The pages.
| Page | What it covers | Address |
|---|---|---|
| HomeWhat AI reads, it repeats. AI Readiness, outside in | AAAnow assesses AI Readiness from the outside in: what an organisation publishes is what AI systems read, and what they read is what they repeat. | / |
| AboutOur Heritage, built on evidence not assumptions | AAAnow, built on evidence, not assumptions: 25 years of outside-in assessment, more than 3.7 trillion data points, and the group behind AI Readiness. | / |
| AboutGeneral Introduction, what you know and what AI sees | What you know of your organisation, and what AI sees of it: the 2 questions AI Readiness answers, told in a 1 minute film. | / |
| ServiceInside AI Governance, your rules read continually | How AI Governance operates: your content rules, continuous monitoring of published content, alerts to the owner, and the audit trail leadership reviews. | / |
| ServiceRegulated Company Example, AI Governance at work | An illustrative regulated company under AI Governance: the rules it set for its content, what the reading found, and the record its board can be shown. | / |
| ServiceThe accuracy problem outside your AI policy | AI policies cover the AI you control. External AI reads what you publish and answers for you: what goes wrong, why nobody owns it, and what to do. | / |
| ServiceThe service, page by page | Where the service is set out: the introduction and the order of work, the workshop, and the 2 programmes that follow. | / |
| ServiceAI Discovery, where your primary website stands | AI Discovery establishes where your primary website stands in what AI reads, and what has to be decided before the work of acceleration begins. | / |
| ServiceAI Discovery, the detailed overview | What AI Discovery is, who takes part, what is assessed, how the 3 weeks run, what the organisation receives, and where the work that follows belongs. | / |
| ServiceAI Accelerator, acting on what AI Discovery found | AI Discovery helped you understand where you are starting from and what to change. Over 6 weeks, AI Accelerator gets those changes made. Your people make them; we show what to change and check it has worked. AI then reads your content as you intended, and your directors and officers can show the business has acted. | / |
| ServiceAI Accelerator, the detailed overview | What AI Accelerator is, who takes part, how the 6 weeks run, what the organisation receives, responsibilities, buying rules, the 2 investment options, and where the work that follows belongs. | / |
| ServiceAI Confidence, your position with AI, each month | With AI Confidence watching your digital landscape, the board receives an independent report each month: what AI is saying about the organisation, what has moved, and where it stands on the AI Readiness Maturity Scale™. The board sees its position as the organisation advances, with independent evidence to show it. | / |
| ServiceAI Confidence, the detailed overview | What AI Confidence is, who it is for, how it runs, what the organisation receives, AI Governance alongside it, the obligations that apply, responsibilities, buying, the investment and where other work belongs. | / |
| ServiceAI Governance, keeping what AI sees appropriate | AI Governance reads what you publish against the rules you set, alerts the owner when content drifts, and holds the record of what was found and done. | / |
| ServiceThe service, an introduction and the order | An introduction to the AI Readiness service: what is established first, in what order, and what an organisation receives at each stage. | / |
| ServiceThe service, the journey | The AAAnow service as a journey: AI Discovery, AI Accelerator and AI Confidence, with Landscape mapping and AI Governance along the way. | / |
| ServiceSelf-Service AI Readiness, with your own team | Self-Service AI Readiness for organisations with their own resources: the outside-in reading, taken up on your own terms. Online from Oct. 2026. | / |
| PlatformThe platform, and how it works | How AI Readiness is established from the outside in: discovery of the landscape, the AI Readiness maturity scale, the doorway AI arrives through, and the mechanics behind each. | / |
| PlatformAI Misinformation Exposure, organisation-specific | AI Misinformation Exposure identifies where organisational information is more likely to be missed, misunderstood or inaccurately interpreted. | / |
| PlatformAI Readiness Maturity Scale, position in context | The AI Readiness Maturity Scale expresses the readiness result within a benchmarkable maturity framework: a position leaders can compare and track. | / |
| PlatformAI Readiness: how prepared your information is | AI Readiness brings Foundations, Governance and Visibility into 1 position: how prepared the organisational information environment is for AI. | / |
