What problem does it solve?
Much inbound contact is general: opening hours and fees, how do I, where is my request, what does this letter mean. In a bank it is also time critical: a customer abroad without a local SIM, a card that does not work, a payment that has not arrived. Human agents answer the same questions all day while callers wait in a queue, and the conversations that need care (a bereavement, a scam victim, a complaint) wait behind them.
Touch tone menus and first generation chatbots did not fix this. Menus route by the option a caller picks, and those chatbots matched keywords to a fixed list of answers, so anything outside the list ends in a dead end or a transfer. The shift is an agent that understands free speech and text, answers from the organization's approved knowledge, can look up the customer's own case, and knows exactly when a human must take over.
How does it work?
- Greet and understand. The agent identifies the intent and language from the customer's first words, on the phone or in chat, and discloses that it is AI.
- Answer general questions from approved knowledge. Fees, procedures, product terms and service status come from retrieval over the organization's own content, with a refusal when the content does not cover the question.
- Look up what is personal. After authentication it checks the status of the customer's request, order or case through read only APIs and explains it.
- Route deliberately. Regulated journeys (disputes, fraud reports, complaints) follow a defined path; vulnerability, strong emotion or repeated failure trigger a human.
- Hand over with context. The human receives the transcript, a summary, the authenticated identity and what was already tried, so the customer does not repeat anything.
- Audience
- Customer facing
- Autonomy
- Supervised agent
- Adoption
- Mainstream
- Channels
- Phone and voice, Web chat, Mobile app, WhatsApp, Social messaging
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Containment rate | 47% | 44% to 84.6% | 7 | 2 organization, 5 vendor |
| Interactions handled | Not pooled | 10,000 to 3 billion Not pooled: up to 60,000 | 6plus 1 up to | 4 organization, 2 vendor |
| Response time reduction | Too few to pool | 82% to 99.5% | 2 | 1 organization, 1 vendor |
| First contact resolution | Too few to pool | 60% | 1 | 1 organization |
Value drivers: Lower cost to serve, Customer experience, Inclusion and access, Employee productivity.
Indicative value
A retail bank contact centre that receives 2 million contacts a year across phone and chat
USD 900,000 to USD 3.8 million
Human handled contact cost avoided per year
How this is calculated
Formula: contacts * generalShare * containment * costPerContact. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Inbound contacts per year contacts, contacts per year | 2,000,000 | 2,000,000 | The reference contact centre. |
| Share of contacts that are general or routine generalShare, fraction of contacts | 0.5 | 0.7 | Editorial assumption, replace with your own contact reason report. |
| Share of those contacts the agent resolves without a human containment, fraction of general contacts | 0.3 | 0.45 | The low end is conservative; the high end sits above most benchmarks on this page (44 to 47% for Airbnb, Ingka, JetBlue and Vodafone Germany), while a few operators report higher containment (66% at Together Credit Union, 70% for Vodafone TOBi, 84.6% for Commonwealth Bank's self service messaging in May 2026), because early months run lower. |
| Cost of a human handled contact costPerContact, USD per contact | 3 | 6 | Editorial assumption for a blended chat and phone contact. Replace with your own fully loaded cost. |
What it leaves out: Gross avoided contact cost only. It leaves out the cost of the AI and the integrations, the value of shorter waiting times, the cost of customers who give up instead of being helped, and any capacity the organization chooses to reinvest in human service rather than save.
Who already uses it?
18 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Airbnb
United States · Travel and hospitality · 2025
Airbnb runs an AI assistant as the first line of customer support for guests and hosts. It was expanded to all US users in 2025 and then rolled out to more countries and languages, reaching more than 50 languages by mid 2026. Airbnb reports the share of issues resolved without a human agent in each quarterly letter and links part of the fall in support cost per booking to the assistant; it plans an AI voice assistant.
- Containment rate: about 45%, Q2 2026, issues that begin with the AI assistant
"Nearly 45 percent of issues that begin with our AI assistant are now resolved without a human agent, up from Q1, while delivering much faster resolution times."
Claimed by: organization - Cost reduction: about 16%, Q2 2026 year on year, customer support cost per booking
"In Q2, our customer support related cost per booking declined approximately 16 percent year-over-year, driven in part by improvements to our AI assistant."
