Live, today
Recruitment / compliance check · live
14 runs · real numbers, running live
Form in, substantiated status report out
What we built.
A freelancer compliance check, built for a recruitment agency. The freelancer fills in the check and automatically receives a risk score plus PDF report by email, which they can use to substantiate towards the Dutch tax authority that they meet the requirements. A substantiation, not a legally binding judgment.
Outcome.
Running live: 14 successful runs up to and including 2 July 2026. Real run numbers, not an estimate.
14 runs live
through 2 July 2026
What it makes possible.
The same pattern, form → score → report, can be deployed for any intake-heavy process: handling more volume without the next hire.
Stack: n8n + document AI + CRM (live)
Recruitment
Recruitment · running in production
From vacancy to screened shortlist, in minutes
Current way of working.
At an executive search agency, every assignment started with manual sourcing: endlessly scrolling through LinkedIn, assessing profiles one by one.
Problem.
Hours per vacancy: time lost to searching instead of speaking to candidates and advising clients.
What we built.
A sourcing agent turns one vacancy form into a search query, retrieves up to 50 profiles and scores them; only those who clear the threshold make it into the CRM.
Outcome.
From manual sourcing to a shortlist in minutes. Running in production at the agency.
indicative: ~2.5 hours → minutes
running in production
What it makes possible.
More vacancies at once, with the same team: the recruiter starts from a pre-selected list. More placements per recruiter, not more recruiters.
Stack: n8n + CRM + sourcing
Recruitment · built
From application to a phone call within 30 minutes
Current way of working.
Applications come in, the recruiter reads them, compares them with the vacancy requirements and has to follow up with the best ones quickly before they drop off.
Problem.
Manual screening takes time, and top candidates expect speed. Anyone who only hears back after two days is often already in talks elsewhere.
What we built.
A chain that scores every application against the requirements (A-E in the CRM), immediately sends the best ones a text + email and alerts the recruiter: call within 30 minutes.
Outcome.
Manual CV assessment per applicant disappears; top candidates are contacted in minutes, not days.
indicative: ~10-15 min per CV eliminated
top candidates in minutes, not days
What it makes possible.
Processing more applications and reaching the best ones faster, with the same team. No more top candidates lost to slow follow-up.
Stack: n8n + ATS + SMS/email
Recruitment · built + tested
Warm replies that no longer go cold
Current way of working.
Replies to LinkedIn and outreach land in the inbox. The recruiter has to spot the warm ones themselves, remember them and follow up.
Problem.
No system underneath. A strong reply left sitting on a busy day goes cold, and that is a missed placement.
What we built.
A layer that captures every reply, scores the intent with AI (warm / lukewarm / no), adds it as an opportunity in the CRM, alerts on warm and follows up itself.
Outcome.
In testing the system scored and routed correctly: a test reply received a score of 84 = warm and landed at the top. No warm reply slips through anymore.
score 84 = warm (test)
indicative ~1.5 hours/week less triage
What it makes possible.
Recruiters focus on the candidates who are already there. More conversations from the same stream of replies, without an extra recruiter.
Stack: n8n + CRM + outreach tools
Custom software & dashboards
Recruitment · designed
Your own database, searchable by meaning
Current way of working.
An executive search agency in the cultural sector opened LinkedIn for every vacancy. Its own database with thousands of paid profiles remained untouched.
Problem.
The ATS searches by keywords. Someone with ten years of museum experience does not surface for “cultural policy advisor”, even though that experience is right there.
What we designed.
A hosted search app on their own database: paste the vacancy text and get a top 10 by meaning, not by keyword, each with an explanation.
Outcome.
From hours of LinkedIn sourcing to a shortlist in ±1 minute. Candidates you never found by keyword now do surface.
indicative: hours → ±1 min
top 10 by meaning
What it makes possible.
More placements from the database you already paid for. One extra placement from your own file already pays for the tool.
Stack: own data + semantic search (designed)
B2B · custom software
Custom work, built for a paying client
An intel dashboard that reads public market data
Current way of working.
A B2B company manually researched who the biggest players in a market were, document by document.
Problem.
Hundreds of hours of research, and by the time the overview was ready, it was already outdated.
What we built.
A secured dashboard that automatically reads public market data and summarises who the biggest players are, with its own interface, login and database.
Outcome.
Hundreds of hours of manual research, reduced to the push of a button.
indicative: hundreds of hours → 1 button
public market data
What it makes possible.
Think of internal dashboards, intel tools and semantic search apps on your own data. Properly secured, data for you only.
Stack: Next.js + Supabase + n8n + AI
How we keep it honest
Why there are no names and hard numbers here (yet)
We do not put client names or logos on display that we are not allowed to use, and we do not make up results. What you see above are systems we actually built, anonymised on request.
Numbers marked “indicative” or “indicative estimate” are substantiated estimates, not measured client results. The freelancer status check is the exception: it runs with real run numbers.
As soon as a client gives permission, a case with a name, quote and hard number will replace one of these scenarios.
Anonymised on request: no client names or logos. Numbers marked “indicative” are substantiated estimates, not measured client results, except for the freelancer status check, which runs with real run numbers.
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