215],{"name":273,"anonymized":222,"country":274,"region":159,"industry":17},"Driver and Vehicle Licensing Agency","GB",[276,279],{"name":277,"role":278},"Content Guru","integrator",{"name":280,"role":255},"Google (Dialogflow)","The Driver and Vehicle Licensing Agency (DVLA), which handles driving licence and vehicle enquiries such as vehicle tax, asks phone callers to say what their enquiry is about and routes them by intent to a recorded answer, an SMS link to a GOV.UK service or the right adviser. On web chat, a bot answers general enquiries with scripted responses and gathers diagnostic information before handing over to an adviser. Both run on Google Dialogflow inside the Content Guru Storm contact centre platform. The records state that neither uses generative AI to write answers (every response is configured by DVLA staff) and that neither makes significant decisions about customers. The natural language IVR cut the average time a caller spends in the IVR menus by half (90 seconds), a navigation time rather than an adviser handling time.","scaled",2025,[31,28],[286],"en",[288,296,301,307],{"kpi":44,"value":289,"unit":290,"qualifier":291,"period":292,"claimant":293,"quote":294,"sourceUrl":295},20,"percent","approximately","web chat enquiries","organization","Automation of around 20% of customer enquiries on the web chat channel through bot generated auto responses.","https://www.gov.uk/algorithmic-transparency-records/dvla-contact-centre-chatbot-service",{"kpi":46,"value":297,"unit":298,"qualifier":291,"period":299,"claimant":293,"quote":300,"sourceUrl":295},300000,"count","per month, web chat bot","Around 300k customers access the chat bot every month.",{"kpi":49,"value":302,"unit":303,"qualifier":291,"period":304,"baseline":305,"claimant":293,"quote":306,"sourceUrl":295},2,"minutes","adviser chat handling time per handed over chat","advisers asking for the diagnostic information themselves","Reduction in an advisor's chat handling time of around 2 minutes linked to chat bot data capture (minimising the advisor time asking for this).",{"kpi":46,"value":308,"unit":298,"qualifier":291,"period":309,"claimant":293,"quote":310,"sourceUrl":311},900000,"per month, natural language IVR","Around 900k customers access the IVR every month.","https://www.gov.uk/algorithmic-transparency-records/dvla-contact-centre-natural-language-ivr",true,[314,318],{"url":295,"title":315,"publisher":316,"date":317},"DVLA: Contact Centre Chatbot service","GOV.UK (Algorithmic Transparency Recording Standard)","2025-12-16",{"url":311,"title":319,"publisher":316,"date":320},"DVLA: Contact Centre Natural Language IVR","2026-04-07",{"level":241,"checkedAt":211},"dvla-contact-centre-conversational-ai",{"title":324,"useCases":325,"organization":326,"vendors":327,"summary":333,"stage":334,"year":283,"channels":335,"languages":336,"metrics":337,"outcomeDisclosed":222,"sources":338,"verification":343,"grade":243,"id":344,"organizationSlug":245},"Government Digital Service: GOV.UK Chat in the GOV.UK app",[215],{"name":169,"anonymized":222,"country":274,"region":159,"industry":17},[328,331],{"name":329,"role":330},"Anthropic","model-provider",{"name":332,"role":255},"Amazon Web Services","GOV.UK Chat is a retrieval augmented generation assistant in the GOV.UK app that answers questions from GOV.UK guidance only, with links to the source pages under every answer. It retrieves from a vector index of roughly 100,000 GOV.UK pages, uses Claude Sonnet 4 on Amazon Bedrock in the EU, rejects questions that contain common personal data patterns, and runs every answer through a second model check on advice, language, tone and quality before the user sees it. The transparency record describes a limited test with up to 2,000 users over four weeks; it makes no decisions about users.","pilot",[29],[286],[],[339],{"url":340,"title":341,"publisher":316,"date":342},"https://www.gov.uk/algorithmic-transparency-records/dsit-gov-dot-uk-chat","DSIT: GOV.UK Chat","2025-10-07",{"level":241,"checkedAt":242},"government-digital-service-gov-uk-chat",{"title":346,"useCases":347,"organization":348,"vendors":350,"summary":357,"stage":334,"year":358,"channels":359,"languages":360,"metrics":361,"outcomeDisclosed":312,"sources":369,"verification":373,"grade":243,"id":374,"organizationSlug":245},"FCDO: