Alt-TAB is a human-rights-aware pre-deployment risk discovery platform. It helps organisations find the risks, obligations, and blind spots they didn't know existed. before a product, service, process, or policy goes live.
A structured, two-stage ethics and Safety by Design assessment grounded in 35 governance frameworks. Built in Australia. Launched at the United Nations. Used around the world.
Why Alt-TAB exists
Preventable digital harm should never be the cost of innovation.
Most harm caused by technology was preventable. It simply was not anticipated or questioned early enough. The failures that make headlines rarely come from bad intentions. They come from good people who never stopped to ask the right questions before it was released.
If you're here, you've already done something that matters. Most technology gets deployed without anyone stopping to ask who it might affect, what could go wrong, or whether the people it's meant to help were ever really considered. You stopped. That's worth something.
Technology moves fast. Reflection rarely keeps pace. In many build cycles, nobody stops to ask who could be harmed, what unintended consequences may emerge, or what risks are being created for users, creators, and the organisations behind the product. Alt-TAB creates that moment. Before deployment, when something can still be changed.
It's not a compliance checklist. It's named after the keyboard shortcut that switches windows: a designed pause, an intentional interruption. The questions matter. But the pause is the intervention.
Three things every assessment must address
1
Human rights and equity safeguards
Gender equality, child protection, disability access, Indigenous data sovereignty, and inclusion. Who does this affect? Who gets left out? Who is carrying a risk they don't know about?
AI tools, data systems, and digital platforms can be weaponised for coercion, exploitation, and abuse. This is screened in every single assessment regardless of what kind of product is being built, because the risk is almost always invisible to the builder.
3
Cybersecurity and systems integrity
Data flows, threat vectors, access controls, and potential exploitation pathways. Digital harm is not only a social issue. It's a systems vulnerability, and it gets checked in every assessment.
The problem in numbers
97%
of products assessed had serious gaps that needed addressing before deployment
5.7
blindspots identified per assessment on average across the tool's entire history
1
assessment, across the entire history of the tool, has ever reached strong readiness
These are not outliers. This is what happens when no one has ever asked the right questions before deployment. Tracking started 16 May 2026, consistent with patterns observed across every assessment round since the tool launched at the United Nations in March 2026.
The most common blindspot found is second-order risk, how a product could be misused in ways the builder never imagined. A fitness app becoming a stalking tool. A messaging platform enabling coercive control. A booking system exposing where a family violence survivor sleeps. These are not edge cases. They showed up in 61 of the 210 blindspots found across 37 assessments.
Anyone deploying technology or changing a digital process. No specialist knowledge required. No account. Free.
🏘️
Community organisations
Moving client records to a new system, adding a booking tool, or adopting a case management platform.
🚀
Startups and founders
Building something new and wanting to know what they haven't thought about before they ship.
🤝
NGOs and charities
Adopting AI tools, migrating data, or building digital services for vulnerable communities.
🏛️
Government teams
Procuring or building technology that will affect the public, particularly vulnerable populations.
📚
Educators and researchers
Teaching responsible technology development or assessing research tools before deployment.
💼
Sole traders
Adding a chatbot, a payment system, or a client portal and wanting to make sure they've thought it through.
Child safeguarding and upstream harm prevention
The greatest risks are not always the most obvious. While child safety may not be the first issue people think of, it remains Alt-TAB's highest-risk category. The consequences of getting it wrong are profound. It carries the potential for the most significant harm and organisational impact.
Child safety is screened in every single assessment, regardless of whether the builder mentions children. This is a deliberate design decision grounded in how harm actually occurs: children encounter technology designed for adults. A fitness app, a community forum, a local business booking system, a mental health tool. None of these are "children's products," and almost none of their builders consider child safety when designing them. Alt-TAB does.
Across assessments conducted since launch, child safety gaps (missing age verification, features enabling adult-child anonymous contact, platforms with community features that create grooming pathways) represent the most serious harm potential of any risk category identified. The mandatory screening means these gaps surface even when the builder has not considered children at all.
The question Alt-TAB asks is not "is this a children's product?" but "could a child be harmed by this?" That reframing is the intervention.
Upstream harm prevention
Upstream prevention means stepping in before harm happens, not cleaning up after it. In child protection, this principle is well established in social work and public health. It's rarely been applied to technology design. Until now. Alt-TAB creates the moment where a builder stops and asks who this could hurt, before the product ships, when something can still be changed.
