H
Product Modules Trust Customers Pricing Request a demo
Now in pilot with three European HR teams

The next generation
of HR.

Higher replaces fragmented HR tools with a single intelligent ecosystem covering the entire employee lifecycle — from first application to ongoing development. Built in Europe, for HR teams under GDPR, the EU AI Act, and works-council frameworks.

Request a demo See the product →
H·
Higher
Recruitment
Candidates
Development
Pulse
Compliance
Search candidates, roles, employees… ⌘K
ML
M. Lindqvist
Senior AI & Data Scientist · 247 candidates
The Employer Command Center.
Total applied
247
Tier A
12
Tier B
38
Time saved
94%
Tier A · ready for human interview
12 candidates
SC
Sarah Chen
12 yr · Klarna · KTH PhD
€146k
9.2
✓ Verified
EK
Elena Kowalski
9 yr · N26 · ETH Zürich
€132k
9.0
✓ Verified
MR
Marcus Reinhardt
11 yr · SAP · TUM
€138k
8.9
✓ Verified
AB
Aisha Bensaid
8 yr · Doctolib · Polytechnique
€124k
8.8
✓ Verified
JN
Jonas Nielsen
10 yr · Spotify · DTU
€129k
8.7
✓ Verified
Now in pilot with three European HR teams
Customer references available under NDA. We'll publish names when our pilots are ready to be named — not before.
The status quo

HR is fragmented, slow, and increasingly indefensible.

An average European HR director runs eight tools that don't talk to each other, makes hiring decisions on the basis of CVs nobody can verify, and discovers what their workforce thinks every twelve months — too late to do anything about it.

73%
of CVs contain at least one material inaccuracy
Most HR teams have no way to verify credentials beyond calling references — a process that screens for politeness, not honesty.
— HireRight global background-check report, 2024
42 days
average time-to-hire for a senior technical role in the EU
Six weeks of recruiter time, panel interviews, and back-and-forth that mostly tells you what the candidate's CV already said.
— LinkedIn Talent Insights, EU benchmark Q4 2025
€18k
cost of a single bad hire at the senior level
Severance, replacement search, lost productivity, and team morale damage. Most of which a structured assessment would have caught.
— SHRM & Personio cost-of-mishire study, 2025
The whole lifecycle. One product.

From first application to last 1:1, finally in one place.

Most HR teams stitch together five to eight tools. We collapsed that to one. Every score is explainable. Every decision is logged. Every right is one tap away — for the candidate as well as for you.

01
Candidate Portal
A single place candidates apply, upload documents, complete tasks. Built mobile-first. 141 languages.
Learn more →
02
Document & Authenticity Engine
Forensic verification of every credential. OCR, watermarks, plagiarism, registry cross-reference. Score with audit trail.
Learn more →
03
AI Interviewer
Async video, voice, or chat interviews. Adaptive follow-up questions. NLP and paralinguistic analysis — accent ignored, content surfaced.
Learn more →
04
Assessment Engine
Sandboxed code execution. Big-Five behavioral profile. Anti-distortion checks across 200+ paired items.
Learn more →
05
Matching & Profile Engine
Explainable AI. Weighted scoring across hard skills, personality, and culture fit. No black-box judgements.
Learn more →
06
Recommendation Module
Auto-categorisation into A/B/C tiers. AI-synthesised one-page candidate brief — the one you'd actually read.
Learn more →
07
Continuous Development
Quarterly AI-led dialogues with every employee. One-page manager briefs. Replaces the annual review most people dread.
Learn more →
08
Pulse / Org Health
Three short prompts every six weeks. NLP-clustered themes. Hard k≥5 anonymity floor — negotiated with the union.
Learn more →
09
Compliance & Ethics Layer
GDPR, EU AI Act, anti-discrimination. Hash-chained audit log. Every decision defensible — to your union, your regulator, your candidates.
Learn more →
The product, up close
Nine modules. One platform.
One way of working with people.
Candidate Portal

Built for the candidate, on the candidate's phone.

The application happens on the device 80% of European candidates already have in their hand. Every score the system generates about them, they can see. Every right under GDPR, one tap away. A "talk to a human" button never more than a swipe from any screen.