LLM triage of written consular enquiries",[215],{"name":349,"anonymized":222,"country":274,"region":159,"industry":17},"Foreign, Commonwealth and Development Office",[351,353,355],{"name":352,"role":278},"Caution Your Blast Ltd",{"name":354,"role":330},"Microsoft (Azure OpenAI Service, GPT-4 and GPT-3.5 Turbo)",{"name":356,"role":278},"Kainos","British nationals abroad who write to the FCDO first see a triage tool that matches their question to approved guidance templates, such as how to replace a lost passport or what documents are needed to marry abroad. Personal data is removed with Azure services, a model splits the message into component questions, an embedding search shortlists templates and a second model picks the best template identifier, so the user never sees generated text. Users can still send the written enquiry to staff, and the FCDO says it will review each such case against the tool's answer; urgent cases are pointed to the consular contact centre. The record lists the tool's phase as beta or pilot.",2024,[28],[286],[362],{"kpi":47,"value":363,"unit":290,"qualifier":364,"period":365,"baseline":366,"claimant":293,"quote":367,"sourceUrl":368},76,"at-least","offline test on batches of historic anonymised enquiries (76% to 81%)","templates chosen by FCDO staff","Depending on the batch, the tool provided the correct response for 76% to 81% of enquiries.","https://www.gov.uk/algorithmic-transparency-records/fcdo-consular-digital-triage-written-enquiries-llm",[370],{"url":368,"title":371,"publisher":316,"date":372},"FCDO: Consular Digital Triage, Written Enquiries LLM","2024-12-17",{"level":241,"checkedAt":242},"fcdo-consular-enquiry-triage",{"title":376,"useCases":377,"organization":378,"vendors":382,"summary":387,"stage":282,"year":283,"channels":388,"languages":389,"metrics":390,"outcomeDisclosed":222,"sources":391,"verification":397,"grade":398,"id":399,"organizationSlug":245},"Abu Dhabi Government: TAMM AI assistant for government services",[215,206],{"name":379,"anonymized":222,"country":380,"region":381,"industry":17},"Abu Dhabi Government (TAMM)","AE","middle-east",[383,385],{"name":384,"role":330},"Microsoft (Azure OpenAI Service)",{"name":386,"role":255},"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.",[29,28],[],[],[392],{"url":393,"title":394,"publisher":395,"date":396},"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":241,"checkedAt":242},"C","abu-dhabi-tamm-ai-assistant",{"title":401,"useCases":402,"organization":403,"vendors":407,"summary":411,"stage":282,"year":358,"channels":412,"languages":413,"metrics":415,"outcomeDisclosed":312,"sources":421,"verification":429,"grade":398,"id":430,"organizationSlug":245},"Government of the City of Buenos Aires: Boti citizen chatbot",[215],{"name":404,"anonymized":222,"country":405,"region":406,"industry":17},"Government of the City of Buenos Aires","AR","latin-america",[408,409],{"name":384,"role":255},{"name":410,"role":278},"Pi Data Strategy & Consulting","Boti is the city's WhatsApp assistant for residents and visitors, launched on WhatsApp in 2019, which the city describes as the first government in the world to use WhatsApp as a contact channel with its citizens. Its services include appointments for procedures such as driver's licence renewal, public transport schedules, requests such as bulky waste collection and, during the pandemic, vaccination appointments and test results. In 2024 the city added a generative AI experience built on Azure OpenAI Service, scoped to tourism so the team could experiment without sensitive data. The city stresses tone (River Plate voseo, inclusive language) and a single central repository of government information as the basis for grounded answers.",[30],[414,286],"es",[416],{"kpi":45,"value":56,"unit":298,"qualifier":364,"period":417,"claimant":418,"quote":419,"sourceUrl":420},"queries per month without human intervention","vendor","Boti, the AI-powered chatbot, handles over 2 million queries per month without human