Technology-facilitated child exploitation almost never involves malicious code. It exploits legitimate features: messaging systems, anonymous profiles, community forums, location data, content recommendation algorithms. These features are built without malicious intent and harm children anyway. Alt-TAB uses ACCCE research on how these harm pathways actually operate to ask the questions that surface vulnerabilities before a child is harmed, not after.
Child safety frameworks applied in every relevant assessment
UN Convention on the Rights of the Child
The foundational international treaty on children's rights, ratified by Australia. Articles 3 (best interests of the child), 16 (privacy), and 17 (access to safe information) applied directly to technology design decisions.
ACCCE Research on Child Exploitation
The Australian Centre to Counter Child Exploitation produces evidence-based research on how digital platforms are exploited for grooming and child sexual abuse material. This research directly informs the follow-up questions Alt-TAB generates for products with child exposure risk.
UNICEF Guidance on AI and Children 3.0
UNICEF's 2025 framework for responsible AI when children are involved, covering data collection, consent, age-appropriate design, and AI systems that interact with or affect young people.
UK Age Appropriate Design Code
The most detailed practical standard for child-safe product design in existence. 15 design standards including privacy by default, no nudge techniques, and data minimisation. Cited as a design benchmark for Australian products and as a legal obligation where UK child users are involved.
How it works
Alt-TAB runs in two stages. Most ethics tools stop at stage one. The second stage is where the real work happens.
The nine questions in Stage 1 are not just data collection. By the time you reach your report, you have already stopped. You have named who your product affects, what could go wrong, and what safeguards you have or don't have. Many builders find that the questions themselves are where the insight happens. The report confirms and extends what the structured reflection process already started. This is intentional. The most valuable thing Alt-TAB does may not be the analysis. It may be the 15 minutes before it.
1
Tell us what you're assessing
Pick the type of technology decision: building, adopting, changing a process, or reviewing something existing. Nine quick questions about your situation and a plain-language description of what you're working on.
2
A deeper look at the areas that matter
Alt-TAB writes targeted follow-up questions specific to what you described. Three questions per risk area flagged, plus a question about your deployment timeline.
3
Your report
A readiness score, blindspot analysis, recommendations split between legal obligations and ethical best practice, a 30-day action plan, and five questions to take to your team. Downloadable as a PDF. Nothing you submitted is kept.
Before any questions, you pick the type of technology decision you're facing: building something from scratch, adopting an existing tool, changing a digital process, or reviewing something already running. This changes everything: the questions you get, the language used, and what the AI focuses on. A community organisation moving client records gets a completely different experience than a startup building an AI product, even if they both flag the same risk areas.
2
Stage 1: nine questions, four layers of analysis
You answer nine domain questions and describe your product or process in plain language. That description is the most important thing you submit. It's what Alt-TAB checks your answers against.
The Stage 1 analysis works in four layers:
Consistency checking. If you say you have safeguards but your description doesn't mention any, Alt-TAB flags that as confident ignorance, one of the four risk patterns the tool looks for. You can't game it by ticking boxes, because the description is the ground truth.
Mandatory screening. Two areas are checked in every single assessment regardless of what you answer: technology-facilitated gender-based violence and cybersecurity. These risks are almost always invisible to builders. Not because they're careless, but because the harm pathways aren't obvious. A fitness app with location sharing is a technology-facilitated gender-based violence risk. A community calendar with user accounts is a cybersecurity exposure. Child safety is also screened in every assessment, because children encounter technology designed for adults and most builders never consider this.
Jurisdiction calibration. The tool reads your description for context: where you're based, who your users are, whether First Nations communities or children are involved. Australian products get Australian law. Products with EU users get GDPR flagged. Products affecting Aboriginal and Torres Strait Islander communities get AIATSIS and CARE Principles guidance.
Tailored follow-up questions. For each risk area flagged, Alt-TAB writes three to five follow-up questions specific to your product. These aren't drawn from a question bank. They're written fresh for each assessment based on what your description revealed.
3
Stage 2: your full readiness report
You answer the follow-up questions and tell us your deployment timeline. Alt-TAB's second-stage analysis takes everything: your original answers, your description, your follow-up responses, and your timeline. It produces a structured report with:
A readiness score out of 100 with a plain-language explanation of how it was calculated. Not an arbitrary number, but one derived from the specific risks found, safeguards identified, accountability structure, and how soon you're planning to launch.
