Corporate pilot and product-collaboration page for companies, retailers, catalog teams, operations teams, product leaders, technical teams, AI transformation teams, and organizations interested in scoped real-world AI pilots with inAi.
AI becomes useful when it touches real work.
Not only a demo. Not only a model. Not only a slide about productivity. Real inputs, real workflows, real users, real constraints, real review, real systems, and a clear reason to build.
inAi builds AI-native products and open technology for the intelligence era. Some of that work can move faster with corporate partners: companies that have real operational problems, product data, catalog workflows, technical systems, agent-facing software needs, developer workflows, or AI adoption questions that are concrete enough to test.
This page is for companies and organizations that want to explore a scoped pilot or product collaboration with inAi.
It is not a generic demo request page. It is not a consulting request page. It is not a promise that every company inquiry becomes a pilot. It is a route for serious companies that can describe a real workflow, constraint, user group, and desired outcome.
This page is for organizations that can work with inAi around real workflows.
You may also fit if you are not sure which inAi product or category is relevant, but you can describe the workflow clearly enough for us to evaluate it.
A strong corporate pilot starts from a specific problem, not from the sentence “we want to use AI.”
A pilot is a scoped collaboration around a real workflow.
It should define the problem, users or team, inputs, desired outputs, current process, constraints, review needs, data boundaries, success criteria, timeline, and the next decision after the pilot.
A pilot is not the same as a public launch. It is not an endorsement. It is not a promise of production deployment. It is not a guarantee that the product will solve every edge case. It is a controlled way to test whether an AI-native product, workflow, or system can create value in a real environment.
The point is not to force every company into one product. The point is to find the right route for real-world AI work.
AI adoption often fails for practical reasons.
The company sees impressive demos, but the real workflow is messier. Data is incomplete. Inputs arrive in different formats. Review is still needed. Existing systems are rigid. Teams do not know whether AI should automate, assist, structure, classify, explain, or simply prepare work for human judgment. Technical teams may want agents, but their current software was built only for humans using screens.
inAi is useful because we build around those realities.
We are not only interested in AI as a feature. We are interested in AI as a product layer: software that can read messy inputs, structure work, connect steps, expose tools, support review, and become useful inside real systems.
The value of a pilot is not only the output. The value is learning what AI can actually do in the company’s context.
A useful pilot needs more than a model call.
inAi can bring AI-native product thinking, business workflow understanding, products for different operators, honest maturity framing, and research and systems thinking.
The best corporate partners bring reality.
That may include real workflows, real users, real input examples, real constraints, domain knowledge, operational context, product-data problems, technical systems, evaluation criteria, review processes, team feedback, a clear decision-maker, a budget or pilot path if commercial, willingness to test early products honestly, permission-safe public feedback if agreed, technical collaboration for agent or Open Source work, and a specific business problem that is not easy to solve with generic software.
A pilot partner does not need perfect data or a perfect process. In many cases, the messiness is the reason AI-native products matter. But the partner must be able to describe the mess clearly enough to make the work scoped.
A strong pilot usually has a real workflow, defined team or user group, clear operational bottleneck, relevant inputs that can be shared safely, an output that can be evaluated, a clear reason AI may help, understood review or approval requirements, discussed data sensitivity, realistic maturity expectations, no exaggerated claims, a practical next step, structured feedback, proportional legal/privacy/procurement/security expectations, and connection to at least one real inAi layer.
A company can expect inAi to be direct about fit.
If a workflow does not fit current product categories, we should say so. If a product is private beta, pre-launch, experimental, or research-facing, we should label it honestly. If a request needs legal, procurement, data-protection, or security review, we should not pretend it can be handled casually.
Many pilots involve operational or product data.
Do not send confidential customer data, supplier data, candidate data, credentials, regulated data, security-sensitive information, private datasets, proprietary documents, or personal data through a general inquiry.
A useful first message can describe the type of data without attaching the data itself.
