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Testers

Testing paths for people, teams, builders, job seekers, developers, researchers, and early users who want to help inAi improve products, Open Source tools, agent-facing software, education pages, and public website flows.

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Help test inAi’s work

inAi builds AI-native products and open technology for the intelligence era.

Some work can be understood from strategy. Some can be explained in research. Some can be shown through public repositories. But products become real when they meet real people, real workflows, real data shapes, real edge cases, real devices, real confusion, and real expectations.

This page is for people and teams who want to help test inAi’s work.

Testing can mean many things: trying a product before wider release, reporting a broken flow, testing an Open Source repository, giving structured feedback on a career workflow, checking whether an AI explanation is understandable, reviewing agent-facing documentation, or helping us see where a product makes sense in the real world.

Testing is not a guarantee of access, employment, payment, partnership, product launch, or public availability. It is a serious feedback path for people who want to help inAi improve what it builds.

Pilots / Corporate partnersPageMindEmploContactOpen SourcePartnersHow We BuildAI in the Real World

Who this page is for

This page is for people who can give useful, specific, honest feedback.

The best testers do not need to be famous, senior, or technical. They need to be precise.

Specificity is respect for everyone’s time.

  1. A potential PageMind user who works with product data, supplier information, catalog workflows, retail operations, multilingual product pages, or product compliance support.
  2. A job seeker interested in testing Emplo as an AI career agent before wider release.
  3. A developer who wants to test an inAi Open Source project and report what works, breaks, or needs clearer documentation.
  4. An AI builder interested in agent-facing tools, MCP-style workflows, agent-readable documentation, memory, code workflows, or software built for agents to use.
  5. A researcher or technical reader who can review whether an explanation, research page, or AGI-related argument is clear and coherent.
  6. A nontechnical reader who can tell us when an AI explanation is too complicated, too vague, too technical, or not useful enough.
  7. A product operator who can describe a real workflow and where AI would or would not help.
  8. A designer, engineer, analyst, student, founder, operator, or builder who can test public pages, forms, flows, language, and user experience.
  9. A company or institution that wants to test a product in a controlled context before considering a deeper pilot.
  10. A careful user who can report bugs, edge cases, confusing copy, broken links, broken forms, or unclear routes.

Why testing matters to inAi

inAi is not building around one app, one workflow, or one isolated feature.

The company works across business products, consumer products, Products for Agents, Open Source, AGI / Research, AI for Everybody, Trust, and public website infrastructure. Those layers are connected by one thesis: AI is becoming a new operating layer for software and work, and inAi exists to bring that layer into real products and systems.

Testing is where that thesis becomes practical.

A product can sound correct in writing and still fail in use. A tool can be powerful and still be hard to install. A page can be accurate and still be unclear. A career assistant can be useful in theory and still miss what job seekers actually need. An agent-facing tool can expose a callable interface and still be uncomfortable for agents or developers to operate. A contact route can exist and still send people to the wrong place.

Testing helps us close that gap.

Testing is not decoration. It is how the public architecture becomes usable.

What testers can help with

PageMind testing

PageMind is inAi’s current business product. Useful PageMind testing may involve checking whether the page explains the product clearly, describing real catalog workflows and product-data pain points, testing pilot request flows, identifying overexplained or underexplained copy, helping understand product-data formats and operational constraints, or participating in selected controlled testing if there is a good fit. PageMind is not public self-serve software by default.

PageMind

Emplo testing

Emplo is inAi’s current consumer product: an AI career agent for job seekers. Useful Emplo testing may involve joining the waitlist, giving feedback on career-search workflows, reviewing whether the page explains the product clearly, testing onboarding or waitlist flows if invited, describing what job seekers need, identifying where the product should stay user-controlled, and helping avoid spam-automation or job-guarantee language.

Emplo

Products for Agents testing

Products for Agents is inAi’s category for software built so AI agents can use it. Testing may involve reviewing agent-facing documentation, testing tool descriptions, trying MCP-style or tool-call workflows where public tools exist, identifying whether a workflow is agent-operable or only human-operable, testing state, memory, instruction, retry, and output patterns where publicly available, or giving feedback on software for the agent era.

Products for Agents

Open Source testing

Useful Open Source testing may involve installing a repository, checking setup instructions, running examples, reporting bugs, opening issues, proposing documentation fixes, testing workflows across operating systems or environments, checking whether a project status label is clear, and helping a tool become easier for other builders to use. Open Source testing should usually start on GitHub.

Open Source

AI for Everybody testing

AI for Everybody is inAi’s public explanation layer. Useful testing may involve checking whether an explanation is understandable to a nontechnical reader, identifying jargon, suggesting simpler examples, reviewing whether fears or public questions are handled honestly, and telling us whether a page feels too technical, too promotional, too vague, too cautious, or too optimistic.

AI for Everybody

AGI and Research readability testing

Useful testing may involve checking whether the AGI as a System thesis is understandable, identifying where the page accidentally sounds like a product claim, reviewing research-direction pages for clarity, suggesting better cross-links between AGI, Products for Agents, Research, and AI for Everybody, and telling us where a technical argument needs an accessible explanation. This is not peer review, unless a separate research collaboration is arranged.

