Every one of them ends up as a system your firm owns, on infrastructure you already pay for. No per-seat rent, no platform to be moved off later.
A recruitment/HR system shaped to how you actually work, not a rented SaaS you bend yourself around. Built on the Microsoft 365 or Google Workspace your firm already runs, or your own stack.
From the build on a staffing firm's own Microsoft 365, the one in the case study. Nothing here is a roadmap item.
See pricing ranges · usually one fixed monthly number See it work
Less manual work: pull candidate data straight from CVs, passports and IDs; draft summaries, job ads and emails in seconds.
Where the week actually goes. Recruiters report spending close to two full days a week on search and screening alone. That is the part we automate first, because it is the biggest block of time in the week and the least enjoyable part of anyone's job. You type what you are looking for in a sentence, the system searches the archive, and a person decides. It will also refuse: ask it to screen on age or sex and it says no and tells you why, because that is a rule in the product rather than a setting somebody can switch off in a hurry.
Always in control: AI does the busywork. A person makes every decision. No black boxes, nothing sent automatically. On the last build we handed over, the automatic reminders shipped switched off and stayed off until the firm had read and approved every text they send. Where the system ranks candidates against a live vacancy, it writes a line saying why it ranked each one that way, so a recruiter can read the reason and disagree with it.
The test we hold a build to: by the time someone is placed, how many times has their name been typed? In most agencies the honest answer is more than once, because the ATS, the offer letter, the timesheet and the invoice each want it again. On the last build we handed over, fourteen documents fill themselves from what is already in the system, and the mail history is read where it already lives instead of being copied into a second database. A system that still makes you retype is not saving you time, it is a filing cabinet with a login.
Hand-built, fast, and on-brand, not a template. Career site, employer pages and intake funnels designed to actually convert.
The machine behind the marketing: job-posting structured data, Google for Jobs, employer landing pages and candidate funnels, all wired into your CRM.
The old CVs you cannot search become structured, searchable records. Counted honestly, a 20,000-CV archive hides real placement value, on the calculator’s own cautious defaults around €80–200k a year, and indexing it into searchable records takes 3 to 5 days, from €3k standalone.
Why this comes before the AI, not after it. Every agency we speak to wants the thing that reads its own history and surfaces the right person on Tuesday morning. That only works on records a machine can actually read. A folder of 20,000 files, many of them scans, is invisible to any model you point at it. It will happily summarise the one CV you hand it and do nothing at all with the other 19,999. Indexing is the dull step that makes every clever step afterwards possible, and it is the one nobody sells you because it does not demo well.
Twenty-plus modules across four packs: Hidden Value, Compliance, Fee Recovery and the rest. Pick what your firm actually needs, skip the rest.
The one vertical already shaped end to end: technician and workshop pipelines, the trade specifics, the paperwork that comes with them.
Vorentis in production, a Norwegian staffing firm's 25,000 scattered records consolidated into one system, and ongoing systems for a Swiss home-care service.
One rule: if it won't make you money or save it, we don't build it.