| PlatformCCDM, Continuous Content Discovery and Mapping | Continuous Content Discovery and Mapping, the AAAnow Platform: 1 continuing outside-in view of the information environment as the external position changes. | / |
| PlatformIN|SITE: where the organisation starts from | IN|SITE sets out an organisation’s overall position at a point in time: AI Readiness, likely misinformation exposure, a breakdown by content infrastructure, web pages and PDF pages, and what AI can reach. | / |
| PlatformOVER|SITE: what has changed since we last looked? | OVER|SITE carries the IN|SITE readings forward on an ongoing basis, follows how the organisation’s position changes and brings significant change to attention. | / |
| PlatformWORK|PACK: what to correct, and in what order | WORK|PACK follows on from IN|SITE and OVER|SITE. For the agency, the Head of Digital and their teams, it sets out what to correct, in what order, why, and how. | / |
| PlatformDigital Doorway: can AI reach your information? | Digital Doorway examines relevant AI access routes with 24 agents and establishes whether important organisational information can be reached through them. | / |
| PlatformLandscape Discovery: the scale of your landscape | One secure link across the organisation, partners and agencies. Known properties added in 2 minutes, then AAAnow maps outward across the wider landscape. | / |
| PlatformDiagnostics, where the reading of the estate is explained | Diagnostics sets out how the AAAnow Platform reads an organisation from the outside in, and how each finding is traced back to the evidence behind it. | / |
| PlatformPDF to HTML, the PDFs still shaping AI answers | PDFs across your websites and partner sites keep shaping AI answers after their information is out of date. | / |
| PlatformSecure Tag, verifiable content authority for AI | Secure Tag makes the relationship between an organisation, its domains, its content and the verified brand owner verifiable. | / |
| PlatformThe AAAnow Platform | The AAAnow Platform (CCDM): what organisational information exists, whether AI routes can reach it, how prepared it is and where it may be misunderstood. | / |
| PlatformSchematic, the system behind the AAAnow Platform | The system behind the AAAnow Platform: the unchanged 2023 schematic (ARTYMUS, KRATOS, parallel AI) and the infrastructure record behind delivery. | / |
| PlatformWhy AAAnow | Why AAAnow rather than consultants, a manual project or internal delivery: a ready-to-go platform that can begin within hours. | / |
| Use casesThe use cases: what happened, and the impact | The 45 AI Readiness use cases in 1 list: what happened when AI answered from an organisation’s own published estate, and the impact, 2 lines each. | / |
| Use cases£350,000 to be seen by AI routes the estate was turning away | An organisation committed £350,000 to a programme to make it more visible in AI answers. Throughout the programme, most of the AI routes the programme was paying to… | / |
| Use cases2 AI tools, 2 different answers, and consequences the organisation could not see in full | Employees checking a mileage repayment rate from published government information asked AI and received different rates by different tools. | / |
| Use cases20 years of documents were still answering for the company as if each were current | Consider a pharmaceutical company whose published estate holds 20 years of documents: clinical summaries, product monographs, guidance, presentations and press material. | / |
| Use casesA 2-year-old partner promotion put the firm in front of the regulator | A financial services firm was cited at inspection for misrepresenting its products to a market it had withdrawn from. | / |
| Use casesA hostile version of the results was the one AI quoted, because the company's was locked away | Consider a listed company that publishes its results in documents carrying no titles and no authors, and blocks AI from reading its site. | / |
| Use casesRegulator embarrassment at poor document control | A national financial regulator published its rules and guidance as PDF documents. 20% of the links inside them were broken, including links to the legal basis of some of… | / |
| Use casesA withdrawn announcement was quoted as the company's current statement | Consider a listed company that withdraws a regulatory announcement and issues a corrected one the same day. | / |
| Use casesA withdrawn medicine still carried full dosing guidance online, and AI served it as current | A pharmaceutical company's pandemic-era microsite was still live years after the end of COVID. | / |
| Use casesAI created the claim and then the brand exposure | In this scenario, a consumer brand withdraws a product claim after a complaint. | / |
| Use casesAI read half the annual report and made up the rest | A listed company's annual report was large enough that AI read only part of it before it stopped. The strategic report at the front was read; | / |
