AI that does half of your recruiter's job

    Drag a CV, AI creates the profile. Open an RFP, AI proposes candidates. Recruiter stays focused on what matters: the relationship.

    Consulting recruiting is high-volume, low-automation

    Consulting firm recruiters process hundreds of CVs weekly, must keep an updated talent pool, match dozens of open RFPs. Without AI, it's unsustainable.

    • Huge time on data entry of incoming CVs
    • Talent pool aging fast (obsolete skills)
    • Candidate-project matching depends on recruiter memory
    • Slow outreach, good candidates going to competition
    • Manual recruiting reporting for leadership

    AI applied to consulting recruiting

    hice.ai automates repetitive work: parsing, normalization, matching, screening, suggestions. Recruiter uses freed time for the relationship.

    • "Upload these 20 CVs into the database"
    • "Find me 3 Senior React for the Vodafone project"
    • "Suggest outreach message for Anna"
    • "Which candidates haven't been contacted in the last 6 months?"
    Try it in chat

    Examples you can ask:

    Static demo of hice's AI chat.

    What you get

    Bulk CV parsing

    Upload dozens of CVs, AI structures them all in minutes.

    Skill normalization

    Equivalences, variants, levels normalized automatically.

    AI match with score

    For every RFP, candidates ranked by match with motivation.

    Screening question generator

    AI proposes screening questions adapted to the profile.

    AI outreach

    Personalized LinkedIn/email message suggestions.

    Talent pool refresh

    Alerts on 'old' candidates to recontact before competition.

    What to automate, what to assist and what to leave to people

    The useful boundary does not separate easy tasks from hard ones; it separates verifiable tasks from decisions about people. Posting a role, acknowledging an application, offering interview slots or spotting that someone is already in the database are actions whose result can be checked at a glance, and automation there returns time at no cost to quality.

    Extracting data from a CV, normalizing job titles or summarizing evidence from three interviews is a different category: AI accelerates a great deal, but the output needs a visible source. And rejecting a person, ordering the final shortlist or communicating a decline should not be automated at all.

    • Full automation for repetitive, verifiable tasks
    • Assistance with a visible source when preparing information
    • Recorded human decision on anything affecting a person
    • Ability to depart from the recommendation and log the reason

    How to stop a screening model from amplifying bias

    A system that learns from history reproduces the patterns in that history, including the ones the firm would not want to repeat. Review therefore cannot be a one-off check at go-live: it has to be a routine with a date in someone's calendar.

    The reasonable minimum is to compare progression and exclusion rates between groups every quarter, review which fields actually drive the recommendations, and analyse when recruiters override the system and why. A difference does not by itself prove discrimination, but it does require investigation before the use is widened.

    • Job-related criteria and excluded data set out in writing
    • Periodic review of progression rates by group
    • Analysis of human corrections as a source of improvement
    • Ability to suspend a feature if results degrade

    Measuring whether the assistance actually works

    A hundred percent acceptance rate for suggestions is a warning sign rather than a success: it means nobody is reviewing. The useful indicators are different and all obtainable from the system itself.

    The four that best reflect real improvement are time to a defensible shortlist, profiles considered versus profiles that genuinely fitted, the share of corrections that end up fixing a data point at source, and retention of hires at six months. That last one is the least watched and says the most about process quality.

    • Time to the first shortlist accepted by the client
    • Manual corrections and their cause, classified
    • Relevant profiles retrieved against total proposed
    • Six-month retention as a quality control

    IT consulting recruiting team, 4 recruiters, 200 CVs/week

    Before: 4 recruiters × 8 hrs/day = 160 hrs/week, of which 60 on data entry and manual searches. With hice.ai: data entry near zero, search via chat. 50+ hrs/week freed for relationship = more hires with same team.

    hice.ai vs ATS without AI

    Featurehice.aiClassic ATS
    Automatic CV parsingAdvanced AIBasic or none
    Match with explanationYesFilters
    AI screeningYesManual
    AI outreachYesManual
    Multilingual nativeYesOften English only
    Integrated with projectsYesNo

    FAQ

    Does AI replace the recruiter?+

    No. Automates repetitive work (parsing, search, basic screening). Recruiter stays in charge of decisions and relationship.

    How accurate is CV parsing?+

    Typically >95% on standard fields. On complex fields it's interactive: AI proposes, recruiter confirms.

    Can I train AI on my criteria?+

    Yes. System learns from your match choices and improves over time.

    AI writes outreach for me?+

    Suggests personalized drafts per candidate. You adapt and send: human tone guaranteed.

    Works for traditional recruiting (not staffing)?+

    Yes. Standard candidate pipeline, integrable with public job postings.

    Privacy?+

    GDPR-compliant. Data hosted in the EU and encrypted, processed with confidentiality guarantees and a full audit trail.

    LinkedIn integration?+

    Import via PDF parsing and dedicated Chrome extension. Direct LinkedIn API on roadmap.

    What ROI to expect?+

    Typical cases: 30-50% time-to-hire reduction, 2x staffing speed on RFPs.

    Let AI handle the CVs, you keep the people

    30-minute demo or instant free access.

    Explore more solutions

    All solutions →