Claimed by: organization
Bank of America
United States · Banking · 2025
Erica, launched in 2018, is Bank of America's virtual financial assistant in its Mobile Banking app. Beyond answering questions it delivers proactive, personalized insights: BankAmeriDeals cash back deals based on the client's spending, where balances are trending over the next seven days and eligibility for the Preferred Rewards program. It also gives guidance on investment topics for Merrill clients and hands off to people by scheduling appointments. The bank reports that clients have received and interacted with more than 1.7 billion of these insights, and that most users find the information they need, which it links to lower call centre volume. Bank of America says Erica selects answers from a predefined set and does not use generative AI or large language models.
- Users served: about 50 million, since launch in 2018, as of August 2025
"assisting nearly 50 million users since launch, surpassing 3 billion client interactions, and now averaging more than 58 million interactions per month"
Claimed by: organization - Interactions handled: at least 3 billion, client interactions since launch in 2018, as of August 2025
"surpassing 3 billion client interactions"
Claimed by: organization
Bank of the Philippine Islands
Philippines · Banking · 2025
Bank of the Philippine Islands runs BEA Chat, a conversational AI assistant on its website and Facebook page that answers general inquiries around the clock, offers self service for common concerns, lets customers apply for products and track service requests, and escalates complex issues to live agents. It serves both logged in clients and guests, and the bank positions it as a channel for Filipinos working overseas who would otherwise need a local SIM card or international calls. No containment or cost outcome is disclosed.
No outcome disclosed.
NatWest Group
United Kingdom · Banking · 2025
NatWest routes a wide range of everyday customer queries through Cora, its AI assistant in online banking and the mobile app, now with generative AI (Cora+). Customers whose ATM withdrawal did not pay out are sent to Cora with the phrase "ATM dispute" as the first step of the claim, and the assistant is available before login as well. NatWest says the generative AI version improved customer satisfaction and reduced how often a colleague has to step in, and in 2025 it began a collaboration with OpenAI to extend the assistant to more complex tasks.
- Satisfaction uplift: 150%, Cora+ generative AI functionality
"The GenAI functionality offered by Cora+ has shown a 150% improvement in customer satisfaction, while reducing the number of times a colleague needs to intervene."
Claimed by: organization
BT Group
United Kingdom · Telecommunications · 2024
BT Group runs the EE virtual assistant Aimee on Sprinklr's customer experience platform, drawing on BT Group data for personalised answers. The platform lets BT Group use generative AI for EE and BT customers, for example in an Aimee journey that prepares customers for international travel and in billing support, where generative AI gives detailed explanations of billing charges. BT Group says Aimee handles up to 60,000 conversations a week, double the volume of two years earlier, that the travel journey halved the need for chat support, and that it stays model agnostic behind a private cloud instance with safeguards against attempts to make the AI misbehave.
- Interactions handled: up to 60,000, per week
"EE virtual assistant Aimee now handles up to 60,000 customer conversations per week, with automation success rates on several types of customer journey now approaching 50%, freeing time for guides to focus on more complex issues"
Claimed by: organization - Automation rate: up to 50%, several types of customer journey
"EE virtual assistant Aimee now handles up to 60,000 customer conversations per week, with automation success rates on several types of customer journey now approaching 50%, freeing time for guides to focus on more complex issues"
Claimed by: organization
Klarna
Sweden · Payments and cards · 2024
Klarna announced in February 2024 that its AI assistant built on OpenAI models had been live globally for a month as the first line of its customer service, handling refunds, returns, payment issues, cancellations and disputes in more than 35 languages across 23 markets. In 2025 the company said it had gone too far in replacing people and began recruiting human agents again so that customers can always reach a person; the assistant still handles the majority of inquiries. The record is useful precisely because it shows both the gain and the correction.
- Interactions handled: 2.3 million, first month after launch
"The AI assistant has had 2.3 million conversations, two-thirds of Klarna’s customer service chats"
Claimed by: organization - Response time reduction: 82%, since launch, as reported in 2025
"Since launch, response times have improved by 82%, and Klarna has seen a 25% drop in repeat issues."
