AI for recruiting and CV analysis for independent recruiters

How AI-powered CV analysis works

Every CV that enters Hilancer (through an application, through your portal's drop-your-CV page, or through a bulk import) is read automatically with AI: contact details, experience, education, education level, skills and languages are structured into the candidate's profile, with nothing for you to type.

Candidates benefit too: on the public application form, their CV pre-fills the fields in seconds (never overwriting what they already typed by hand), which reduces drop-off right at the most sensitive moment of the process, when the candidate is one step from finishing.

The same thing happens if you import candidates in bulk from a folder of old CVs, or add them one by one with the Chrome extension from a LinkedIn profile: in all three cases, the profile fills itself in and the candidate is ready to assign to a position.

Per-position fit, not a generic score

For each opening, AI generates a candidate summary with a compatibility score against that specific position: strengths, points to validate in the interview and a short recommendation. The same candidate can score differently across two searches, which is exactly how a recruiter reasons: evaluating someone for a senior role isn't the same as for a junior one.

And it works both ways: with the opening loaded, Hilancer recommends candidates from your own base who match the description, even if they never applied to that specific search. It's the difference between searching from scratch and looking at what you already have first.

Semantic search across your base

AI search understands concepts, not just exact words: ask for “backend developer with fintech experience and small teams” and get results ranked by real affinity, even when no CV uses those literal words. It works both for building a shortlist fast and for checking whether you already have someone who fits before you go looking on LinkedIn.

It works in Spanish, English and Portuguese without you having to translate the search or the CV, and it runs across your whole base at once, no matter which search a candidate originally came from.

Detects when a file isn't a CV

On the public application form, it happens often: someone uploads an invoice, a ticket or a screenshot by mistake instead of their resume. The same AI reading that structures CVs detects when a file doesn't look like one and warns the candidate right away, before the application ends up with an empty profile that you only notice when you go to review it.

AI-generated job descriptions

When you set up a new opening, AI helps you draft the job description from the data you already entered (title, seniority, requirements): a starting point to edit, not a final text. It saves the blank-page moment of writing a description from scratch for every search.

A concrete case: from a folder of CVs to a shortlist

Say you start a search for a senior accounting analyst role and you have a folder of 40 CVs you've collected over past months, plus what's coming in through the opening's public form. You import the folder in bulk: AI reads each file, fills in name, contact, experience and education level, and immediately flags the two or three files that were actually something else (a certificate, a receipt).

With the opening published, every new application arrives with its compatibility summary already computed: who has more years in the field, who mentions the ERP the client asked for, who scores high but is missing the English level. Instead of opening CV after CV to build a first list, you sort by compatibility and spend full-read time only on the top eight or ten.

Before posting anywhere else, you try semantic search across your entire base: “senior accounting analyst with SAP and month-end close.” Two people from past searches show up who never applied to this specific opening but fit. You add them to the pipeline without duplicating their profile, with their full history (notes, past interviews) visible. The shortlist comes from there, not from starting over.

What it costs: AI credits

Each AI action (a search, a per-position candidate summary, a generated job description, a batch of recommendations) consumes one credit. The Lite plan includes 30 free credits per month, roughly a search or a summary a day; on the Pro plan, AI is unlimited, no credits counted. Automatic CV parsing on import does NOT consume credits: only what you actively request (search, summarize, generate, recommend) counts.

Frequently asked questions