| Use casesAn old annual report answered as this year's after the page that dated it was removed | Consider a listed company that redesigns its investor site and removes the archive pages that framed each annual report by year. | / |
| Use casesApplicants acted on entry requirements the university had already changed | Applicants receive last cycle’s entry requirements by AI, drawn from a prospectus the university has replaced; | / |
| Use casesAt renewal, the firm could not show what it had online, and the terms showed it | Picture a firm arriving at its insurance renewal unable to say what it has online, how that material is controlled, or what AI is telling its customers about it. | / |
| Use casesCandidates told about benefits no longer offered | In this scenario, candidates researching a consumer brand as an employer ask AI what it is like to work there. | / |
| Use casesChemicals, the use cases | The AI Readiness use cases for chemicals: handling and storage information served wrong, steps out of order, and the internal AI project that failed on the documents,… | / |
| Use casesClinical and licensing exposure, a use case | A use case: how outdated clinical and licensing information still reachable online is repeated by AI as current, and what AI Readiness changes. | / |
| Use casesClinicians received instructions the company had replaced | Picture a clinician asking AI how a medicine is administered, and receiving a dosing schedule the manufacturer replaced 2 years earlier. | / |
| Use casesConsumer brands, the use cases | The AI Readiness use cases for consumer brands: brand information misunderstood, retailer reach, duplicates, careers material, the external repository and intellectual… | / |
| Use casesCustomers abroad received the wrong cover and the wrong number to call | Customers of an insurer who asked AI what their travel policy covered, and whom to call in an emergency, were given the cover levels from a holiday promotion page and an… | / |
| Use casesThe impact of out-of-date safety information: who is liable? | A major US retailer's pages for a manufacturer's electrical products carried support and safety details that were out of date, including which product versions applied… | / |
| Use casesCustomers were shown a brand the company had retired, from guidelines it had replaced | When they ‘the corp’ published new brand guidelines and retired the old ones, which stayed online with no date, no version and no marker of authority. | / |
| Use casesCustomers were told a discontinued product was still on sale | In this scenario, a consumer brand discontinues a product line and, a year later, sells the division that made it. | / |
| Use casesDrafts uploaded, served as the company’s final word | In this scenario, a consumer brand contracts a third party to hold its published documents: product manuals, safety assets, the annual report and press material. | / |
| Use casesFinancial services, the use cases | The AI Readiness use cases for financial services and insurance: partner promotions, promotion pages versus the policy, regulatory inquiry and evidencing mitigation at… | / |
| Use casesFunding stopped on submissions that looked fine | A government funding body refused funding to projects that qualified for it. Its own system had misread 64% of the applications, so the decisions rested on figures the… | / |
| Use casesGovernment, the use cases | The AI Readiness use cases for government: planning, chatbots, the regulator’s own documents, funding submissions and contradictory guidance, with what happened and the… | / |
| Use casesHigher education, the use cases | The AI Readiness use cases for higher education: applicants acted on entry requirements the university had already changed, and the estate the digital team had warned… | / |
| Use casesIntellectual property disputes, a use case | A use case: brand claims, product names and assets spread across regional sites, campaign pages and partner material, blended by AI into contested claims. | / |
| Use casesInvestor updates lost in templates, a use case | A use case: investor updates published into templated pages, built for people rather than machines, fail to reach the AI systems answering about the company. | / |
| Use casesInvestors acted on results the company had already superseded | Picture investors, analysts and advisers asking AI about a listed company and receiving superseded results, old guidance and legacy pages as the current position, each… | / |
| Use casesInvestors were quoted guidance the company had already revised | Consider a listed company that revises its full-year guidance at the half year. The original range, issued 6 months earlier, stays online in the trading update, the… | / |