Claimed by: organization
Vodafone
United Kingdom · Telecommunications · 2024
Vodafone rebuilt its TOBi chatbot on Azure OpenAI as SuperTOBi, launched in Italy and Portugal, with Germany and Turkey announced to follow from July 2024 and other markets later that year. A companion SuperAgent helps human agents search the company knowledge base and, in Ireland, sends the human agent a summary of the online customer conversation so customers do not repeat themselves. Vodafone reports that initial tests at one of its call centres showed a 50% improvement in first time resolution of critical journeys such as complex billing inquiries, and that in Portugal first time resolution on appointment booking rose from 15% to 60%, with billing journeys being added next.
- First contact resolution: 60%, appointment booking journey, Vodafone Portugal
"As a result, the first-time resolution rate has increased from 15% to 60% and Vodafone’s online net promoter scores (where respondents are asked to rate their experience) improved by 14 points to 64 points – anything above 50 points is considered a strong result."
Claimed by: organization - NPS change: +14 points, online NPS, Vodafone Portugal
"As a result, the first-time resolution rate has increased from 15% to 60% and Vodafone’s online net promoter scores (where respondents are asked to rate their experience) improved by 14 points to 64 points – anything above 50 points is considered a strong result."
Claimed by: organization
Ingka Group
Sweden · Retail and ecommerce · 2021
Ingka Group, the largest IKEA retailer, rolled out the AI chatbot Billie in its 2021 financial year to answer simpler customer enquiries around the clock. Between 2021 and 2023 Billie resolved about 47% of the enquiries it received. With the chatbot taking simpler enquiries, Ingka reskilled 8,500 call centre staff for remote interior design and remote selling, and sales through its remote customer meeting points reached EUR 1.3 billion in its 2022 financial year.
- Containment rate: about 47%, 2021 to 2023
"Since the rollout of the solution in FY21[1], Billie has continued to provide value, and from 2021 to 2023 it resolved approximately 47% of customer enquiries it received, which translates to 3,2 million interactions solved by the chatbot and nearly EUR 13 million in savings thus far."
Claimed by: organization - Interactions handled: about 3.2 million, resolved by the chatbot, 2021 to 2023
"Since the rollout of the solution in FY21[1], Billie has continued to provide value, and from 2021 to 2023 it resolved approximately 47% of customer enquiries it received, which translates to 3,2 million interactions solved by the chatbot and nearly EUR 13 million in savings thus far."
Claimed by: organization - Cost savings: about EUR 13 million, cumulative, 2021 to 2023
"Since the rollout of the solution in FY21[1], Billie has continued to provide value, and from 2021 to 2023 it resolved approximately 47% of customer enquiries it received, which translates to 3,2 million interactions solved by the chatbot and nearly EUR 13 million in savings thus far."
Claimed by: organization
Pegasus Airlines
Türkiye · Travel and hospitality · 2025
Pegasus Airlines retrained FlyBot, the virtual assistant on its website, with Azure OpenAI and integrated it with internal systems, so customers can ask about flights, flight rules, baggage allowances and claims and reissue tickets in the same conversation. The airline reports that satisfaction with the virtual assistant doubled after the change.
- Satisfaction uplift: 100%
"“Since we integrated Azure AI Services into our FlyBot, customer satisfaction rates for our virtual assistant have doubled,” points out Bora."
Claimed by: organization
Together Credit Union
United States · Banking · 2025
Together Credit Union runs a voice agent on its inbound phone line that answers member questions, resolves routine requests and hands the rest to a person, drawing on one knowledge base that the branch, contact centre and chat channels also use. The vendor reports that the agent now contains the majority of inbound calls, 12 points more than at the start, that after hours escalations to an outsourced contact centre fell, and that member satisfaction held above 94% during the rollout.
- Containment rate: 66%, total inbound calls, as reported by the vendor
"Heather, Together CU's Posh-powered voice agent, now contains 66% of total inbound calls"
Claimed by: vendor - Contact deflection: 14%, after hours escalations to the outsourced contact centre
"After-hours escalations to their third-party contact center dropped 14%."