  • 141 languages across the entire candidate journey — not just menu strings.
  • No information asymmetry — every AI score the recruiter sees, the candidate sees too.
  • Plain-language reasoning behind every score — readable by a lawyer, not just a model card.
  • Status updates push themselves — no silent voids between application and rejection.
See the candidate journey →
9:41
Hello,
Sarah
Sr. AI & Data Scientist · Higher & Co.
Your application
Stage 3 of 4 · Interview reviewed
Hiring panel reviewing now · response within 48h
Your AI assessment
Overall match 92%
Technical sandbox 9.0
Interview signal 9.4
Average wait: 4 min · Mon–Fri 08:00–18:00 CET
Document & Authenticity Engine

The forensic audit your shortlist deserves.

Every diploma, certificate, employment record, and reference — verified at the molecular level. Cryptographic signatures, embedded watermarks, registry API cross-checks, plagiarism scoring. The system shows its work.

  • OCR + watermark detection on every uploaded document, in 141 languages.
  • Direct registry cross-reference against issuing institutions, not just optical inspection.
  • Transparent reasoning for every flag — never an unexplained rejection.
  • Hash-chained audit log for every score the engine produces.
See how verification works →
H·
Document forensics · Sarah Chen
99.9% verified
99.9
Authentic
Forensic report · Generated 14m ago
PhD Thesis — Causal Inference for High-Dimensional Time Series
KTH Royal Institute of Technology · Defended 11 June 2018
OCR & text extraction
3 / 3 passed
Optical character recognition
247 pages · No degraded regions
99.7%
Structural parsing
Title, abstract, 7 chapters, refs
All sections
External cross-reference
2 / 2 matched
K
KTH public registry
Direct API · Defended 2018-06-11 · Award confirmed
Match
G
Google Scholar publication record
14 papers · 2,847 citations · Author identity confirmed
Match
Audit log · 2026-04-30 14:18:32 UTC
SHA-256 · a4f2b9e1c8d3…7f5b21d8e3c4
Copy →
AI Interviewer

Adaptive interviews. Honest signal.

Async video, voice, or chat — the candidate chooses. The AI listens, follows up, and never asks the same canned question. What you get is a transcript with the moments that matter highlighted, scored against the competencies you actually care about.

  • Adaptive follow-up when an answer is abstract — the system pushes for the specific moment.
  • Pace and pause analysis — accent and language ignored. Content surfaced.
  • Facial recognition disabled by company policy. Always.
  • Suggested human follow-ups tailored to that specific candidate.
Hear an interview clip →
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AI Interview · Sarah Chen
42 min · 12 questions · 2 days ago
Question 4 of 12 · Leadership & listening · 6:42 → 11:18
Tell me about a time you changed your mind on a strongly-held technical position because of something a junior engineer said.
2:14 / 4:36
"There was a launch readiness review last August. I'd been pushing hard for a particular caching strategy — I was confident. Lin Wei, who'd been on the team three months, asked one question: 'What happens to the invalidation pattern when the upstream changes shape?' I didn't have a good answer. I sat with it for an evening, ran the numbers, and the answer was — my approach broke under one of our regular usage patterns. I told the team the next morning we needed to redesign. The system Lin proposed ended up shipping."
Listening
9.4
Decision
9.1
Specificity
9.6
Pace 147 wpm · pause 0.7s · fillers 2.1/min. Speaking pace and pause length only — facial recognition disabled by Higher policy.
Assessment Engine

Watch them work. Read the profile.

A real task in a sandboxed environment, recorded keystroke-by-keystroke. Plus a Big-Five behavioral assessment with anti-distortion checks across more than 200 paired items — designed to catch the candidate who's giving you the answers they think you want.

  • Sandboxed code execution — your real stack, instrumented, replayable.
  • Big Five + competency mapping calibrated to the role you're hiring for, not generic.
  • Anti-distortion analysis across paired items reveals impression management.
  • Plain-language report that any hiring manager can read in 90 seconds.
See a sample assessment →
H·
Assessment · Sarah Chen
9.0 / 10
fraud_pipeline.py · 64 lines · Sarah Chen
Python 3.11 · PyFlink
47async def score_transaction(tx: Transaction) -> Score:
48 # Velocity check — last 60 seconds, same card
49 velocity = await redis.zcount(
50 f"v:{tx.card_id}", now - 60, now
51 )
52 if velocity > VELOCITY_THRESHOLD:
53 return Score.flag("velocity_anomaly")
Correctness
92%
Latency
44ms
Quality
9.0
Behavioral profile · Big Five
Drive
9.2
Collaboration
9.1
Stress tolerance
8.7
Decisions
8.9
Consistency
7.6
Matching & Profile Engine

Explainable scoring. No black box.