intervention.","https://www.microsoft.com/en/customers/story/21596-government-of-the-city-of-buenos-aires-azure-open-ai-service",[422,425],{"url":420,"title":423,"publisher":424},"Buenos Aires City: How generative AI is revolutionizing the lives of millions with Azure OpenAI Services","Microsoft Customer Stories",{"url":426,"title":427,"publisher":428},"https://buenosaires.gob.ar/boti","Boti","Gobierno de la Ciudad Autónoma de Buenos Aires",{"level":241,"checkedAt":211},"government-of-the-city-of-buenos-aires-boti",{"title":432,"useCases":433,"organization":435,"vendors":438,"summary":442,"stage":229,"year":358,"channels":443,"languages":444,"metrics":445,"outcomeDisclosed":222,"sources":446,"verification":455,"grade":398,"id":456,"organizationSlug":245},"City of Madrid (Madrid Destino): VisitMadridGPT multilingual visitor assistant",[208,215,434],"ai-visitor-and-tour-guide",{"name":436,"anonymized":222,"country":437,"region":159,"industry":17},"Madrid Destino","ES",[439,441],{"name":440,"role":278},"iUrban",{"name":384,"role":330},"Madrid Destino, the city's municipal tourism office, runs VisitMadridGPT, a virtual assistant that answers visitors in more than 95 languages from the city's official, expert curated tourism site, available when physical offices are closed. The city analyses the questions to find the most requested topics and adjust its website content. According to the story, Madrid attracted 10.6 million visitors in 2023.",[28],[],[],[447,450],{"url":448,"title":449,"publisher":424},"https://customers.microsoft.com/en-us/story/1831036907720807463-esmadrid-azure-openai-service-national-government-en-spain","Transforming tourism in Madrid with Azure OpenAI Service",{"url":451,"title":452,"publisher":453,"date":454},"https://www.eldiariodemadrid.es/articulo/madrid/visitmadridgpt-asistente-virtual-turistas-madrid/20240410141511074094.html","VisitMadridGPT, el asistente virtual para los turistas en Madrid","El Diario de Madrid","2024-04-10",{"level":241,"checkedAt":211},"madrid-destino-visitmadridgpt",{"title":458,"useCases":459,"organization":460,"vendors":463,"summary":467,"stage":229,"year":358,"channels":468,"languages":469,"metrics":470,"outcomeDisclosed":312,"sources":479,"verification":482,"grade":398,"id":483,"organizationSlug":245},"Montgomery County, Maryland: Monty 2.0 constituent chatbot",[215,206,208],{"name":461,"anonymized":222,"country":462,"region":175,"industry":17},"Montgomery County Government","US",[464,466],{"name":465,"role":255},"Zammo.ai",{"name":384,"role":330},"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.",[28],[286],[471,476],{"kpi":45,"value":472,"unit":298,"qualifier":364,"period":473,"claimant":418,"quote":474,"sourceUrl":475},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":48,"value":477,"unit":290,"qualifier":478,"period":473,"claimant":418,"quote":474,"sourceUrl":475},50,"exact",[480],{"url":475,"title":481,"publisher":424},"Montgomery County revolutionizes constituent experiences with an AI chatbot powered by Microsoft Azure OpenAI Service",{"level":241,"checkedAt":211},"montgomery-county-monty-chatbot",[485,493,499,504,509,514],{"kpi":45,"label":486,"unit":298,"aggregate":222,"higherIsBetter":312,"n":302,"nUpTo":487,"median":488,"min":472,"max":56,"byClaimant":489,"vendorOnly":312,"points":490},"Interactions handled",0,1010000,{"organization":487,"vendor":302,"regulator":487,"independent":487},[491,492],{"evidenceId":430,"organization":404,"value":56,"qualifier":364,"claimant":418,"grade":398,"pooled":312},{"evidenceId":483,"organization":461,"value":472,"qualifier":364,"claimant":418,"grade":398,"pooled":312},{"kpi":47,"label":494,"unit":290,"aggregate":312,"higherIsBetter":312,"n":495,"nUpTo":487,"median":363,"min":363,"max":363,"byClaimant":496,"vendorOnly":222,"points":497},"Accuracy",1,{"organization":495,"vendor":487,"regulator":487,"independent":487},[498],{"evidenceId":374,"organization":349,"value":363,"qualifier":364,"claimant":293,"grade":243,"pooled":312},{"kpi":44,"label":500,"unit":290,"aggregate":312,"higherIsBetter":312,"n":495,"nUpTo":487,"median":289,"min":289,"max":289,"byClaimant":501,"vendorOnly":222,"points":502},"Containment