Claimed by: vendor
Commonwealth Bank of Australia
Australia · Banking · 2024
Commonwealth Bank built a central AI orchestration agent that reads the customer's intent and routes it to a conversational AI, retrieval over public content, a deterministic guarded path for regulated journeys such as fraud disputes, or a human specialist with the full context, on its messaging channel. It migrated nearly 700 chatbot topics and launched a generative AI banking chatbot in November 2024. Voice bots are a planned extension of the orchestration layer.
- Containment rate: about 84.6%, May 2026, self service messaging
"In May 2026, approximately 84.6% of self-service messaging interactions were resolved end-to-end in the messaging channel."
Claimed by: vendor
Telkomsel
Indonesia · Telecommunications · 2024
Telkomsel, with more than 159 million mobile subscribers, rebuilt its Veronika virtual assistant on Azure OpenAI to handle routine customer questions in Bahasa Indonesia with the local accents and expressions its first chatbot could not follow, so that human agents could focus on complex issues. Telkomsel's chief information officer says the architecture can handle up to 5 million transactions a month. Telkomsel's chief marketing officer says customer self service interactions rose from 19% to 45% after the launch.
- Automation rate: 45%, share of customer interactions that were self service, as Telkomsel describes it
"“I’m thrilled to share that since introducing Veronika, we’ve seen a leap in customer self-service interactions from 19 percent to 45 percent,” enthuses Heng."
Claimed by: organization
Vodafone
United Kingdom · Telecommunications · 2024
TOBi is Vodafone's digital assistant on the website, the My Vodafone app, messaging and telephony, first launched in Italy and extended to 15 language versions. It handles billing questions, contract updates and simple technical troubleshooting, hands over to a live agent with a summary, and during the pandemic made the same sales offers as human agents, such as data boosts and upgrades. Microsoft reports that TOBi now fully resolves 70% of inquiries arriving through digital channels; an earlier Microsoft story quotes Vodafone on a 12% year on year fall in contacts to call centres after launch.
- Containment rate: 70%, inquiries arriving through digital channels
"Currently, TOBi handles nearly 45 million customer calls a month, fully resolving 70% of customer inquiries coming through the company’s digital channels."
Claimed by: vendor - Interactions handled: about 45 million, per month
"Currently, TOBi handles nearly 45 million customer calls a month, fully resolving 70% of customer inquiries coming through the company’s digital channels."
Claimed by: vendor - Contact deflection: 12%, frequency of customer contacts to call centres, year over year after the TOBi launch
"Since launching TOBi, we’ve reduced the frequency of customer contacts to call centers by 12 percent year-over-year"
Claimed by: organization
Air India
India · Travel and hospitality · 2023
Air India launched AI.g in May 2023, a virtual assistant on Azure OpenAI that is integrated with the reservation system and answers questions across 1,300 topic areas including bookings, flight status, baggage, check in, frequent flyer awards and lounge access, and escalates automatically to contact centre staff when it detects the need. The airline says it has kept contact centre call volume flat while its passenger count doubled.
- Automation rate: 97%, cumulative, of nearly 4 million queries
"To date, AI.g has successfully handled nearly 4 million customer queries, 97% of them with full automation."
Claimed by: vendor - Interactions handled: about 10,000, per day
"That's because AI.g is handling about 10,000 a day."
Claimed by: organization
Mobily
Saudi Arabia · Telecommunications · 2022
Mobily deployed customer facing AI agents on eight channels, including WhatsApp, Twitter and Apple Business Chat, connected to its internal systems. The agents answer billing, balance and data usage questions, change subscriptions, sell add ons, take payments and recharges, and handle feedback and complaints, with a warm handover to a specialist who can take over or hand back. NiCE Cognigy reports that the first response time fell from 20 minutes to about 6 seconds. The deployment was already live in 2022, when the case study described it as conversational AI; the current version presents it as agentic AI.
- Response time reduction: 99.5%, first response time on messaging channels
"An AI agent picks up any inquiry in around 6 seconds, reducing first response times significantly from the previous 20 minutes: a 99,5% improvement."
Claimed by: vendor
Lufthansa Group
Germany · Travel and hospitality · 2020
During the pandemic, when passengers flooded call centres to change or cancel flights, Lufthansa Group replaced its in house chatbot with a conversational AI platform and built self service AI agents that manage rebookings, check alternative flights, give travel information and process refunds. The agents run on the airline websites and through SMS links that open a self service chat, with multilingual support and real time translation, and are used to absorb peaks such as strikes.