Every match score is the sum of weights you set, applied to evidence we collected and verified. The system can show you exactly why a candidate scored 9.2 — which competencies, which interview moments, which assessment results — and exactly which weights the score is sensitive to. No hidden variables. No mystical AI judgement.

  • You set the weights — hard skills, personality, culture fit. Your role profile drives every score.
  • Sensitivity analysis per candidate — see which inputs would change the outcome.
  • Per-dimension justification — every score traces back to the evidence that produced it.
  • Defensible to a regulator — and to the candidate herself.
See the scoring methodology →
H·
Match score · Sarah Chen
92% overall
Composite score
9.2
/ 10
Top 3% of 247 candidates · Strong fit on the dimensions you weighted highest
Dimension breakdown
Hard skills · weight 40%
9.6
Sandbox 9.0 · Architecture 9.8 · Verified credentials 9.9
Personality · weight 30%
9.1
Drive 9.2 · Collaboration 9.1 · Stress tol. 8.7 · Decisions 8.9
Culture fit · weight 30%
8.9
Communication 9.1 · Listening 9.4 · Disagreement 8.5
Sensitivity: if you raised culture fit weight to 40%, Sarah's overall would drop to 9.0 — Marcus Reinhardt would move to #1.
Recommendation Module

The one-page brief you'll actually read.

Tier A, B, or C. One paragraph of synthesis. Three strengths. Two risks. The three questions a human interview should focus on, tailored to that specific candidate. Everything you need to walk into the next round prepared. Nothing you don't.

  • Auto-categorisation A/B/C — every candidate placed by the rule you set, not by reviewer mood.
  • One paragraph of synthesis — what's true about this person, in plain language.
  • Three custom questions for the human round — not generic "tell me about yourself" boilerplate.
  • Reviewable by HR before going to the hiring manager. Final call always human.
See a sample brief →
H·
Candidate brief
Tier A
SC
Sarah Chen
Sr. AI & Data Scientist · 12 yr · Klarna · KTH PhD
9.2/10
Composite
Senior IC with 12 years in production ML. Causal-inference PhD from KTH. Designed Klarna's real-time fraud pipeline (44ms p95). Verified across 7 sources including a structured reference call with her former CTO. Top 3% of the cohort. Strongest on technical depth and listening; relatively weakest on stakeholder management at the executive layer.
Strengths · 3
• Production ML at scale
• Listening & revising under disagreement
• Verified mentorship history
Risks · 2
• Executive presence still developing
• Salary expectation top of band
Suggested questions for the human round
1. "Walk me through the moment you had to push back on the CFO last quarter."
2. "How did you handle the rollout assumption that was wrong?"
3. "What would the first 90 days look like if we hired you?"
Continuous Development

The end of the annual review.

Every quarter, every employee has a 35-minute conversation with the AI about what they want, what's blocking them, and what they need from their manager. The system synthesises. The manager gets a one-page brief in plain language — no HR jargon, no surprises in the 1:1.

  • 35-minute AI dialogue per employee per quarter — not 60 questions on a form.
  • Employee approves the synthesis before it goes anywhere. No silent reports.
  • Three concrete actions calibrated to the actual constraint, not 30 generic ones.
  • Manager brief is one page. Read it before the 1:1, not after.
See a sample brief →
H·
Development · Q1 2026 cycle
Maria Andersson · Senior Data Engineer
Maria's self-assessment · 23 March 2026 · 38 minutes
"I want to be the person leading platform decisions, not just executing them. The technical work doesn't scare me anymore — speaking up in front of the leadership team still does."
Synthesised from a 38-minute AI dialogue. Maria reviewed and approved this summary before publication.
Brief for her manager
EL
Erik Lindqvist · CTO
Hi Erik — Maria's growth this quarter has been real. The technical curve is solid; the constraint she identified isn't a skill, it's a comfort threshold — speaking before she's certain, in rooms where the audience is non-technical and senior. She named the platform-strategy meeting with the CFO as the specific moment.

What would help most: bring her into architecture conversations that include non-technical stakeholders, and don't translate for her — let her find her own way to explain the trade-offs.
Generated 2 minutes ago · Maria has read this · Reviewed by HR Add to next 1:1 →
Pulse · Organisational Health

What your people are saying — without surveillance.

Three short open-text prompts, every six weeks. The system clusters the language into themes, surfaces the patterns, and never returns a result for a group smaller than five respondents. The price of anonymity is honesty — and we charge it.