rate",{"organization":495,"vendor":487,"regulator":487,"independent":487},[503],{"evidenceId":322,"organization":273,"value":289,"qualifier":291,"claimant":293,"grade":243,"pooled":312},{"kpi":48,"label":505,"unit":290,"aggregate":312,"higherIsBetter":312,"n":495,"nUpTo":487,"median":477,"min":477,"max":477,"byClaimant":506,"vendorOnly":312,"points":507},"Customer satisfaction",{"organization":487,"vendor":495,"regulator":487,"independent":487},[508],{"evidenceId":483,"organization":461,"value":477,"qualifier":478,"claimant":418,"grade":398,"pooled":312},{"kpi":49,"label":510,"unit":303,"aggregate":312,"higherIsBetter":312,"n":495,"nUpTo":487,"median":302,"min":302,"max":302,"byClaimant":511,"vendorOnly":222,"points":512},"Time saved per task",{"organization":495,"vendor":487,"regulator":487,"independent":487},[513],{"evidenceId":322,"organization":273,"value":302,"qualifier":291,"claimant":293,"grade":243,"pooled":312},{"kpi":46,"label":515,"unit":298,"aggregate":222,"higherIsBetter":312,"n":495,"nUpTo":487,"median":297,"min":297,"max":297,"byClaimant":516,"vendorOnly":222,"points":517},"Users served",{"organization":495,"vendor":487,"regulator":487,"independent":487},[518],{"evidenceId":322,"organization":273,"value":297,"qualifier":291,"claimant":293,"grade":243,"pooled":312},{"low":520,"high":521},480000,3200000,[523,546,562,576,591,602],{"slug":205,"title":524,"shortTitle":525,"definition":526,"status":9,"industries":527,"functions":528,"patterns":530,"audience":32,"autonomy":532,"adoptionStage":533,"evidenceCount":534,"publicEvidenceCount":534,"organizations":535,"bestGrade":243,"headline":543,"lastVerified":242,"indexable":312},"AI assistant for benefits eligibility questions and applications","Benefits eligibility and application assistant","An AI assistant that helps people understand which public benefits and grants may apply to them, explains the rules and documents in plain language, guides them through the application and checks it for completeness, while the eligibility decision stays with the agency's rules and caseworkers.",[17],[19,529,20],"case-management",[24,23,25,531],"document-processing","copilot","early-adopters",7,[536,537,538,539,540,541,542],"Department for Work and Pensions","Federal Student Aid (U.S. Department of Education)","Federal Emergency Management Agency","Gemeente Nissewaard","Leeds City Council","Région Provence-Alpes-Côte d'Azur (Région Sud)","YoungWilliams",{"kpi":47,"label":494,"unit":290,"n":302,"nUpTo":487,"kind":544,"value":545,"qualifier":478,"claimant":293,"organization":536,"vendorReported":222},"reported",97,{"slug":206,"title":547,"shortTitle":548,"definition":549,"status":9,"industries":550,"functions":551,"patterns":552,"audience":32,"autonomy":33,"adoptionStage":533,"evidenceCount":534,"publicEvidenceCount":534,"organizations":554,"bestGrade":243,"headline":560,"lastVerified":242,"indexable":312},"AI for non emergency service requests and 311 routing","Non emergency service request routing","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.",[17],[19,20,529],[24,25,26,553],"agentic-workflow",[379,555,556,557,461,558,559],"London Borough of Barnet","City of Kelowna","Galt Police Department","Newcastle City Council","Rio de Janeiro City Data Office (Escritório de Dados)",{"kpi":47,"label":494,"unit":290,"n":495,"nUpTo":487,"kind":544,"value":561,"qualifier":478,"claimant":418,"organization":556,"vendorReported":312},80,{"slug":207,"title":563,"shortTitle":564,"definition":565,"status":9,"industries":566,"functions":567,"patterns":569,"audience":32,"autonomy":33,"adoptionStage":34,"evidenceCount":76,"publicEvidenceCount":76,"organizations":570,"bestGrade":243,"headline":574,"lastVerified":242,"indexable":312},"AI assistant for tax questions and filing support","Tax questions and filing assistant","An AI assistant that answers taxpayers' questions about taxes, deadlines, refunds and payments, lets authenticated taxpayers check their status or set up a payment plan within set rules, and guides them through filing, while assessments, penalties and disputes stay with the tax authority's staff and systems.",[17],[19,20,568],"finance-and-accounting",[24,25,23,26],[571,572,573],"ClearTax","HM Revenue and Customs","Internal Revenue