Stop reading 1000 CVs.
Read the 30 you would have flagged anyway.
HireScout codifies your job description into a deterministic lens, reads every candidate through it, and shows you the resume passage behind every decision. Override anything you disagree with. Every shortlist defensible from the first screen call to the offer letter.
“Finance leader with 14 years across SaaS and fintech, including 6 as VP Finance at a Series C company.”
Every extracted value carries the sentence it came from. Click any number, see the source.
Not an AI hiring tool.
A tool that makes your judgment faster.
Hiring is judgment under uncertainty. The system that wraps that judgment should be deterministic where it can be, transparent everywhere else, and never decide on your behalf.
The lens is yours
Paste a job description, HireScout drafts your scoring categories, extraction schema, and hard filters from it. Every choice carries a one-line rationale citing the JD passage that drove it. You edit anything before locking. The system never decides what matters.
Every value cites a passage
Each extracted field comes with the resume excerpt that produced it, plus a per-field confidence indicator. Click any number, see the sentence it came from. No hallucinations, no “the AI said so” — every recommendation is auditable in two clicks.
Override anything
Disagree with an extraction? Correct it inline with a reason logged. A great candidate failed a hard filter you don't agree with? Bypass the filter with a defensible note. Manual corrections are preserved across re-extraction — your judgment never gets wiped out.
Defensible by default
Every decision — pass, fail, score, override, bypass — has a who / when / why on it. When a partner asks “why this candidate over that one?” you have the answer in the interface. When legal asks for a non-discrimination paper trail, the trail already exists.
Test your lens before you connect your ATS.
When a candidate writes a CV for a role, they read the JD, identify the criteria implicitly, and tailor their experience to the language. That is the same operation HireScout does in reverse: from the JD it derives the lens, then can generate synthetic candidate pools from that same lens with deliberate distribution.
Try it with three strong fits, four mixed, three weak — and every candidate's resume is real LLM-written prose with real source-quoted extractions. A controlled stress test of your hiring grammar.
- • A satisfy / violate map per hard filter
- • A diverse pre-selected name (60+ first / 40+ last pool)
- • A polished prose resume from the LLM
- • Real source quotes from re-extraction
Four steps. The first three are deterministic.
The expensive AI call happens once when you draft the lens. After that, every candidate flows through cheap structured extraction and deterministic filtering before any LLM scoring runs.
- 1
Codify the JD
HireScout drafts categories, extraction schema, and hard filters from your JD. You review and edit.
- 2
Extract with quotes
A cheap-tier LLM pulls structured fields from each CV. Every value carries the resume passage that supports it.
- 3
Filter deterministically
Numbers, booleans, closed taxonomies. Candidates fail or pass on math, not vibes. Bypass with a logged reason.
- 4
Score the survivors
Only the candidates who clear hard filters reach the expensive scoring pass. Source-quoted, weighted, ranked.
Most agentic pipelines do everything with the LLM at every step. HireScout splits cleanly: semantic understanding happens once at extraction, deterministic gates run on the structured output, and the expensive scoring only touches survivors. A pipeline of 200 CVs costs roughly a dollar. Decisions stay defensible because the gates are math.
Same code path. Any role.
From CFO to plumber. Each scenario was generated from a real JD, with 10 candidates distributed across strong / mid / weak fit tiers. Same lens system, totally different feature spaces.
What a recruiter sees, in two clicks.
years_experience: 14 "Finance leader with 14 years across SaaS and fintech…"
✓ years_experience >= 12 ✗ has_active_ca_license → JD requires CA license; candidate is licensed in MA only
Override years_experience: 11 → 14 Reason: Includes IB and audit years from 2007. Candidate moved to PASSED pile.
The shortlist you walk into the room with
should be one you can defend.
Open any scenario, click a candidate, see how the system read their materials, and decide for yourself.
Start with a scenario