| Use casesInvestors told the former chair still ran the board | Consider a listed company whose chair retires and whose new chief executive is appointed within the same year. | / |
| Use casesLegacy document exposure in health, a use case | A use case: decades of trial summaries, product information and regulatory material stay readable, and AI repeats superseded documents as current in health. | / |
| Use casesLife sciences, the use cases | The AI Readiness use cases for life sciences: withdrawn products, the COVID-era microsite, clinical instructions misread, legacy documents and the correction that lost… | / |
| Use casesListed companies, the use cases | The AI Readiness use cases for listed companies: the annual report misread, results outcompeted, superseded guidance, the former board and the group position, with what… | / |
| Use casesNew York City’s business chatbot told companies to break the law | New York City launched a chatbot to help businesses understand local rules. It misstated city policies and advised businesses to break the law. | / |
| Use casesPatients were told a withdrawn medicine was available, because the website still said so | Picture a medicine that is withdrawn from a country's market and can no longer legally be supplied there. The company's pages for that country still list it. | / |
| Use casesPlanning decisions went wrong on a website the city had forgotten | Residents of a newly consolidated municipality received wrong planning and zoning advice, permit applications that should have been approved were denied, appeals… | / |
| Use casesRegulatory inquiry from old material, a use case | A use case: how superseded published material draws regulatory questions once AI systems restate it as current, and what AI Readiness changes. | / |
| Use casesResidents get different answers to the same question, and the cost lands on the council | Residents of a local authority asking about the same service received different answers depending on where they asked and when. | / |
| Use casesShareholder confusion from the record, a use case | A use case: how a public company’s own published record produces AI answers that confuse shareholders, and what AI Readiness changes. | / |
| Use casesStaff handled chemicals on replaced instructions | A chemicals company's staff and customers were given handling and storage instructions by AI that the company had replaced. | / |
| Use casesThe annual report AI read was not the one the board signed | Listed companies' annual reports, approved by their boards and published as the primary disclosure, were read by AI as different documents: values loose from their… | / |
| Use casesThe buyer's AI knew things about the company that the company did not | Consider a company being acquired that discovers, during due diligence, that the buyer's AI-assisted review has read the wider reachable public record under its name. | / |
| Use casesThe chatbot was replaced twice before anyone looked at what it was reading | A local authority deployed 3 chatbots in succession to answer citizen enquiries. The first was blamed on the software vendor and replaced. | / |
| Use casesThe company’s AI amplified its own PDF failings | An organisation's internal AI and agentic systems ingested its documents automatically. Where a document was poorly structured, the resulting error entered the system,… | / |
| Use casesThe corrected storage instructions lost to the document they replaced | A chemicals company updated the storage temperatures for a product and published the correction in a new procedure doc. | / |
| Use casesThe correction never reached the customers, because the old copies kept answering | In this scenario, a consumer brand corrects a product page and republishes it. | / |
| Use casesThe estate the digital team had warned about | 2 organisations found the cause of their AI misinformation in the same place: an online estate their digital teams had warned about for years and nobody had mapped. | / |
| Use casesForgotten sites nobody was patching: AI finding and highlighting vulnerabilities | An organisation's forgotten subdomains, old microsites, legacy platforms, forms and scripts sat outside patching, access review, certificate renewal and monitoring,… | / |
| Use casesThe impact of AI finding what you cannot see | A listed company's annual report used the brand's own typeface for its headings, titles and table labels. The font was not embedded in the published file. | / |
| Use casesThe internal AI project failed, was written off, and the documents that sank it were never examined | A chemicals company invested in a generic internal AI system to answer questions on handling and storage. | / |
| Use casesProduct announcements, published yet missed by AI. | In this scenario, a consumer brand announces a product change that matters to its customers. | / |