Service",{"kpi":47,"label":494,"unit":290,"n":495,"nUpTo":487,"kind":544,"value":575,"qualifier":478,"claimant":293,"organization":572,"vendorReported":222},83.03,{"slug":208,"title":577,"shortTitle":578,"definition":579,"status":9,"industries":580,"functions":581,"patterns":583,"audience":32,"autonomy":532,"adoptionStage":533,"evidenceCount":77,"publicEvidenceCount":77,"organizations":586,"bestGrade":243,"headline":245,"lastVerified":242,"indexable":312},"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],[19,20,582],"operations",[584,24,585,531],"translation","speech-analytics",[587,588,589,538,573,436,461,590],"Baltimore City 911 (Emergency Communications)","Delaware County","European Commission","U.S. Department of State (Bureau of Consular Affairs)",{"slug":209,"title":592,"shortTitle":593,"definition":594,"status":9,"industries":595,"functions":596,"patterns":597,"audience":32,"autonomy":532,"adoptionStage":533,"evidenceCount":76,"publicEvidenceCount":76,"organizations":598,"bestGrade":243,"headline":245,"lastVerified":211,"indexable":312},"AI for immigration and visa applications, from applicant questions to case preparation","Immigration and visa application assistant","AI that helps applicants understand immigration and visa requirements and submit complete applications, and helps immigration staff prepare cases by extracting form data, classifying evidence, routing applications and supporting interviews, while every grant or refusal is decided by an officer against the immigration rules.",[17],[19,529,582],[24,531,26,584],[599,590,600,601],"Home Office (Visa, Status and Information Services)","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services",{"slug":210,"title":603,"shortTitle":604,"definition":605,"status":9,"industries":606,"functions":607,"patterns":609,"audience":610,"autonomy":532,"adoptionStage":611,"evidenceCount":612,"publicEvidenceCount":612,"organizations":613,"bestGrade":243,"headline":618,"lastVerified":242,"indexable":312},"AI for permit and licence application processing","Permit and licence application processing","AI that helps applicants submit complete permit and licence applications and helps officers process them, by answering questions about requirements, checking applications for missing or inconsistent information, pulling the relevant policies, history and constraints, and drafting reports, while the grant or refusal stays with a named officer or a published rule.",[17],[19,529,608],"regulatory-compliance",[531,553,23,24],"employee-facing","emerging",5,[614,540,615,616,617],"Intellectual Property Office","U.S. Fish and Wildlife Service","U.S. Department of Agriculture","West Berkshire Council",{"kpi":47,"label":494,"unit":290,"n":495,"nUpTo":487,"kind":544,"value":619,"qualifier":364,"claimant":293,"organization":540,"vendorReported":222},85,{"indexable":312,"reasons":621},[],[623,628,633,640,646,652,658,665,673,680,686,692,696,703,709,714,721,727,733,739,745,751,757,762,767,774,780,785,791,797,803,809,816,821],{"id":149,"label":624,"issuer":158,"region":159,"url":625,"description":626,"useCases":627,"indexable":312},"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":150,"label":629,"issuer":158,"region":159,"url":630,"description":631,"useCases":632,"indexable":312},"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":152,"label":634,"issuer":635,"region":636,"url":637,"description":638,"useCases":639,"indexable":312},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":151,"label":641,"issuer":642,"region":175,"url":643,"description":644,"useCases":645,"indexable":312},"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":647,"label":648,"issuer":158,"region":159,"url":649,"description":650,"useCases":651,"indexable":312},"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":153,"label":653,"issuer":654,"region":159,"url":655,"description":656,"useCases":657,"indexable":312},"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":659,"label":660,"issuer":661,"region":159,"url":662,"description":663,"useCases":664,"indexable":312},"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":666,"label":667,"issuer":668,"region":669,"url":670,"description":671,"useCases":672,"indexable":312},"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":674,"label":675,"issuer":676,"region":669,"url":677,"description":678,"useCases":679,"indexable":312},"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":681,"label":682,"issuer":683,"region":636,"url":684,"description":685,"useCases":289,"indexable":312},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":687,"label":688,"issuer":689,"region":175,"url":690,"description":691,"useCases":289,"indexable":312},"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":154,"label":693,"issuer":164,"region":159,"url":170,"description":694,"useCases":695,"indexable":312},"UK