Blindspot analysis where each gap is put into one of four categories: confident ignorance, missing context, false safety, or second-order risks. Each one includes the evidence from your submission, a question to ask yourself, and the framework that applies.
Specific recommendations clearly split between legal obligations and ethical best practice, so you can see what the law requires versus what's simply the right thing to do. Every recommendation includes a concrete first step in plain language.
A 30-day action plan split into this week and this month, timed to your deployment.
Questions to take to your team, written for the person running the assessment to hand to whoever can actually act on the findings.
4
You get a PDF and nothing gets stored
The full report downloads as a PDF, including the action plan, team questions, and all. Legal obligations are visually separated from ethical recommendations. Every framework cited links to a plain-language explainer. And nothing you submitted is kept: no product descriptions, no answers, no report content. Only anonymised stats are retained, and only with your consent. Alt-TAB was built to practise what it assesses.
Which AI powers this tool?
Alt-TAB uses Claude, made by Anthropic. The structured assessment logic, framework selection, and question generation are designed by Away from Keyboard. Claude applies that structure to your specific product description and generates the analysis.
Does Anthropic see what I type into Alt-TAB?
Your description and answers are sent to Anthropic's API to generate your report. Anthropic processes this content to produce the analysis and does not use API inputs to train its models by default. Your assessment content is not stored by Anthropic after the request completes. This is standard for API usage, which is different from how consumer products like Claude.ai may handle data. Anthropic's privacy policy
Does Alt-TAB or Away from Keyboard see what I type?
No. Your product description and answers pass from your browser to Anthropic's API and are discarded immediately after your report is generated. Away from Keyboard's server acts as a relay, it sends your input to Anthropic and returns the analysis to your browser. The content is never written to disk on our server. We cannot read it. We do not store it. The only things we retain are anonymised aggregate counts, how many assessments were completed, which risk bands were triggered, and only with your explicit consent.
Is my input used to train the AI?
Anthropic's API terms specify that content submitted via the API is not used to train models by default. Away from Keyboard does not provide your assessment content to Anthropic or any other party for training purposes. If Anthropic's policy changes, we will update this page. Anthropic's privacy policy
We have named the AI provider and answered these questions directly because we ask others to be transparent about their technology. We think you should know exactly what happens to what you type.
What makes this different from other tools
35 frameworks at once
Most ethics tools cite one or two frameworks. Alt-TAB applies 35 simultaneously, from the UNCRC and the Disability Discrimination Act 1992 to GDPR and the eSafety First Nations Family Safety resource. The relevant ones are selected based on what your description reveals.
The description comes last. Deliberately.
You describe your product after answering the nine domain questions, not before. By then your answers are already recorded. The system cross-references what you said against what you described, and contradictions are surfaced as findings. Someone who answers "No, this doesn't affect children" and then describes a platform used by families has revealed a gap that a conventional questionnaire would miss entirely.
Four risk types, not a list of problems
Gaps are put into one of four categories: confident ignorance, missing context, false safety, or second-order risks. This taxonomy was developed through analysis of real assessments and refined through complete redesign. It tells you not just what's wrong but what kind of wrong it is.
The law that applies to you specifically
Australian products get Australian law. EU users trigger GDPR. First Nations community impact triggers AIATSIS and CARE Principles. The recommendations aren't generic. They're built from what your description reveals about your context and reach.
Built for the person actually doing it
A community group migrating a contact list has different risk exposure than a startup building an AI product. The tool knows the difference and adjusts: not just cosmetically, but in the questions asked, the frameworks applied, and the language used.
Actually private
What you describe is never stored. The analysis happens in real time and the content is discarded. This isn't a policy. It's how it's built. We couldn't access your product description after the fact even if we wanted to.
This is not a chatbot. Here's what's actually happening.
A common assumption is that Alt-TAB is a wrapper around an AI: that it asks Claude one question and returns whatever comes back. That's not what this is. Every assessment runs a system that took months of deliberate design to build. Here's what's actually happening when you submit your answers.
1
Your assessment type changes everything. And the description comes last deliberately.
