Flatchr vs Keka

Flatchr is shipping across four fronts. The newest, Suggest IA, reads the customer's existing CV library and returns the candidates best matched to an open offer, ranked by a correspondence index, with the back catalogue syncing progressively while new applications are indexed on arrival. Around it sit Flatchr Skills for candidate assessment, an administration space for toggling AI features and steering job-offer generation, self-serve seat purchase for monthly plans, and a recruitment-authorization workflow that external approvers can complete by email.

Flatchr has been closing loops that used to end in someone's inbox — approvals chase themselves, seats are bought without a call — and separately widening what the ATS does. Suggest IA is the widest move yet: the product now acts before anyone applies, which makes stored CVs an asset rather than an archive. Read with Fit's summaries and Skills' assessment scores, ranking is becoming Flatchr's organising idea, applied first to applicants, then to evaluation, now to the database itself. Note that the feed's titles arrive with broken emoji encoding and the content is translated from French.

Expect Suggest IA's correspondence index to appear as a filter or sort in the dashboard currently in beta, and expect it to land under the AI configuration page's toggles like Fit and automatic tagging before it becomes default behaviour.