AI workflow automation to free your teams from repetitive tasks

These three approaches complement each other, and a single flow can combine them.

At Kreante, if a rule and a trigger are enough, we don’t add an AI model, because that is often what makes a project expensive and fragile.

Those that meet three criteria:

• a clear flow, from the initial request to the result
• a cost that can be measured today: time spent, errors, unbilled revenue
• frequent repetition, since the gain multiplies with each run

By function, French companies direct AI first toward IT security and administration. Here are the uses reported in 2025, as a share of the workforce of the companies concerned^[1]:

Certain signals show that a process is ready to be automated: the same information entered in several tools, or a document rebuilt by hand every week.

A few examples of automation projects we have carried out:

• CartelOne, for an American automotive photography company operating in 10 markets: automatic import of payroll data from its existing software and ranking of photographers by their effective hourly rate
• TheFlow, a ticketing platform for event organizers: registrations, waiting lists, ticket checks and invoicing automated
• DAVCO AI, for real estate investors in Miami: more than 8,000 listings analyzed every day and a summary sent every morning with the properties exceeding a 15% estimated margin

The French market has many automation agencies. Most were built around tools such as n8n or Make, then added AI agents to their offering.

The tools to use depend on the tasks to automate: connecting applications, processing information, coordinating several AI steps or building a custom solution.

• Connecting your applications. Automation platforms such as n8n, Make and Zapier link applications together. They let you build flows in which a trigger launches one or more actions.
• Automating tasks in software without an API. RPA tools, such as UiPath and Automation Anywhere, reproduce a user’s actions directly on the software’s interface. They are useful when applications don’t offer an API.
• Reading, sorting, extracting and writing. The language models offered by OpenAI, Anthropic with Claude, and Mistral handle information processing: reading documents, sorting content, extracting data or writing text.
• Coordinating several AI steps. Agent orchestration, with a tool such as LangGraph, lets you chain several AI processing steps while keeping control over how they run.
• Building a custom solution. When platforms are no longer enough, custom development lets you create software you own. At Kreante, this code is produced with agentic development tools such as Cursor, Codex or Claude Code, then reviewed and approved by senior developers.

“We choose technologies according to the product’s lifespan. On DAVCO AI, the daily collection of listings, the renovation estimates by OpenAI and the orchestration by LangGraph rely on Supabase and FastAPI.”

At Kreante, an automation project starts from €3,900. The budget is approved in advance, on a scope defined in writing: if you add a feature along the way, it is priced separately, without pushing back the schedule.

Four factors change the budget:

You also need to anticipate recurring costs: AI model calls, hosting and maintenance. Cost remains a real obstacle, since 33% of companies that already use AI say they are held back by costs that are too high^[1]. That is one more reason to use a model only at the steps that need it.

The schedule depends on the number of software tools to connect and the complexity of the process.

The first version comes early so you can test it on your own data and adjust course before committing to the rest of the project.

Yes, in most cases. It is even a common concern: 38% of companies that don’t use AI yet cite incompatibility with their existing equipment and software, and nearly one in two among those with more than 250 employees^[1]. Our approach rests on three principles:

• connect what works: your CRM, your billing or your business tool stay in place if they meet the need
• make the connections the heart of the project, because they require the most work
• only replace what costs more than it brings in, typically a per-user subscription for a tool you only use part of

After a lack of perceived usefulness, the first obstacle cited is skills, ahead of budget: 54% of companies that don’t use AI mention a lack of expertise, and 73% among those with more than 250 employees. Even among companies that use it, 53% say they are held back by this lack^[1].

Working with an agency doesn’t stop you from keeping control of the project. At Kreante, the automations are handed over to you on delivery, and your teams can be trained to monitor them. If your main obstacle is adoption rather than technology, AI training and support is the right starting point.

All those that repeat tasks across several tools, but adoption depends heavily on size and sector. In 2025, 15% of companies with 10 to 49 employees use AI, 31% of those with 50 to 249 employees and 58% of those with 250 or more^[1].

Source: Insee^[1]. Scope: companies with 10 or more employees, excluding the agricultural, financial and insurance sectors.

We mainly support very small businesses and SMBs with 15 to 200 employees, particularly in services, logistics, temporary staffing or facility management. These are often sectors that are still poorly equipped, where the potential is greatest.

By managing the risk from the moment the flow is designed, because an AI model doesn’t always give the same answer. Three safeguards cover most situations:

• not using a model when a rule is enough, because an “if... then” condition doesn’t make mistakes
• planning human approval where a mistake would be costly: the agent prepares, a person approves. On REMI AI, the clinic’s team checks each interpretation before it is sent to the patient
• relying on reliable data: 29% of companies that don’t use AI cite data-related difficulties, and 49% above 250 employees^[1]. That is why we check data quality before any automation

It is one of the first questions to ask, because an automation accesses a large part of your information. The danger often comes from isolated initiatives: an employee who connects their tools with a personal account, copies customer data into a consumer assistant or has AI generate a small internal tool, put online without any checks. These improvised solutions let through visible access keys, files open to everyone or rights that are far too broad.

The risk grows as cyberattacks multiply, especially since attackers also rely on AI to spot these flaws faster.

At Kreante, security is part of the specifications of every automation: limited access rights, protected connection keys, personal data processed in compliance with the GDPR and testing before anything goes live. The code produced by our AI agents is systematically reviewed and approved by a senior developer.

By comparing, after go-live, the results obtained with the situation measured at the start. Without this baseline, no gain can be demonstrated.

French companies that use AI aim first to reduce costs (73%) and improve the quality of their processes (64%)^[1]. We track three areas:

• costs: processing time per case, licenses eliminated
• quality: errors, re-entries, lead times
• revenue: revenue recovered, such as services delivered but never billed

Two regulatory frameworks apply, and they still hold back many projects: 43% of companies that use AI are concerned about data protection, and 42% mention a lack of legal clarity^[1].

• The GDPR applies as soon as an automation processes personal data. It requires a legal basis and, when the processing poses a high risk to people, as in human resources or customer scoring, an impact assessment^[2].
• The AI Act classifies AI systems by risk level^[3]. The obligations for high-risk systems, which concern in particular recruitment, credit, education and health, will apply from December 2, 2027, and those for systems built into already regulated products from August 2, 2028^[4].

Preparing quotes, scheduling interventions or summarizing reports generally don’t fall under high risk. At Kreante, we identify sensitive cases from the project scoping stage, without replacing a lawyer’s advice.

You do, entirely. In practice:

• the automations, and the code when there is some, are handed over to you on delivery
• no license or per-user fee is payable to Kreante
• reversibility is complete: if you change provider, you keep everything, and another team can take over the project

That is the main difference with a software subscription, whose cost grows with the number of users and only stops when you leave the tool.

A post-launch warranty is included in every project. Maintenance is then offered as blocks of hours, because the first weeks of use often reveal new needs: an unforeseen case, a higher volume, an additional piece of software to connect.

It is also the time for the method’s final step: comparing the results with the starting point, to check that the automation delivers on its promises.