Before you answer a single question, you select what kind of decision you are making. That selection restructures the entire prompt. But there is a second design decision worth naming: the plain-language description of your product appears after the nine domain questions, not before. This is intentional. By the time you describe your product, you have already declared your answers. The system then compares what you said with what you described, and gaps between them are often the most important finding. A builder who answers "No, this doesn't affect children" and then describes a family finance app has revealed something their answers alone would not show. That contradiction is scored, named, and surfaced as a finding.
2
Stage 1 runs four simultaneous analyses
When you submit your nine domain answers and product description, the system runs four layers at once: consistency checking (does your description contradict your answers?), mandatory screening (technology-facilitated gender-based violence and cybersecurity are checked in every assessment regardless of answers), jurisdiction calibration (which legal frameworks apply?), and compounding risk detection (which combinations of answers create multiplied risk?). The follow-up questions are then written fresh for your specific situation, not drawn from a question bank.
3
The scoring formula is explicit and fixed
The readiness score is not a vibe. Stage 1 answers carry 30% of the score. Follow-up responses carry 50% (yes = full credit, partially = half credit, no = no credit). Deployment strategy quality carries 20%. The formula is written into the prompt. The AI applies it, it cannot change the weights or invent a different system. When you answer "partially" and add detail, that detail genuinely improves your score because it signals real engagement with the risk.
4
Capacity detection adapts the report
The system detects whether you're likely a small organisation, community group, or sole trader based on language patterns in your description. If you are, a different set of instructions fires: the AI is told to prioritise three things over listing everything, lead with free resources rather than recommending consultants, and write in plainer language. The prompt changes. The report changes. You may not notice, but the difference is intentional.
5
Framework injection is conditional, not blanket
The 35 frameworks are not all cited in every report. The system has two tiers. Tier 1 frameworks (UNESCO AI Ethics, OECD AI Principles, NIST AI RMF, UN Guiding Principles on Business and Human Rights) apply to every assessment. Tier 2 frameworks are injected only when specific domains are flagged: the ACCCE research on child exploitation appears only when children or interaction domains are triggered. The Victorian MARAM Framework appears only when harm severity is flagged in an Australian jurisdiction. The frameworks cited in your report are the ones that apply to your situation.
6
The blindspot taxonomy is a design decision
The four categories: confident ignorance, missing context, false safety, and second-order risks, are not labels the AI invented. They are a taxonomy developed through analysis of real assessment patterns and encoded into the system as a classification requirement. The AI must assign every gap to one of these four types and justify the assignment with evidence from your submission. This structure is what produces a structured report rather than a list of generic concerns.
The AI is the analytical engine. The methodology, the framework selection, the scoring formula, the risk taxonomy, the capacity calibration, and the two-stage structure are the product. They took months to develop and test. Asking Claude one question about your product would give you a generic response. Alt-TAB gives you a structured assessment against 35 governance frameworks with jurisdiction-specific legal obligations identified and a scored 30-day action plan. The difference is the design.
How it makes a difference
Alt-TAB makes impact in three ways.
Better decisions before launch
By surfacing threat indicators, misuse pathways, exploitation risks, coercive control vectors, and unsafe data practices before deployment, the tool helps organisations identify gaps between what they think their risk is and what it actually is.
Access to ethical governance for everyone
As a free tool, it enables small businesses, startups, educators, and community organisations to apply structured risk logic without needing dedicated legal, cybersecurity, or compliance teams. Ethics governance shouldn't be a service only well-resourced organisations can afford.
Cultural change over time
By normalising upstream ethical reflection as part of innovation, Alt-TAB helps reframe technology development from "move fast" to "build responsibly." When organisations learn to identify harm pathways before they exist, innovation becomes more equitable, more resilient, and more trustworthy.
Alt-TAB is not anti-innovation. It's pro-responsibility. You can move fast and still build safely, when safety is embedded from the start rather than bolted on after the fact.
The 35 frameworks behind every assessment
Applied simultaneously, selected based on your product description, jurisdiction, and user context. Two areas, technology-facilitated gender-based violence and cybersecurity, are checked in every single assessment regardless of your answers, because these risks are almost always invisible to builders. Plain-language explainers for every framework are on the Resources page.
