An AI ATS for the team hiring into its own company

Every recruiter is carrying more reqs than they can actually work

AI agents take the execution, not the judgement

Sourcing, outreach, follow-ups and scheduling run on their own against every open req. What stays with the recruiter is who to go after, who is actually right, and when to move on an offer — the three decisions that determine the hire.

Applicants pile up faster than anyone can review them

Every applicant scored against the req, with the reasoning shown

AI matching ranks the whole pool by role fit rather than keyword overlap, and every score traces back to the weighted criteria that produced it. You review a ranked shortlist with reasons attached instead of opening a hundred profiles to find six.

Good people applied last year and nobody ever looks at them again

Your existing applicant database becomes searchable by intent

Semantic search reads past applicants against a new req the same way it reads new ones. The candidate who was almost right for a role in March surfaces automatically when the role that actually fits them opens.

Hiring managers go quiet and the pipeline stalls waiting on them

The follow-up happens without anyone remembering to send it

Agents chase interview feedback, send the reminders, and book the next stage against both calendars. Decisions made in Slack are captured onto the right req automatically, with the source message linked, so nothing is lost in a thread.

You have dashboards but cannot say why a req is stuck

Analytics that open up to the activity underneath

Time to submit, stage conversion, at-risk reqs and recruiter load — each number opens to the candidates and actions that produced it, and the AI attaches the reason a req has been flagged rather than leaving you to work it out.

What is the best ATS for an in-house recruiting team?

For in-house teams the deciding factor is usually how much of the week goes to admin rather than to candidates. Nervyn AI is built around that: AI agents run sourcing, outreach, follow-ups and interview coordination against every req, so recruiters keep the judgement calls and hand over the coordination. It suits teams from two recruiters up to in-house functions covering a whole company.

Is Nervyn AI suitable for startups and smaller companies?

Yes. Plans start at two recruiters and there are no setup or implementation fees. The AI agents matter most where the team is smaller than the workload, which is exactly the position a scaling company is in — a two or three person talent team covering every function at once.

Can Nervyn AI find good candidates in our old applicant database?

That is one of the main reasons in-house teams adopt it. Semantic matching reads past applicants against a new req the same way it reads fresh ones, so someone who was nearly right for a role months ago surfaces when a role that genuinely fits them opens. Most teams are sitting on a pool they have no practical way to search.

Does Nervyn AI replace Greenhouse, Lever, or Ashby?

It can replace them or run alongside them — your choice. Many teams import their database and run their existing ATS read-only through the transition. Candidates, reqs, and interview history come across during the first week of onboarding.

How do hiring managers interact with Nervyn AI?

Through their own portal, where they review shortlisted candidates for their reqs. They do not consume a seat, so you are not paying for occasional reviewers. Scheduling is negotiated against their calendar automatically, and any decision they make in Slack is captured onto the right req.

How long does it take to implement Nervyn AI for an in-house team?

Twenty days end to end, with no IT involvement. Onboarding and data migration are included in every plan rather than sold as an implementation project.