| Use casesRegulator's questioned governance where the compliance register stopped | Consider a regulated firm that receives an information request from its regulator about statements AI has made to customers in the firm's name. | / |
| Use casesThe results table AI read had its figures in the wrong columns | A listed company's segment results, laid out in a table across 3 years, reached AI as a single stream of figures with the headings at the top and the values in one run. | / |
| Use casesThe mixing steps answered out of order by AI | A chemicals company's procedure for mixing a product ran to 9 steps. Asked for the procedure, AI returned a sequence that included 1, 3, 9, 6 and 2 in that order,… | / |
| ArticlesStop paying AI to reread what has not changed. | Basic website errors drive billions of needless machine requests. Removing 3 in 4 saves millions in AI spend and 7,139 tonnes of CO2 a year in the US, and makes current information easier for AI to identify. | / |
| ArticlesWorld Aviation Festival. RWS sharing the Global Airlines benchmark | The World Aviation Festival, Lisbon, 13 to 15 Oct. 2026: the first Global Airlines benchmark with RWS, what RWS does for airlines, the attendee summary and 10 questions for airline leaders. | / |
| ArticlesIf AI poisoning reached your annual results, who controls the figures investors see? | How AI poisoning reached a listed company's results: what grounding checks test and miss, what directors will be asked, and 3 questions for the board this week. | / |
| ArticlesCorrect annual report, wrong AI answer. Who answers for it | Why a correct annual report can give AI a wrong answer: where the fault sits in the financial PDF, the duties directors carry, and 10 questions the CFO should ask. | / |
| ArticlesConvergence, New York: what AI can find, and who answers for it | Convergence, Wilson Elser, New York City, 30 Sep. 2026. Matthew McQueeney joined the panel for AAAnow, on what AI can find about an organisation and who is responsible. | / |
| ArticlesLatest CMS Wire piece, why is AI turned away? | Nearly 1 in 5 top websites block at least 1 major AI crawler, often unknowingly. What AI cannot reach on your estate, it finds elsewhere. | / |
| ArticlesAttending Convergence? Join us in New York | Convergence, the Wilson Elser conference on privacy, cybersecurity and AI governance, New York, 30 Sep. 2026. Matthew McQueeney of AAAnow attends. | / |
| ArticlesSupporting investor confidence as AI reliance grows | Investors research with AI, so an outdated forecast shapes their view of a company: 5 review areas, and the evidence a board needs to hold. | / |
| ArticlesAI governance: 5 questions directors should ask | An AI system presents an investor with figures that were never in the accounts. Who answers for it: the duties in play, and 5 questions for the board. | / |
| ArticlesArticles, research and dispatches from the field | Short, practical writing from the team behind AI Readiness: what AI reads about an organisation, and the digital estate it reads from. | / |
| ArticlesWhy do 9 in 10 AI projects fail? | MIT NANDA found only 5% of enterprise GenAI pilots extracting value. Why a failed pilot cannot be diagnosed until its source material is examined. | / |
| ArticlesAI is collecting on your digital estate debt | Years of launches, acquisitions and published documents built a digital estate AI now reads in full. The four impacts, and what the debt costs. | / |
| ArticlesAudio transcript, The Triad of Exposure in full | The complete written transcript of the audio edition of The Triad of Exposure: misrepresentation, future visibility and the cyber problem. | / |
| ArticlesAutomation supporting AI Governance, outside-in | Governance starts with knowing where you are and what you are in control of: the outside view AI now forms of an organisation, what it reads, and how automation keeps the rules. | / |
| ArticlesCorrect to people, wrong to machines | Financial PDFs can show the right figures to people while presenting broken relationships to machines, and AI answers repeat the difference. | / |
| ArticlesEvergreen PR and the sector benchmark that wins | A sector Scorecard turns independent measurement into the benchmark a market checks itself against, and earns digital teams recognition for their work. | / |
| ArticlesHow AI Accelerator reduces AI misinformation | AI Accelerator shows an organisation what AI can find about it, and where that material is misread, then evidences how the position moves across 6 weeks. | / |
| ArticlesIf AI Is the Front Door, What Does It See? | Sam Altman predicts AI will soon handle most agency and creative work. Lawrence Shaw of AAAnow asks what AI can actually read about you first. | / |
| ArticlesLooking both ways, and not being run over by AI | Being AI-first runs on two axes: internal maturity, and how AI reads and represents an organisation to the market. The second half decides. | / |