MARAM: Multi Agency Risk Assessment and Management Framework
Family Violence Protection Act 2008 (Victoria)
Our Watch: Change the Story Framework
National Plan to End Violence Against Women and Children 2022-2032
Safe and Equal MARAM Practice Guides
UN Convention on the Rights of the Child
UNICEF Guidance on AI & Children 3.0
UK Age Appropriate Design Code
ACCCE Child Exploitation Research
UNESCO Recommendation on the Ethics of AI
OECD AI Principles
NIST AI Risk Management Framework
UN Guiding Principles on Business & Human Rights
Global Digital Compact (UN)
Privacy Act 1988 & Australian Privacy Principles
Online Safety Act 2021 (Australia)
eSafety Commissioner Safety by Design
eSafety Technology-Facilitated Gender-Based Violence Industry Guide
eSafety Phase 2 Industry Codes
eSafety Self-Harm Material Guidance (2026)
eSafety Women in the Spotlight
eSafety First Nations Family Safety
ASD Essential Eight
NAIC Voluntary AI Safety Standard
NAIC Practical Guides and Learning Hub
National Framework for Assurance of AI in Government
eSafety Tech Trends and Challenges
UN Convention on the Rights of Persons with Disabilities
Disability Discrimination Act 1992 (Australia)
WCAG 2.2 Accessibility Guidelines
Australian Digital Accessibility Toolkit
CARE Principles for Indigenous Data Governance
AIATSIS Code of Ethics
Design Justice Network Principles
Childlight / University of Edinburgh Research
From prototype to what it is today
The foundation of Alt-TAB was laid through the Victorian Summer of Cyber program, a research initiative run by the Australian Women in Security Network. Between January and April 2026, two student researchers, Camille Ang and Hashini Thanushika, worked with Away from Keyboard under supervision from Dr Muna Al-Hawawreh to review governance frameworks, conduct early methodology development, build a first digital prototype, and run alpha and beta testing with real users.
That work was valuable. It confirmed what the evidence already suggested: governance frameworks exist, but most organisations making technology decisions can't access or interpret them. The prototype demonstrated real demand for a practical, accessible tool. User feedback was clear about what was missing: the outputs were the same regardless of what you put in. Every scenario got essentially the same generic response. That wasn't good enough.
After the program concluded, Away from Keyboard rebuilt the tool from scratch. The current version of Alt-TAB is a complete redesign, developed and independently beta tested by Sarah Barnbrook with a separate group of users across different sectors and organisation types. That second round of testing shaped the report structure, the plain-language framing, and the action plan format.
The two-stage analysis engine, the consistency detection layer, the four risk pattern taxonomy, the 35 frameworks applied simultaneously, the jurisdiction-aware recommendations, the mandatory technology-facilitated gender-based violence and child safety screening in every assessment, the tailored follow-up questions written fresh for each submission, and the legal versus ethical separation in the report: none of that existed in the prototype. It was designed, built, and deployed by Sarah as a complete rebuild.
Camille and Hashini laid important groundwork with the prototype. The current tool represents what became possible when that groundwork was rebuilt from the inside out and validated through independent testing.
Original prototype: Victorian Summer of Cyber program, supervised by Dr Muna Al-Hawawreh. Current tool: designed, rebuilt, and independently beta tested by Sarah Barnbrook, Away from Keyboard Inc. Full research report available on request.
Peer review status
Alt-TAB is currently in the process of seeking independent academic peer review of its methodology. We are actively reaching out to researchers in the fields of technology ethics, child safety, privacy law, and human rights to review the assessment framework, the framework selection logic, and the consistency detection approach. We are transparent about this because intellectual honesty requires acknowledging that the tool has not yet been externally validated at a formal research level. The Victorian Summer of Cyber program provided supervised student research input to the prototype. The current tool represents a complete redesign that has not yet undergone formal peer review. We believe it reliably identifies risks that builders had not considered — the assessment outputs support this — but we are committed to strengthening that evidence base through independent review. If you are a researcher in a relevant field and would like to engage with this work, we welcome that. Contact us at info@afk.org.au.
What Alt-TAB cannot do
Intellectual honesty requires naming what this tool is not.
It is not a guarantee
Completing an assessment and acting on the recommendations does not guarantee your product is safe, ethical, or legally compliant. It means you asked structured questions and addressed the gaps found. That is genuinely valuable. It is not the same as certainty.
It focuses on common harm patterns
Alt-TAB screens for the most common categories of technology-facilitated harm: intimate partner violence, child exploitation, cybersecurity gaps, data privacy failures, and accessibility barriers. It does not comprehensively screen for all forms of harm. State surveillance of journalists or protesters, corporate monitoring of workers, and algorithmic discrimination are areas where Alt-TAB provides partial coverage at best. For products operating in these contexts, specialist assessment is needed alongside this tool.