Before any sensitive data is shared, both sides should define purpose, data categories, legal basis where relevant, confidentiality expectations, access limits, retention expectations, deletion expectations, output ownership, review process, security requirements, and whether formal legal documents are needed.
The general pilot route is not a data-transfer mechanism.
A pilot is not automatically a public partnership.
inAi will not publicly claim a partnership, customer status, production deployment, successful outcome, product validation, endorsement, certification, institutional approval, enterprise readiness, performance metrics, ROI, legal/compliance approval, or public launch unless the wording is true, permission-safe, and agreed.
Useful public wording may be modest: inAi is exploring a scoped pilot with [company]; inAi is testing PageMind with selected retail/catalog workflows; inAi is working with selected partners to understand real product-data workflows; or inAi is discussing agent-ready software with selected technical partners.
Some pilots should remain private. That is normal. A serious pilot does not need to become a press release.
If your company is interested in PageMind, the strongest message is concrete.
Useful context includes company type, product category, catalog size if safe, current product-data workflow, input types, target catalog system, target languages, review needs, quality problems, compliance-support needs if relevant, whether the goal is exploration, pilot, or procurement, whether a sample can be prepared later, and desired timeline.
Do not send confidential supplier files, product datasets, credentials, or customer data through a general message.
Suggested subject line: PageMind pilot — [Company] — [Catalog / Product data scope].
If your company is interested in Products for Agents, the strongest message should be technical and concrete.
Useful context includes what kind of agent or agent-controlled workflow you are exploring; what tool, API, documentation, memory, state, or workflow surface is involved; whether the user is a developer, internal team, customer, or AI agent; what the agent needs to call, read, control, or continue; whether the work is internal, developer-facing, Open Source, or product-facing; what success would look like; what should remain controlled or reviewed; and whether this is research, product discovery, technical integration, or pilot.
Do not frame this as buying agents from inAi. Do not send private architecture, credentials, internal prompts, sensitive logs, or private datasets through a general inquiry.
Suggested subject line: Products for Agents pilot — [Company] — [Workflow / Tool surface].
If your company is interested in Open Source or developer tooling, start from the repository or tool context.
Useful context includes which project is relevant, what you tried, what workflow it supports, what worked, what failed, what improvement you need, whether you want to contribute, test, sponsor, document, integrate, or discuss, and whether the work can remain public or must stay private.
For issues or code contributions, GitHub may be the best route. For broader collaboration, use Contributions or Contact.
Suggested subject line: Open Source collaboration — [Project] — [Company].
If your company wants to understand AI, agents, AGI, or AI-native product thinking before starting a product pilot, the inquiry should be framed as education or briefing, not product deployment.
Useful context includes target audience, what they need to understand, whether the focus is AI basics, agents, AGI as a system, business operations, jobs, productivity, Open Source, or trust, whether this is an internal workshop, written guide, leadership briefing, product-team session, or public-facing content, desired timeline, and whether the output is private or public.
Suggested subject line: AI briefing — [Company] — [Audience / Topic].
A useful pilot or corporate-partner message should include company name, your role, collaboration type, workflow or problem, current process, inputs, desired output, users or operators, constraints, pilot size and timeline, decision after pilot, and public/private expectations.
Do not attach sensitive data in the first message.
Do not send an initial message that only says: We want AI for our company. Can you build us a chatbot? Can you automate our whole business? Can we announce a partnership? We have data, can you process it? We want agents to replace our employees.
Those messages are too vague or misaligned.
Describe the data type first. Transfer data only after scope, legal, privacy, and security expectations are clear.
Use Contact when available and choose the closest pilot or corporate-partner route.
Suggested contact route: Contact -> Pilots / Corporate partners.
If your inquiry is specifically about PageMind, use Contact -> PageMind pilot or start from PageMind.
If email is needed, use partnerships@inai.world.
Suggested subject line: Corporate pilot — [Company] — [Scope].
For research-only collaboration, use Academia / Research or research@inai.world. For investment, use Investors. For grants or public funding, use Grants. For public-sector or institutional work, use Government / Public institutions.
