Supporting the research team with multiple agentic workflows

Maes & Lunau automated how form submissions enter its internal system, saving its research team an estimated 15–20 hours a month.

Client

Industry

Professional Services

Maes & Lunau is an executive search firm. It finds and places C-level leaders at large companies, including interim and supervisory board roles. We worked with its research department, as part of a broader engagement to bring AI and automation into daily work.

15–20 hrs

est.

Saved per month across the research team

10 minutes → 0

manual work per application

2 hours → 20 minutes

to draft a job profile with the AI agent (user-reported)

Challenge

Data typed in twice, by hand

Candidates and other external parties submitted their details through forms on the Maes & Lunau website. Researchers and job intake processors then read each submission and typed the data into the internal system by hand. Every entry was checked before it was saved, because the team assumed this needed human verification. Almost every researcher was involved, several times a day. Together, this cost the team about an hour a day. Manual entry also introduced errors and inconsistencies in the data. And the process did not scale: every extra application or registration added directly to the workload.

Solution

Form submissions flow straight into the system

We automated the step between the website forms and the internal system. When someone submits a form, the data is captured right away. A set of rules decides how it is structured and where it belongs. The data is then written into the system, without anyone retyping it.

Form capture: picks up each submission from the website the moment it comes in.
Rule set: maps every field to the right place in the internal system.
System integration: writes the data directly into the internal system through its API.

Human-in-the-loop

Approach

From bottleneck to background process

We spotted the bottleneck early, tested the options, then built the automation together with the team.

Identify During a wider AI integration engagement, we flagged manual data entry as one of the first bottlenecks worth solving.
Assess We explored the technical options and estimated the impact before building anything.
Build We built the automation in Power Automate, connecting the website forms to the internal system's API.
Involve the team Researchers took part during implementation. This built confidence that automated input could match the accuracy of manual checks.

Built by

Mart KempenaarAI Engineer

Impact

Hours back for client work

Incoming form data now lands in the internal system automatically. Researchers and job intake processors no longer retype or check each submission. That saves the team an estimated 15 to 20 hours a month. Rule-based input also makes the data more consistent and less error-prone. The process also scales: if applications grow, the automation absorbs the extra volume without extra staff time. The automation was one part of a broader shift towards working with AI. Another example is an AI agent that drafts job profiles. One user finished a profile in about 20 minutes, instead of the two hours they had planned.

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