Question 1

How much does Custom AI Workflow Integration cost?

Accepted Answer

Projects start at $10,000 and can range up to $35,000. Pricing depends on system access, integrations, compliance needs, and workflow scope.

Question 2

What does custom AI workflow integration include?

Accepted Answer

The work covers workflow, data-source, and decision-point discovery; a secure agent or RAG architecture; agreed integrations; failure handling; milestone demos; acceptance criteria; and handover.

Question 3

Can you integrate AI with our CRM, ERP, database, and APIs?

Accepted Answer

Yes, when the named systems are part of the agreed workflow and access can be provided safely. The project defines the integrations, permissions, logging, and failure behavior before implementation starts.

Question 4

Can you build an AI agent or RAG workflow for internal operations?

Accepted Answer

Yes. The work is designed around one bounded operational workflow with a named owner, real systems of record, explicit permissions, and a handover your team can operate.

Question 5

What makes this different from a chatbot?

Accepted Answer

The work connects one bounded workflow to your real systems with explicit permissions, failure handling, and a team handover. A conversational interface may be part of it, but it is not the whole deliverable.

Question 6

Can you automate manual back-office work with AI?

Accepted Answer

Yes, if there is a clear workflow, source data, decision boundary, and accountable owner. We first define where AI can assist and where the system must stop, confirm, or hand work to a person.

Question 7

How do you keep AI actions secure and approved?

Accepted Answer

The project defines explicit permissions, action boundaries, logging, confirmation points, and failure behavior for the agreed workflow. We do not rely on an AI model to infer what it is allowed to do.

Question 8

How do you handle data privacy and tenant isolation in an AI integration?

Accepted Answer

We trace the data and access boundaries of the agreed workflow, including the systems of record, permissions, tenant separation where relevant, and the evidence needed to operate the integration safely.

Question 9

What happens when an API, model, or downstream system fails?

Accepted Answer

The build defines what the workflow should do when a dependency fails, such as retrying within limits, falling back, stopping safely, or handing the work to a person. Those choices are part of the acceptance criteria.

Question 10

Can you migrate an AI prototype into a production workflow?

Accepted Answer

Yes, when the prototype can be narrowed to one owned workflow. We map the required systems, data, permissions, failure handling, and handover before treating it as production work.

Question 11

Do you build standalone chatbots?

Accepted Answer

The offer is for AI that works inside an owned business workflow. A standalone chatbot is only a fit when it has a clear operational boundary, system integrations, and a team responsible for the outcome.

Question 12

What is the first milestone in a custom AI integration project?

Accepted Answer

The first milestone is a shared definition of the workflow, source data, decision points, systems, access, risks, and acceptance evidence. That makes the build boundary visible before implementation expands.

Question 13

Will our team own the AI workflow after handover?

Accepted Answer

Yes. Milestone demos, acceptance criteria, implementation notes, and handover are part of the project so the named team can own the workflow after delivery.

Question 14

Can Topiax work with our existing engineering and operations teams?

Accepted Answer

Yes. Roles, access, acceptance criteria, implementation responsibilities, and handover are agreed before delivery starts.

Question 15

Who is a good fit for Custom AI Workflow Integration?

Accepted Answer

Operations and product teams with a high-value workflow, the systems and data needed to support it, and a named owner who can make decisions about the outcome.

Put AI inside the workflow your team already uses.

AI inside the business, not beside it.

A production-minded build with a clean handover.

Before you start.