AI integration services: AI features in the software you already use

What AI integration covers

An AI feature is only useful if it works with the data your people already maintain every day. Integration creates that connection without making you switch software. At titanspear.ai, that includes:

• Interfaces: We connect your existing systems through their APIs.
• Access rights: The AI sees and changes only what its task requires.
• Logging: It stays traceable what the AI did and when.
• Approval: Where a result has consequences, a person checks it before it reaches your system.
• Operations: When your systems or rules change, we adjust the connection.

AI integration or process automation?

The line is blurry, but the question behind it is simple. With AI integration, your existing software is the center: it gains AI features and your team keeps working in familiar screens. With AI process automation, the workflow is the center: it runs from the incoming email to the booking, often across several systems. Many projects need both.

Which systems can AI be connected to?

What matters is not the product but access to the data: an interface (API) or another orderly route. Whether your systems offer one is part of the intro call. Typical system classes and tasks:

If the main goal is to make knowledge in documents findable, a RAG system is the more direct route. Whether integration, RAG or both fits is decided in the mapping step.

How an AI integration works

1. Mapping: Which task should the AI take over, with which data, in which system? We go through it with the people who work with it today.
2. Interfaces: We connect your existing systems through their APIs.
3. Rights and logging: Access rights, traceability and the points where a person approves are defined and documented.
4. Pilot on real data: One task goes live, at first with every result checked.
5. Operations: Once the pilot runs stably, more tasks follow. We monitor the connection and adjust it when systems or rules change.

This is the same path as the whole AI consulting engagement. For how the steps fit into a larger rollout, see the guide to an AI implementation roadmap.

Integration, implementation consulting and implementation services

The terms overlap. Integration means connecting AI to your systems. Implementation is wider: it covers the whole rollout, including use case selection, rules, training and operations. At titanspear.ai the consulting and the integration come from the same team, so the person who maps your process is the one who connects your systems.

Use the built-in AI feature, or integrate yourself?

Many software vendors ship AI features of their own. If one covers your task and the data stays where you want it, that is the shortest path. A custom integration pays off when the task touches several systems, when the built-in feature does not reflect your data or rules, or when you want control over what flows where. An in-house team pays off if you can keep capacity and know-how available for the long run. Part of the consulting is telling you where the built-in feature is enough.

Data protection in an integration

• Contract: We sign a data processing agreement under Art. 28 GDPR with you.
• Model training: For RAG and Corporate LLM, your data is never used to train third-party models. For your integration, we settle how data is handled before anything is connected.
• Traceability: Rights, logging and approvals are documented so your data protection and IT security leads can review them.

The company-wide frame is covered in the guide to AI governance. If you want to know first where the company stands, start with the AI readiness assessment.

How much do AI integration services cost?

The effort depends on four things: how many systems are involved, how good their interfaces are, which rights and approvals are needed, and whether operations and support are part of the work. titanspear.ai does not publish flat rates. You get a quote after the intro call, as a fixed price.

For orientation, one integration provider (smartexe.com, page opened October 11, 2026) publishes these typical budgets. They are not titanspear.ai prices.