| ArticlesThe same exposure lands differently by sector | The exposure is the same in all sectors: what an organisation publishes is what AI systems read. What differs is where it lands, and the cost of correcting it. | / |
| ArticlesThe Triad of Exposure, digital sprawl as risk | Misrepresentation, future visibility, and the cyber problem: 3 exposures organisations carry as AI reads the forgotten digital estate. | / |
| ArticlesThree stages, and only one order, explained | AI Foundations first, AI Governance next, AI Visibility ongoing. The order is not a preference: out of order, each month raises the cost of putting it right. | / |
| ArticlesUnderstanding, then protecting: AI Misinformation | The outside-in risk frontier: what public-facing AI systems already say about an organisation, what the 2026 benchmarking data shows, and how the 6-week AI Accelerator… | / |
| ArticlesVisibility, amplifying whatever governance exists | Visibility applied before the foundations are confirmed amplifies whatever governance is in place, faults included. Why order decides the outcome. | / |
| ArticlesWhy organisations block the AI systems reading them | Outside-in assessments show sites fully open to people while AI-related routes are turned away. Five anonymised examples, and what it costs. | / |
| ArticlesAI readiness, by client status | One AI readiness conversation, worth something different to a present client, a lapsed client, a prospect, a live discussion, and an inbound inquiry, with a ready message for each. | / |
| ArticlesAI Search Visibility Is Not the Same as AI Readiness | Visibility and AI readiness answer different questions: how an organization appears in AI-generated answers, and whether the material underneath is fit to be read, trusted, and reused. | / |
| ArticlesAutomate quotes & meeting work packs so client reviews can be evidence-led | Generate evidence-led work packs on demand. Cut pre-meeting prep time, improve quote accuracy, and close reviews with clearer next steps. | / |
| ArticlesBeing heard, standing apart, and the revenue that results | For agencies. A standing, independent reading of how AI finds and represents a client, taken from the outside in and applied across present, lapsed and prospective clients. | / |
| ArticlesEquip BDRs with target-named outreach that triggers real conversations | Replace generic templates with target-named insight, so BDRs lead with something specific, relevant, and provable, and improve first-call progression. | / |
| ArticlesHow losing the pitch is worth more. 30 days, $40,000. | Losing a client rarely ends the story. A long-term account moved its website to another agency and performance collapsed; the original agency stepped in with evidence and won a $40,000 engagement. | / |
| ArticlesWhere The Agency Revenue Radar fits the BenchPress 2026 priorities | BenchPress 2026, Driving Agency Growth, sets out where £1m+ agencies are placing their bets for the year, read against where The Agency Revenue Radar carries the work. | / |
| ArticlesRe-engage dormant accounts automatically, with evidence that earns a reply | Restart lapsed client conversations with account-specific evidence. Automated outreach that earns replies, not generic ‘just checking in’ messages. | / |
| ArticlesThe One Change That Wins More Pitches | Most of the decision forms before the pitch is heard. The agency with something to say about the buyer, not only about itself, holds the rarer thing to offer. | / |
| BenchmarkThe benchmark, sector by sector | Where sectors stand on the AI Readiness maturity scale, published as a set: listed companies, insurance, higher education, life sciences, chemicals, hotels and airlines. | / |
| BenchmarkTop 50 Insurers / brokers, the AI Readiness Benchmark | The AI Readiness Benchmark for the top 50 insurers and brokers: 50 organisations read from the outside in and placed on the AI Readiness Maturity Scale™. | / |
| BenchmarkFortune, AI Readiness BenchmarkFortuneBenchmark has not been completed, Global Chemicals is available presently | The AI Readiness Benchmark across the Fortune listing: how AI reads these organisations from what they have published, and where the exposure sits. | / |
| BenchmarkGlobal Airlines, the AI Readiness Benchmark | The AI Readiness Benchmark for global airlines: 75 of the largest airline groups read from the outside in, as AI and AI agents read them, and placed on the AI Readiness Maturity Scale™. | / |
| BenchmarkGlobal Chemicals, the AI Readiness Benchmark | The AI Readiness Benchmark for global chemicals: how AI reads the sector from what its organisations have published, and where the exposure concentrates. | / |