- Interactions handled: about 16 million, per year
"By leveraging AI-driven Self-Service Agents, the airline managed to significantly increase its interaction capacity, handling about 16 million conversations throughout the year with AI, with peak days seeing up to 375,000 interactions."
Claimed by: vendor
Vodafone Germany
Germany · Telecommunications · 2020
Vodafone Germany moved its messaging channels into one central team and put the TOBi chatbot in front of every WhatsApp, Apple Business Chat and SMS conversation, with a handover to a human agent on the same screen. TOBi understands more than 230 intents and can classify the photos and screenshots customers send, such as a bill or a router with a flashing red light. Genesys reports that the share of inquiries resolved by AI (which it calls first contact success) rose from 16% at launch to 44%.
- Containment rate: 44%, share of messaging inquiries resolved by TOBi without a human
"44% of inquiries now resolved by AI, up from 16%."
Claimed by: vendor
JetBlue
United States · Travel and hospitality · 2019
JetBlue moved its customer support to an AI platform from late 2019, opening messaging channels (Apple Messages for Business, Google Business Messaging, web and app chat, WhatsApp) with Spanish language support, a virtual agent that resolves routine requests and AI assistance for the crewmembers who handle the rest. In a January 2026 conference session published by the vendor, a JetBlue customer support leader described weather disruptions, when passengers ask for their options, and said the conversations crewmembers now handle (rebooking, refunds, alternatives weeks away) are multifaceted, which is why the airline has looked at AI that orchestrates several workflows. The ASAPP speaker in the same session warned against reading containment gains without checking whether customers still have the option to escalate.
- Containment rate: 45%, May 2023, virtual agent
"The integration of virtual agent experiences facilitated streamlined interactions and contributed to a remarkable 36% year-over-year growth in containment, with a 45% containment rate achieved in May 2023."
Claimed by: vendor - Hours saved: 73,000 hours, Q1 2023 only (one quarter, not annualized)
"In Q1 2023 alone, this AI-driven efficiency translated into significant savings of 73,000 workforce hours."
Claimed by: vendor
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- A contact reason report with volumes per intent and channel
- Approved, current knowledge articles with an owner and review date each
- A written list of intents and signals that always go to a human
- Historical transcripts to build test sets
Systems to integrate
- Telephony or contact centre platform (routing, queues, transfer with context)
- Messaging channels (web chat, app, WhatsApp, social)
- Authentication (app confirmation, one time passcode, voice biometrics)
- CRM or case management, read only at first
- Knowledge management system
Complexity: Medium
Answering from knowledge is straightforward. The effort is in clean, owned knowledge content, authentication on the phone, integration with the contact centre platform for a warm handover, and deciding which intents must never be contained.
- 1
Choose intents by volume and by risk
From the contact reason report pick the top general intents and write down, just as explicitly, the intents the agent must route and never contain: complaints, disputes, fraud, hardship and bereavement.
- 2
Clean the knowledge before you connect it
Retire duplicate and outdated articles, give every article an owner and a review date, and make the agent refuse when retrieval finds nothing. Many wrong answers are content problems.
- 3
Design the handover first
Agree with the contact centre what a human receives (summary, identity, attempted steps) and how the customer keeps their place in the queue. Always offer a clear route to a person.
- 4
Test on real conversations
Build test sets from historical transcripts per intent, including angry customers, vulnerable customers and attempts to push the agent off policy, and run them on every change.
- 5
Launch in chat, then voice
Chat is easier to monitor and correct. Add voice once containment and satisfaction per intent are stable, and measure repeat contacts, not just containment.
Guardrails
- Answers only from approved knowledge, with a refusal and a handover when it is not covered
- Mandatory routing of complaints, disputes, fraud, hardship and vulnerability signals to people
- A visible route to a human at any time, on every channel
- AI disclosure at the start of every conversation
- Masking of personal and card data before text reaches a model or the logs
KPIs to instrument
- Containment per intent, counting a repeat contact within seven days as not contained
- Handover rate and handover reasons per intent
- Customer satisfaction on contained conversations versus human handled ones
- Time to first meaningful response and total time to resolution
- Complaints that mention the assistant
Human in the loop
Human agents take every conversation the agent routes and can take over live. A quality team reviews a weekly sample of contained conversations for fluent but wrong answers and signs off every new intent before it goes live.
