Choose the right candidate, and know why they're the right one.

When you're looking for someone for a project, hice puts what you're asking for on one side of the scale and the evidence it finds on the candidate on the other: career, interviews, CV. It shows you what fits, what's missing and what still needs checking. The score comes last, and you can redo the math yourself.

Matching is in every plan, at no extra cost for AI.

Criteria first, then evidence. The score comes last.

  1. Your request goes on the first pan

    Java, London, available from January, fluent German. Until there's evidence, the scale tips that way.

  2. The career comes in

    Luke Foster has worked in Java for six years, the last two as technical lead. What counts is when and for how long, not just that the word Java appears in the CV.

  3. The interview comes in

    From the notes of the person who met him: London works for him, and he can start on January 7.

  4. One doubt remains, and you can see it

    Only the CV says his German is fluent: nobody has heard him speak it yet. The scale almost balances, and German stays on the list to check at the interview.

First, it reads the request

The first thing hice looks at is what you're asking for. The job title leads, the description refines it. Each requirement gets one of three weights, and it doesn't change weight for convenience.

If a requirement is ambiguous, like “senior” without saying how many years, hice doesn't decide at random: it keeps it as a stated threshold and lets the evidence speak. If you want to clarify it, just write it in the chat.

Must-have

Required. If it's missing, it's missing: no high score makes up for it.

Preference

Welcome. It weighs on the result, but on its own it rules nobody out.

Inference

It isn't written down, but the context makes it clear: “migration” means knowing both the old system and the new one. It stays marked as an inference and never becomes a must-have.

Then, it reads the profile

What counts isn't how many right words are in the CV. It's what they prove. Here's what it looks at, on an example CV.

Career before job title
It judges the level of experience from how the roles grew, how big the projects were and how much autonomy the descriptions show. A “junior” title with twenty years of consistent experience isn't averaged out: it's flagged.
When, and for how long
A technology used eight years ago for two months isn't worth the same as that technology used for four years up to last year.
In what context
React at a video game studio, at an online store or only in a list of keywords are three different things. The evidence counts for the context it comes from.
Long lists don't convince
Lots of skills, few years and no detail on projects are a negative sign, not a neutral one.
A wish isn't experience
“I'd like to work with…”, courses with no projects and job ad requirements copied into the CV don't prove a skill.

When the sources disagree

Seven sources speak about a candidate, and they don't all weigh the same. The order is set in advance, not case by case: the longer the bar, the more the source weighs.

  1. 1The structured interview assessment (the scorecard)
  2. 2Notes from the person who ran the interview
  3. 3The career you can verify: dates, roles, companies
  4. 4Certifications
  5. 5The text of the CV
  6. 6What the candidate states in screening forms
  7. 7The AI's inferences, which never decide on their own

A difference between sources isn't averaged: it's declared

If the interviewer writes “basic German” and the CV says “fluent”, the criterion follows the higher-ranked, more recent source. And the report shows you the conflict, with both statements.

The report: every result with its evidence

Not just a percentage. Every criterion has a status, every status cites the evidence behind it, and the score is a sum you can redo by hand.

Contradicted means conflict

Two sources say opposite things. Nobody picks one in silence: you see who says what.

“To verify” is an honest result

Better a stated doubt than an invented certainty. Doubts aren't rounded off to make the number work.

Two candidates side by side

It explains what you gain and what you lose by choosing one or the other. It doesn't change the ranking: it helps you understand it.

You decide the priorities

Put each criterion in a priority tier and mark the mandatory ones. Two criteria in the same tier count the same. Change the priorities, and the shortlist is recalculated.

You can look at the same request from different angles, ask for five more profiles, dig deeper into a candidate and dismiss a suggestion you don't need: once dismissed, it doesn't come back.

When the answer is no, it tells you

If no candidate covers a criterion, the report says so and shows you what would widen the shortlist. If the analysis isn't finished, because some profiles haven't been assessed yet, it says so instead of passing it off as complete.

A weak shortlist stated clearly is worth more than enthusiasm with no basis.

On your next search, look at the evidence before the score.

Matching is in every plan, at no extra cost for AI. If your candidates are in spreadsheets or another ATS (the software for managing hiring) today, on the Business plan you bring them in with bulk migration.

  • Free for up to 10 people
  • No credit card required
  • Data migration with Business