AI integration agency to connect artificial intelligence to your existing tools

An integration agency is useful when AI has to communicate with several of your software tools and nobody in-house has the time or the expertise to run the project.

Through the same mechanisms as any software integration, chosen according to what your tools allow:

• APIs (application programming interfaces), the most reliable route: the AI reads a customer record, a quote or an order, then writes its result in the right place;
• webhooks: your software notifies the AI as soon as an event occurs (an email received, a ticket created, an order confirmed), which triggers the processing without waiting;
• connectors offered by automation platforms or by the vendor itself, useful for simple flows;
• protocols designed for AI, such as the Model Context Protocol (MCP), an open standard for exposing your tools to a model in a controlled way, by defining precisely what it can see and do.

Most of the work lies in the rules surrounding these connections: what data the AI is allowed to see, what it can change on its own, and what has to go through human approval.

This is common with older or highly specialised software, and it is not a blocker. Several solutions exist: read access to the software’s database, scheduled exports that the AI processes automatically, or software robots that reproduce a user’s actions on the interface.

This last option works, but it can break with every screen change. When a closed tool is holding back an entire process, it is worth asking whether to partly replace it with a custom module. At Kreante, we raise this question at the scoping stage, with figures to back it up, rather than after months of workarounds.

Yes, and it is common. In France, 59% of companies that use AI say they use at least two different technologies, and 36% at least three. This share reaches 68% among companies with 250 or more employees^[1].

A well-designed integration assembles these building blocks around a single flow. On REMI AI, we combined a graph database to link test results and recommendations, a language model to write the nutrition plan, an emailing service to send it, and an approval step by the clinic’s team before each sending. Each building block is chosen for what it does best, and it is the integration that makes them work together.

By treating it as a new exposed component of your information system, with its own risks: data leaks, hijacking through malicious instructions hidden in a document or an email, unwanted actions in your tools.

ANSSI (France’s national cybersecurity agency) has published a guide of 35 recommendations covering the whole lifecycle of a generative AI system, from training to production^[2]. For a business integration, several principles are decisive:

• isolate the AI system and limit its access to what is strictly necessary;
• limit automatic actions triggered from uncontrolled content, such as emails or documents received from outside;
• log exchanges with the models so you can analyse what happened;
• plan a degraded mode, so your teams can keep working if the AI service is unavailable.

These measures are decided when designing the architecture, because adding them after going live costs much more. They also require an experienced developer to review the code of the connections: generated too quickly and without control, they easily let visible access keys or overly broad rights slip through.

Most companies go through the cloud: 73% of companies that use it buy cloud computing services, compared with 33% of those that do not^[1]. It is the fastest way to access the best-performing models.

The choice depends on the sensitivity of the data passing through. For everyday data, a cloud service backed by contractual guarantees may be enough, provided you check that your data is not used to train the provider’s models.

For sensitive data, ANSSI recommends hosting the AI system in trusted environments suited to the security needs, and favouring SecNumCloud-qualified hosting when deploying in a public cloud^[2]. The same company can combine both approaches depending on the flow.

It is a risk that is managed from the architecture stage. A robust integration does not tie your processes to a specific model: it isolates the model call behind a dedicated technical layer, so it can be changed without rewriting the rest.

Two precautions complete this setup: a set of test cases to check that a new model gives at least equivalent results before switching, and a degraded mode if the service becomes unavailable. At Kreante, we choose the model task by task, which avoids depending on a single provider for all your uses.

A first flow connected to your real tools is delivered within a few weeks, then the production version in one to three months depending on the scope.

In integration, the timeline often depends less on development than on factors specific to your environment: access to test environments, the quality of the documentation of the software to connect and the availability of your IT team to approve access. Anticipating them from the scoping stage can save several weeks.

At Kreante, an integration starts from €3,900. A full integration, with several systems connected and orchestrated agents, is priced on a custom quote. In both cases, the budget is approved before we start, on a scope agreed together in writing.

After going live, three recurring items need to be planned for:

• calls to AI models, generally billed on usage by the provider;
• hosting;
• and maintenance of the connections, which evolve with the versions of your software.

These costs depend heavily on the volumes processed and are worth estimating before launching the project.

A post-launch warranty is included, then we offer maintenance as blocks of hours. It is often during the first month that the real needs emerge: an unexpected data format, a higher volume or a new source to connect, for example.

You can also take the integration back in-house. The code and connectors belong to you, and they rely on standard technologies that other teams know how to maintain.

The market brings together digital agencies that have added AI to their offer, specialised engineering firms and integrators that rely on their own platform.