| BenchmarkGlobal Higher Education, AI Readiness BenchmarkGlobal Higher EducationBenchmark has not been completed, Global Chemicals is available presently | The AI Readiness Benchmark for global higher education: how AI reads institutions from decades of published material, and where the exposure concentrates. | / |
| BenchmarkLondon listed 100, AI Readiness BenchmarkLondon listed 100Benchmark has not been completed, Global Chemicals is available presently | The AI Readiness Benchmark for the largest London listed companies: how AI reads them from what they have published, and where the exposure concentrates. | / |
| BenchmarkLondon listed 250, AI Readiness BenchmarkLondon listed 250Benchmark has not been completed, Global Chemicals is available presently | The AI Readiness Benchmark for the London listed 250: how AI reads these companies from what they have published, and where the exposure concentrates. | / |
| BenchmarkWorld Hotels 100, the AI Readiness BenchmarkWorld Hotels 100Benchmark has not been completed, Global Chemicals is available presently | The AI Readiness Benchmark for the World Hotels 100: how AI reads the group from what its organisations have published, and where the exposure concentrates. | / |
| BenchmarkThe AI Readiness Benchmark, why the board needs it | Buyers, investors and regulators ask AI before they ask you, and compare you with your sector as they do. The benchmark shows where you stand. | / |
| BenchmarkPrivate and Corporate AI Readiness BenchmarkPrivate / CorporateBenchmark has not been completed, Global Chemicals is available presently | The AI Readiness Benchmark run on your organisation alone, for the board: how AI represents you, whether that is improving. | / |
| BenchmarkLife Sciences, the AI Readiness Benchmark | The AI Readiness Benchmark for life sciences: 75 organisations read from the outside in and placed on the AI Readiness Maturity Scale™. | / |
| BenchmarkInsurance Pools, the AI Readiness Benchmark | The AI Readiness Benchmark for insurance pools: 25 pool members read from the outside in and placed on the AI Readiness Maturity Scale™. | / |
| BenchmarkResults, AI Readiness | AI Readiness results for single organisations, each at its own address, reported on the AI Readiness Maturity Scale. | / |
| Free toolsFree tools, and how they work | Tools that run in the browser, with no account and nothing stored: a JSON-LD validator, an llms.txt generator, an agent.json builder, and guides for serving markdown to… | / |
| Free tools/ask endpoint: answering questions in plain words | /ask is an address on your site that takes a question in plain language and answers from your own content, defined by NLWeb, with 4 routes to build one. | / |
| Free toolsagent.json: telling AI agents what you offer | The JSON file published at a fixed address on your domain that tells AI agents what your service can do, where its endpoint is and how to authenticate. | / |
| Free toolsAkamai: serving pages as Markdown to AI agents | Serving a web page as clean Markdown when an AI agent asks for it. The Akamai position on AI traffic, 3 routes to build it, and Bot Manager policy. | / |
| Free toolsCloudflare Markdown for Agents, explained | A Cloudflare feature that converts a page from HTML to Markdown at the network edge when an AI agent asks. What it does, who owns it, and how to enable it. | / |
| Free toolsllms.txt: a curated map of your site for AI | llms.txt is a Markdown file at the root of a website: a short description and a curated list of its most useful pages, written for AI systems. | / |
| Free toolsThe JSON-LD Schema Validator, what valid means | A standalone public product that tests structured data in code, HTML and live pages against JSON, JSON-LD, Schema.org and consumer requirements. | / |
| Free toolsThe validator processing sequence, stage by stage | The processing stages, from intake to result assembly, with the evidence each stage accepts, the checks it applies, and the output it assures. | / |
| Free toolsValidator primary sources, each finding traced | The published standards the validator treats as authority, from JSON and JSON-LD to Schema.org and consumer documentation, each with its role. | / |
| Free toolsValidator security and privacy, data handling | What happens to what you submit: security architecture, privacy and isolation, data handling, release assurance, and the recorded behaviour per evidence case. | / |
| Free toolsWorking with the JSON-LD Schema Validator | Submission to result, source-linked findings, graph navigation, the states a check can return, a specimen finding, and versioned export. | / |
| BriefingsBriefings, by invitation | The private briefing, the briefing for members of a body or pool, and how a pool is briefed as a group. | / |
| BriefingsAI misinformation and cyber exposure for risk pools | A dedicated briefing for public entity risk pool executive and risk leadership: the evidence, the two exposures, and what can be measured at pool level. | / |