AGI as a System

Website and routing testing

The website itself is part of the product experience. Useful testing may involve broken links, unclear navigation, confusing Contact routes, unclear Products dropdown behavior, pages that send users to the wrong destination, footer problems, mobile layout issues, forms that do not submit, unclear CTAs, duplicated or stale copy, and pages that sound like old inAi instead of current inAi.

Contact
Pilots / Corporate partnersSoftware for the agent eraOpen Source ApproachAcademia / Research

What makes feedback useful

Useful feedback is specific enough to act on.

A strong tester report usually includes what you were testing; the page, product, repository, form, or workflow involved; what you expected; what happened instead; device, browser, operating system, environment, or tool version where relevant; steps to reproduce; screenshots only if they do not expose private data; sample files only if public, synthetic, or explicitly safe; why the issue matters; and your suggested fix if you have one.

  1. Good report: I tried to use the Contact route for tester interest. I expected to find a product-testing option, but the form only offered general contact and waitlist. I was on mobile Safari. I suggest adding a dedicated Testing route under Product and Open Source.
  2. Good report: The Products for Agents page explains that agents need tools and state, but I did not understand how this differs from a normal API. A short example comparing a human UI, an API, and an agent-facing workflow would help.
  3. Good report: I tried to install the repository on macOS and the setup instructions missed one dependency. Here are the commands I ran, the error message, and the line in the README where the fix could be added.
  4. Weak report: Your website is confusing.
  5. Weak report: I want to test AI. Send me something.
  6. Weak report: I found a bug but I do not remember where.

What inAi looks for in testers

inAi does not need testers who only say yes.

We need testers who can think clearly, report honestly, and understand the difference between a product idea, a public page, an Open Source project, a private beta, a pre-launch product, a research stance, and a future-facing category.

Both technical and nontechnical testers matter.

  1. They use the kind of workflow the product is meant to improve.
  2. They can explain real constraints from their work, studies, job search, development environment, or organization.
  3. They can write clear feedback.
  4. They can distinguish “I dislike this” from “this is unclear or broken because…”.
  5. They understand that early products change.
  6. They can respect confidentiality when invited into private tests.
  7. They do not submit sensitive data without a clear reason.
  8. They can test without demanding immediate support or custom features.
  9. They can be direct without being vague.

What testers can expect

Testing access depends on the product, page, project, and maturity stage.

Some testing is public: reading pages, reporting broken links, reviewing Open Source repositories, opening GitHub issues, checking documentation, testing public forms, and giving general website or explanation feedback.

Some testing is selected: PageMind product tests, Emplo waitlist / pre-launch tests, Products for Agents private alpha or internal-use candidate testing, company workflow tests, structured feedback sessions, research-adjacent review, and institutional or partner testing.

Some testing may require a short screening conversation, a specific use case, a safe test dataset, a company or institutional context, an NDA or confidentiality terms, consent for feedback processing, or a controlled pilot or partner path rather than a public tester route.

  1. Testing does not guarantee product access.
  2. Testing does not guarantee paid work.
  3. Testing does not guarantee employment.
  4. Testing does not guarantee internship selection.
  5. Testing does not guarantee public credit.
  6. Testing does not guarantee roadmap influence.
  7. Testing does not guarantee a feature being built.
  8. Testing does not guarantee immediate support.
  9. Testing does not guarantee a response to every suggestion.
  10. Testing does not guarantee a long-term relationship.

Testing areas

PageMind product or catalog workflow testing

Best route: PageMind or Pilots / Corporate partners.

PageMind

Emplo pre-launch testing

Best route: Emplo or Contact.

Emplo

Products for Agents testing

Best route: Products for Agents or Contact.

Products for Agents

Open Source repository testing

Best route: Open Source and the relevant GitHub repository.

Open Source

AI for Everybody readability testing

Best route: AI for Everybody or Contact.

AI for Everybody

AGI / Research page feedback

Best route: AGI, AGI as a System, or Academia / Research.

AGI

Website, navigation, link, or form issues

Best route: Contact.

Contact

Company pilot testing

Best route: Pilots / Corporate partners.

Pilots

Student testing / internship-related testing

Best route: Internships.

Internships

Contribution-oriented testing

Best route: Contributions.

Contributions

What to send

A good tester message should be short, structured, and specific.

Include who you are; what you want to test; your testing context; what you can provide; technical environment if relevant; availability; data boundary; and relevant links.

Example: I would like to test Products for Agents documentation and agent-facing tool workflows. I am a developer working with AI automation and MCP-style tools. I can test setup instructions, tool descriptions, example prompts, and whether the documentation is clear enough for agent-assisted use. I can provide one structured report per week for one month. I will not send private customer data.

Example: I am a job seeker interested in Emplo. I can test whether the waitlist flow, value proposition, and user-control language are clear. I can give feedback from the perspective of someone applying for internships and entry-level roles. I am not asking for job guarantees or automatic application sending.

Example: I work with product data and catalog enrichment in retail. I would like to test whether PageMind matches the workflow we actually have: supplier PDFs, images, product attributes, multilingual descriptions, and export into catalog templates. We can discuss whether this should be a pilot route rather than a tester route.