Common failure modes
- Containment that is really abandonment
- Customers give up rather than get helped, which looks like success on the dashboard. Count repeat contacts and measure satisfaction per intent.
- Replacing people instead of routing to them
- Klarna reported that its assistant handled two thirds of chats, then said in 2025 that it had gone too far and began hiring human agents again so customers can always reach a person. Keep human capacity for the moments that matter.
- Confident wrong answers
- An agent that invents a policy creates liability, as the Air Canada tribunal case showed. Ground answers in approved content and refuse when unsure.
- Missed vulnerability
- A customer in distress is kept in automation. Detect vulnerability signals and hand over early.
What are the risks and rules?
EU AI Act
Limited risk (transparency)
An AI system that interacts directly with people must be designed so that they know they are dealing with AI, unless that is obvious from the context (Article 50(1)). It is not high risk under Annex III as long as it does not evaluate eligibility for essential public assistance benefits and services (point 5(a)), creditworthiness (point 5(b)), risk and pricing for life and health insurance (point 5(c)) or emergency calls (point 5(d)). This holds only if emotion or vulnerability signals are inferred from what the customer says (text or transcript content), not from voice or other biometric features; an agent that infers emotion from a caller's voice is an emotion recognition system (Article 3(39)), which is high risk under Annex III point 1(c) and triggers the deployer disclosure duty in Article 50(3).
Guidance
- Regulation (EU) 2024/1689 (AI Act), Article 50: transparency obligations for providers and deployers of certain AI systems (European Union, Europe). People must be informed that they are interacting with an AI system unless this is obvious from the context.
- Chatbots in consumer finance (Consumer Financial Protection Bureau, North America). Warns that deficient chatbots that prevent access to live, human support can lead to law violations and customer harm.
- FG22/5: Final non-Handbook Guidance for firms on the Consumer Duty (Financial Conduct Authority, Europe). Says firms will likely need a real time human interface, such as a phone service, for security, fraud and other complex or sensitive journeys, and gives an automated phone system without a route to other support as an example of poor practice.
Controls to put in place
- Inventory entry with an accountable owner and a documented list of contained and routed intents
- Content governance with owners and review dates for every knowledge article
- Transcript logging and retention in line with record keeping rules
- Regression tests on every change to prompts, content or model
- Monitoring of outcomes for vulnerable customers and of complaint trends
When it went wrong elsewhere
- Incident 639: Air Canada Chatbot Reportedly Provides Inaccurate Bereavement Fare Information, Leading to Customer Overpayment. A Canadian small claims tribunal held the airline responsible for what its website chatbot told a customer about bereavement fares, a reminder that the organization owns every answer its agent gives.
Frequently asked questions
- What share of contacts can an AI agent resolve on its own?
- It depends on the intent mix and the channel. Microsoft reports that about 84.6% of Commonwealth Bank's self service messaging interactions were resolved end to end in May 2026, and that Vodafone's TOBi fully resolves 70% of inquiries arriving through digital channels; Posh reports that Together Credit Union's voice agent contains 66% of inbound calls. Airbnb itself reports a lower figure: nearly 45% of issues that begin with its AI assistant were resolved without a human agent in Q2 2026. Treat these as upper references and count repeat contacts before you celebrate.
- Should we remove the option to speak to a person?
- No. Klarna said in 2025 that customers must always have the option to speak with a human and began recruiting customer service agents again. The US Consumer Financial Protection Bureau warns that chatbots that block access to human support can break the law, and the UK Financial Conduct Authority expects a real time human route for complex or sensitive journeys.
- How is this different from account and card servicing?
- The first line agent answers and routes general inbound contact across the whole contact centre. Account and card servicing is the deeper, authenticated layer that performs transactions such as blocking a card. You can launch the first line first and add servicing actions behind it once authentication and handover work.
How to cite this page
Blits.ai AI Use Case Library, "AI agent for first line contact centre service", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/first-line-contact-centre-agent. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published