Algorithmic Transparency Recording Standard","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":697,"label":698,"issuer":699,"region":636,"url":700,"description":701,"useCases":702,"indexable":312},"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":704,"label":705,"issuer":158,"region":159,"url":706,"description":707,"useCases":708,"indexable":312},"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":710,"label":711,"issuer":158,"region":159,"url":712,"description":713,"useCases":708,"indexable":312},"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":715,"label":716,"issuer":717,"region":175,"url":718,"description":719,"useCases":720,"indexable":312},"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":722,"label":723,"issuer":158,"region":159,"url":724,"description":725,"useCases":726,"indexable":312},"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":728,"label":729,"issuer":730,"region":175,"url":731,"description":732,"useCases":726,"indexable":312},"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":734,"label":735,"issuer":736,"region":636,"url":737,"description":738,"useCases":726,"indexable":312},"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":740,"label":741,"issuer":158,"region":159,"url":742,"description":743,"useCases":744,"indexable":312},"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":746,"label":747,"issuer":748,"region":175,"url":749,"description":750,"useCases":744,"indexable":312},"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":752,"label":753,"issuer":668,"region":669,"url":754,"description":755,"useCases":756,"indexable":312},"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":758,"label":759,"issuer":158,"region":159,"url":760,"description":761,"useCases":756,"indexable":312},"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":763,"label":764,"issuer":158,"region":159,"url":765,"description":766,"useCases":756,"indexable":312},"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":768,"label":769,"issuer":770,"region":159,"url":771,"description":772,"useCases":773,"indexable":312},"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":775,"label":776,"issuer":777,"region":175,"url":778,"description":779,"useCases":77,"indexable":312},"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":781,"label":782,"issuer":158,"region":159,"url":783,"description":784,"useCases":77,"indexable":312},"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":786,"label":787,"issuer":158,"region":159,"url":788,"description":789,"useCases":790,"indexable":312},"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":792,"label":793,"issuer":794,"region":381,"url":795,"description":796,"useCases":612,"indexable":312},"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.",{"id":798,"label":799,"issuer":800,"region":159,"url":801,"description":802,"useCases":76,"indexable":312},"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":804,"label":805,"issuer":806,"region":159,"url":807,"description":808,"useCases":76,"indexable":312},"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":810,"label":811,"issuer":812,"region":669,"url":813,"description":814,"useCases":815,"indexable":312},"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":817,"label":818,"issuer":158,"region":159,"url":819,"description":820,"useCases":815,"indexable":312},"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":822,"label":823,"issuer":824,"region":175,"url":825,"description":826,"useCases":815,"indexable":312},"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.",1790598296557]