What not to send

Do not send sensitive or confidential material unless inAi explicitly asks for it through a controlled route.

When possible, use public examples, synthetic examples, anonymized examples, short descriptions of the workflow rather than underlying private files, and controlled pilot channels for sensitive business testing.

If the issue is security-sensitive, do not publish it publicly. Use the appropriate contact route or legal/security contact path.

  1. Credentials.
  2. Passwords.
  3. API keys.
  4. Private customer data.
  5. Confidential supplier data.
  6. Candidate data.
  7. Private resumes or personal documents unless a product-specific route explicitly requests them.
  8. Proprietary datasets.
  9. Private company documents.
  10. Security exploit details through a public form.
  11. Illegal, harmful, or abusive content.
  12. Screenshots containing private information.
  13. Large attachments without prior agreement.
  14. Confidential materials from your employer, school, client, or customer without permission.

Testing and trust

Testing requires trust in both directions.

inAi needs useful feedback without receiving unnecessary private data. Testers need to know that the company will not pretend early testing means public launch, guaranteed access, or unrestricted automation.

The principle is simple: Use the least sensitive data that can test the point.

For PageMind, that may mean describing a catalog workflow before sending any file. For Emplo, that may mean reviewing copy or onboarding before sharing personal career material. For Open Source, that may mean reporting environment errors without exposing private system details. For Products for Agents, that may mean testing documentation or tool behavior without exposing private prompts, credentials, or internal systems. For AI for Everybody, that may mean reviewing readability rather than debating every frontier-lab claim.

How We BuildLegal

Testing is not the same as a pilot

Testing and pilots overlap, but they are not the same.

A tester may try a page, tool, flow, repository, explanation, form, or product concept and report feedback. A pilot is usually more structured. It may involve a company, workflow, expected outcome, scope, confidentiality, operational constraints, and a clearer relationship.

  1. If you are an individual giving feedback, use Testers.
  2. If you are a company testing a workflow, use Pilots / Corporate partners.
  3. If you are a retailer or catalog operator interested in PageMind, use PageMind or Pilots / Corporate partners.
  4. If you are testing a public repository, use Open Source and GitHub first.
  5. If you want to contribute improvements, use Contributions.
  6. If you want formal research collaboration, use Academia / Research.

Testing is not a job application

Testing can sometimes lead to a deeper relationship, but it is not the same as work, employment, internship, contracting, or investment.

If you want to work with inAi, use Work with us. If you want an internship, use Internships. If you want to contribute public work, use Contributions. If you want to test products or pages, use this page.

You can mention multiple interests in one message, but the more specific you are, the easier it is to route correctly.

Work with usInternshipsContributions

What inAi can offer testers

Depending on fit and maturity stage, inAi may offer early access to selected flows, structured feedback sessions, public repository testing paths, product-page review opportunities, AI explanation review, selected private tests under confidentiality, a route into pilots, contributions, internships, or work conversations if the fit becomes clear, acknowledgement where appropriate and permission-safe, and clearer public pages because your feedback made them better.

inAi should not promise payment unless separately agreed, product access for every tester, support for every environment, public credit for every report, acceptance of every suggestion, immediate fixes, long-term beta participation, commercial rights, discounts, or partnership status by default.

Testing is valuable because it improves real work. It should stay practical.

How to contact inAi about testing

Use Contact when available and choose the route closest to testing.

Suggested contact route: Contact -> Testing / early feedback.

If a general email route is needed, use contact@inai.world.

Suggested subject line: Tester interest — [Area] — [Your name].

For company pilots, use partnerships@inai.world with subject line Pilot testing — [Company / workflow] — [Your name].

For research-page or academic testing, use research@inai.world with subject line Research feedback — [Topic] — [Your name].

ContactEmail contact@inai.worldEmail partnerships@inai.worldEmail research@inai.world

Contact route

Product testing

Use Contact, PageMind, or Emplo, depending on the product.

Contact

Company pilot testing

Use Pilots / Corporate partners or email partnerships@inai.world.

Pilots

Open Source testing

Start from Open Source and use the relevant GitHub repository where possible.

Open Source

Agent-facing software testing

Start from Products for Agents and Software for the Agent Era, then use Contact if no public repository or form applies.

Products for Agents

AI explanation testing

Start from AI for Everybody and use Contact for feedback.

AI for Everybody

Research and AGI feedback

Use AGI, AGI as a System, or Academia / Research, depending on whether your feedback is informal or research-collaboration oriented.

AGI

Website issues

Use Contact for broken routes, forms, typos, mobile problems, or confusing navigation.

Contact

Related pages

PartnersContactWork with usInternshipsContributionsPilots / Corporate partnersAcademia / ResearchInvestorsGrantsGovernment / Public institutionsBusiness ProductsConsumer ProductsProducts for AgentsSoftware for the agent eraOpen SourceOpen Source ApproachPageMindEmploAGIAGI as a SystemAI for EverybodyHow We BuildAI in the Real WorldAboutFAQLegalPrivacy