Risks are flagged, not confirmed
When Alt-TAB identifies a gap, it means the assessment found no evidence that the risk has been addressed. It does not mean the risk definitely exists or that no safeguard is in place. Builders often have safeguards they didn't mention in their description. Treat every flag as a question to investigate, not a confirmed problem.
Results vary between runs
The same product description run twice may produce different blindspots, a different score, and different recommendations. This is a known property of AI-generated analysis. Think of it as two qualified professionals reviewing the same situation: the structured questions are consistent, the conclusions may differ in emphasis.
It cannot assess implementation quality
Alt-TAB can identify that you have no documented incident response plan. It cannot verify whether the plan you say you have is effective. The tool assesses what you tell it. Honest answers produce useful results. Optimistic answers produce a score that does not reflect reality.
It is not legal advice
When Alt-TAB identifies a legal obligation, it is flagging that a framework applies to your situation. It is not providing advice specific to your circumstances and is not a substitute for legal counsel. If a recommendation says "you may be required to..." that is a prompt to seek advice, not a legal opinion.
The data has limits
The statistics on this site reflect assessments since May 2026 when privacy-preserving tracking began. The sample self-selects: people who run an ethics assessment are already more likely to be thinking carefully about these issues. The 97% figure reflects what was found in people who chose to use the tool.
Coverage is not exhaustive
Alt-TAB applies 35 frameworks, calibrated for common risk patterns in Australian and international contexts. Novel risks, highly specialised domains, or products with unusual technical architectures may require specialist assessment beyond what the tool covers.
It has not yet been formally peer reviewed
The tool has been developed through structured methodology, tested against real assessments, and refined through user feedback. It has not yet undergone formal independent academic peer review. We are actively seeking this and are transparent about its absence. The outputs are grounded in established governance frameworks, but the overall assessment methodology has not been externally validated as a research instrument. We use it as decision-support, not as a validated measurement tool.
It can over-flag
Alt-TAB is designed to be cautious. It will sometimes flag a risk that, on closer inspection, does not apply to your specific product. This is intentional: a false positive is far less harmful than a missed risk. Treat every flag as a question to investigate, not a confirmed problem.
These limitations are not reasons to avoid the tool. They are reasons to use it as one part of a responsible development process, not as a single checkpoint before launch.
Privacy & data
What you describe is never stored. Your product description, deployment strategy, and follow-up answers stay on your screen. The only things we keep are anonymised counts, domain flags and readiness bands, and only if you explicitly consent.
No personal information. No IP addresses. No accounts. Alt-TAB was built to practise what it assesses.
If you have consented to anonymised research data from your assessment and wish to withdraw that consent, email info@afk.org.au. Because no individual records are created, withdrawal removes your assessment from the aggregate counts where technically possible.
If you believe your report contains a material error (a citation that resolves to the wrong document, a finding that contradicts your answers, or a legal obligation that does not apply to your jurisdiction), email info@afk.org.au with your verification reference code and a brief description of the concern. We will review the assessment inputs and respond within five business days. We cannot regenerate a report retroactively, but we can clarify the finding, correct the record, and use the feedback to improve the tool.
The only exception is the optional 30-day check-in email. If you provide your email address after your assessment, it is stored only long enough to send one follow-up email 30 days later. The email address is permanently deleted immediately after that email is sent. It is never used for any other purpose.
On reproducibility: Running the same product description twice will produce different results. This is a known property of AI-generated analysis. The verification code records a specific run, its score, and its date. It is evidence that an assessment was completed and taken seriously, not a claim that the score is the only valid result for that product.
Assessment verification codes
Every completed assessment generates a reference code in the format AT-YYYYMMDD-XYZZ-CC. This code confirms the date, assessment type, awareness level, and readiness band of an assessment. It does not store or reveal your product description or any identifying information.
You can share this code with a funder, board, or client as evidence that an assessment was completed. For a verifiable record we can co-sign or confirm independently, contact AFK directly.
Common questions about privacy
Is it really free? Yes. No account, no credit card, no catch. The full assessment and PDF report are always free.
Does anyone see what I type? No. What you describe is processed in real time and discarded immediately. We couldn't access your product description after the fact even if we wanted to. The architecture is designed to make that impossible, not just the policy.
