Companies and teams
For organizations that need AI inside a real workflow, product, process, database, document flow, or internal tool.
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Services
Custom AI systems and integrations for organizations with specific problems.
Some AI needs can be served by products. Others need direct work: understanding the problem, designing the right system, preparing data, building the application, integrating it into existing tools, and supporting it after launch.
inAi Services is the custom path for companies, teams, founders, public institutions, and other clients who need AI to work inside a real situation. You bring the problem, context, approvals, and required access. inAi helps define the solution, build it, integrate it, and keep it useful where ongoing support is agreed.
inAi builds products for repeatable needs: business products, consumer products, products for agents, and Open Source projects. Services are different. They are for situations where the solution has to be designed around a specific client, workflow, product, dataset, infrastructure, or operational problem.
That may mean adding AI to an existing product, creating a private assistant over internal knowledge, building a document-processing workflow, preparing data for AI use, creating an AI analysis layer, testing a prototype, or designing a multi-step AI workflow with tools and review points. The important starting point is not the technology label. The important starting point is the problem that needs to be solved.
Products are what inAi builds for repeatable needs. Services are how inAi helps solve specific AI problems with clients.
AI Services are for clients who need more than advice and more than a generic chatbot. The right fit is a real problem that needs diagnosis, design, execution, integration, and support.
That can include companies adding AI to their own software, teams with manual document or data workflows, founders testing AI product ideas, public institutions handling complex information, research or operational groups working with specialized data, and organizations that need AI connected to existing tools, APIs, documents, databases, or internal processes.
The client does not need to arrive with the full architecture already decided. It is enough to understand the problem, the current workflow, the people affected, the available data or systems, and what a useful result would look like.
For organizations that need AI inside a real workflow, product, process, database, document flow, or internal tool.
For people testing whether an AI product idea, MVP, or feature can become a working system.
For public, research, educational, or operational teams with information-heavy problems that need careful implementation.
For individuals with substantial AI implementation needs where the work requires custom design, build, and support.
These are example scenarios, not public client case studies. Client work is confidential by default, and inAi does not publish client names, project details, screenshots, datasets, results, or case studies unless the client agrees.
Example scenario 01
A company has policies, PDFs, procedures, training files, research, technical notes, or internal documents spread across folders and tools. inAi can build a private assistant that searches approved sources, answers questions, summarizes material, and gives teams a more usable way to work with internal knowledge.
Custom AI work is not only model selection or prompt writing. A useful system usually needs the right workflow, data preparation, product logic, integrations, evaluation, deployment path, and support model.
Depending on the project, inAi can help with problem definition, AI system design, data preparation, model/API setup, retrieval or knowledge workflows, application logic, user experience, integrations with existing tools, deployment planning, hosting setup, monitoring, handover, and ongoing improvement.
The goal is simple: the client should not need to manage the AI stack alone. inAi can organize the technical route needed to deliver and operate the solution, while the client provides the business context, access, approvals, and validation needed for the work to succeed.
We start with the workflow, users, data, systems, constraints, and outcome — not with a generic AI feature.
We choose the right shape for the solution: assistant, retrieval system, data workflow, product feature, prototype, automation layer, agentic workflow, or custom application.
We create the application logic, AI behavior, data connections, interfaces, APIs, exports, and integrations needed for the solution to work in practice.
Where agreed, we support the system after delivery through testing, handover, maintenance, updates, monitoring, and further improvements.
Services work often involves a client’s private documents, workflows, data, product plans, internal tools, users, or business context. That work stays private by default.
inAi does not publish client names, project details, screenshots, datasets, results, outputs, testimonials, or case studies unless the client agrees. Some clients may later choose to appear as public references or case studies. That is optional. The default is confidentiality.
AI projects should not end as fragile demos that nobody can maintain. A Services project can include testing, handover, documentation, post-launch support, maintenance, monitoring, model or provider updates, workflow improvements, and new feature development where agreed.
The level of support depends on the project. Some clients need a focused prototype and handover. Others need an integrated system with ongoing support. Others need continued development as the workflow evolves. inAi can define that with the client before work begins, so delivery and long-term responsibility are clear.
AI Services are priced by project because each problem is different. A small prototype, an internal assistant, a product integration, a data workflow, and a long-term supported system do not have the same scope or responsibility.
Pricing depends on factors such as discovery needs, project scope, complexity, data condition, data preparation, integrations, deployment requirements, hosting or infrastructure needs, model/API usage, consequential AI costs, support expectations, and the level of ongoing responsibility after launch.
The right next step is a project conversation. inAi first needs to understand the problem, the desired result, the systems or data involved, and whether the work should be a prototype, integration, fixed-scope build, or longer-term support relationship.
Business Products are inAi products built for repeatable company needs. Consumer Products are inAi products built for individuals. Products for Agents are tools and surfaces for AI agents to use. Open Source is selected public technology released for builders and the wider ecosystem.
AI Services are different. They are for cases where the solution has to be designed around a client’s own situation: their product, data, workflow, infrastructure, users, constraints, and goals.
If your need matches an inAi product, the right path may be the relevant product page. If your problem is specific to your organization, data, workflow, product, or infrastructure, AI Services may be the better route.
You do not need to know whether the answer is a chatbot, assistant, agentic workflow, data pipeline, product feature, prototype, or custom application before contacting inAi.
Start with the real situation: what is not working, what you want to improve, what data or systems are involved, who will use the result, and what would make the project useful. From there, inAi can help decide the right technical path and whether the work should become a prototype, integration, full build, or ongoing support relationship.
Custom AI work should be clear before it is built, private by default, scoped before development, evaluated against real use, delivered with an agreed operating path, and supported after launch where support is part of the project.
How AI Services work
Business Products are inAi products built for repeatable company needs.
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Trust in AI is not one rule applied everywhere. We build with clarity about what is public, private, experimental, controlled, open, and ready.
How we build
Start with the real situation: what is not working, what you want to improve, what data or systems are involved, who will use the result, and what would make the project useful.
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