| BriefingsDigital agencies: what AI sees of your clients | A private 20-minute briefing for digital agencies, by invitation and at no charge: what AI reads about the organisations you serve, what it repeats, and what stands behind that picture. | / |
| BriefingsThe Member Briefing, hosted for your members | A 3.5-hour session you host for up to 36 member organisations, each attendee leaving with their own outside-in AI position and at least £1,400 of service value. | / |
| BriefingsThe Full-Day AI Readiness workshop, in detail | The Full-Day: the complete 3.5-hour executive working session plus client-selected modules, protecting AI investment. Investment £5,900. | / |
| BriefingsYour briefing invite: what AI says about you | A private 20-minute briefing, by invitation and at no charge: what AI reads about your organisation, what it repeats, and what stands behind that picture. | / |
| ResourcesResources, and where to find them | The AI Readiness landing page, published pricing, the site map, tag security, the participant page for adding sites, the collection, and what is still being written. | / |
| ResourcesAdd the sites you know to the digital landscape | Help AI find and represent us correctly. If you know of a public site connected to the organisation, add it: that one step is the whole of the ask. | / |
| ResourcesAI Readiness, outside-in: what AI reads about you | What AI sees, not what the organisation says. The introduction to AI Readiness: the position, the Triad of Exposure, the 3 stages, and the pricing. | / |
| ResourcesPages awaiting content | Working list of the pages that are live with a hero and standfirst only, and the brief each one sets for itself. | / |
| ResourcesPricing, published | Each service has a defined scope and a published investment, the same for each organisation: AI Discovery, AI Accelerator, AI Confidence, AI Governance and the partner subscription. | / |
| ResourcesSite map, all pages on this site in one list | All pages on the AAAnow site in one list: AI Readiness, the briefing, the platform, the use cases, the AI Readiness Benchmark, articles and legal. | / |
| ResourcesTag Security, the architecture and its controls | How the AAAnow secure tag is built, protected, and maintained. Architecture, incident response, integrity controls, and real-world test results. | / |
| ResourcesThe Gallery, the AAAnow collection of works | The Collection is being curated, opening late Sep. 2026. Five rooms are built and lit; the frames stand empty until the works are hung. | / |
| ResourcesConvergence, New York, in the Gallery | Photographs from Convergence, the Wilson Elser event on privacy, security and AI governance, New York City, 30 Sep. 2026, where AAAnow joined the panel. | / |
| ResourcesWorld Aviation Festival, Lisbon, in the Gallery | AAAnow at the World Aviation Festival, Lisbon, 13 to 15 Oct. 2026, arriving with a partner and a full hold of support and goodies for the airlines we meet. | / |
| ResourcesSearch, find what we say and where we say it | Search the pages we publish by title and by what they cover: the use cases, the articles, the service and the AI Readiness Benchmark, sector by sector. | / |
| LegalLegal and policies, in full | Terms of service, privacy, cookies, acceptable use, the disclaimer, accessibility, and how to report a security issue. | / |
| LegalAcceptable Use Policy for AI Readiness services | Guidelines for the responsible and lawful use of AAAnow services and the AAAnow platform, including contributions, conduct, and the consequences of breach. | / |
| LegalAccessibility Statement, WCAG 2.2 AA position | AAAnow is committed to ensuring access to The AAAnow content for all visitors. This statement describes our standards and how to contact us. | / |
| LegalCookie Policy, categories and preferences | How AAAnow uses cookies on The AAAnow website, the categories of cookies in use, why each one is set, and how to manage or withdraw your preferences. | / |
| LegalDisclaimer, the limits of this information | Important disclaimers for the AAAnow site and AI Readiness: external links, the limits of the information provided, and professional advice. | / |
| LegalPrivacy Policy, your data and your rights | How AAAnow collects, uses, and protects your personal information when you use the AI Readiness service, and the rights you have over your data. | / |
| LegalSecurity, how to report a vulnerability to us | How to responsibly report a security vulnerability in any AAAnow online service, what to include, and how our team will respond to your report. | / |
| LegalTerms and Conditions for the AI Readiness service | The basis of our service provision. The legal terms and conditions governing the use of the AI Readiness service from AAANOW.AI Ltd. | / |
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