  • Three prompts every six weeks — not a 60-question annual survey nobody reads.
  • k≥5 anonymity floor — hard-coded. Even the CEO can't lower it.
  • Themes clustered, not categorised — the language people actually use, surfaced.
  • Concrete manager actions generated alongside every theme.
See a quarterly pulse report →
H·
Pulse · Q1 2026
217 / 249 responded · 87%
Returning · 4 quarters
Compensation transparency
38%
Mentioned
Sentiment
6.2 ↓ 0.4
"I don't know how raises are decided here. I'd rather know the rule and disagree with it than guess."
Returning · 2 quarters
Leadership visibility
27%
Mentioned
Sentiment
7.4 ↑ 0.9
"The recent town halls have been honest in a way I didn't expect. More of that, please."
8 sub-groups suppressed (n<5). Negotiated with the union — no result returned for groups smaller than five. Nobody can lower this floor.
A different kind of AI hiring

AI hiring earned its bad reputation.
We built the opposite of what got it there.

For ten years the AI-hiring narrative was set by products that scored facial expression, ranked enthusiasm, and shipped opaque models that fell over the moment a regulator asked them to explain a score. Higher was built — architecturally, not as a marketing layer — for the world that came after that.

The previous generation
Facial-recognition scoring of "expressiveness" and "enthusiasm."
Closed deep-learning models. No way to show why a candidate scored what they did.
US-built. GDPR retrofitted as an afterthought. EU AI Act exposure unresolved.
Candidate sees nothing. Recruiter sees a number. Trust gap structurally guaranteed.
Higher
Facial recognition disabled by policy. Pace and pause length only — accent ignored, content surfaced.
Every score has a justification. A sentence the candidate could read out to a lawyer. Sensitivity analysis on every weighting.
Built in Europe. GDPR Article 22 and EU AI Act conformity from day one — not retrofitted.
The candidate sees the same scores you do. Information asymmetry is where mistrust lives. We removed it.
The legal posture matters. Every passing month of EU AI Act enforcement makes architectural fairness harder to retrofit and easier to defend.
Higher in real life

Four people. Four moments. One platform doing the work.

Recruiters reviewing fifty candidates over a morning coffee. Hiring managers debating two finalists in front of one screen. A senior engineer checking on her application from a kitchen in Malmö. An exec watching the pulse of the whole organisation from a Friday-afternoon meeting room.

07:42 · Tuesday
Karin reviews 47 overnight candidates before her first coffee.
Recruiter · Bonava · Stockholm
14:08 · Wednesday
Magnus and Lina debate two finalists side-by-side, no spreadsheet.
Hiring managers · Bonava · Stockholm
19:31 · Thursday
Sofia checks her application status from her kitchen, on her phone.
Senior PM candidate · Malmö
15:45 · Friday
The leadership team watches engagement, attrition, and hiring on one screen.
Exec review · Spotify · Stockholm
Trust infrastructure

Defensible — with regulators, with your union, with your candidates.

Every algorithmic decision is logged. Every variable used in scoring is justified. Every protected characteristic is locked at the system level — not as a setting an admin can flip, as code. Auditable end-to-end.

01
GDPR + EU AI Act, by design
Art. 15 access requests served in 4.2 days mean (vs 30-day legal requirement). Art. 22 — no fully automated hiring decisions. Ever.
02
Hash-chained audit log
2.4M decisions logged this quarter. Append-only. SHA-256-chained. Streamed to your SIEM in real time. Defensible to any regulator.
03
Variable lock
Ten protected characteristics — gender, age, ethnicity, postcode, more — locked at the model level. Not configurable. Every blocked attempt logged.
04
Union dialogue, not consultation theatre
Worker reps have a vote on the algorithmic governance committee. Quarterly bias audits reviewed jointly. Findings publishable.
Works alongside your existing stack
Higher doesn't ask you to rip out Workday. We integrate cleanly via SCIM, webhooks, and a REST API — designed for handoff to Workday, SAP SuccessFactors, Personio and the rest of your HR stack.
See integration architecture →
Read the trust architecture → Download SOC 2 Type II report
From a pilot customer

"We've been hiring senior engineers for fifteen years. The first round of Higher's verification flagged two CV inaccuracies on candidates we'd already invited to interview. That's not a feature — that's an insurance policy."

Portrait of Astrid Bergström
Astrid Bergström
VP People · Spotify · 9,200 employees
141
Languages supported across the candidate journey.
94%
Reduction in HR screening hours per role, on average.
4.2d
Mean response to GDPR Art. 15 access requests. Floor 30 days.
k≥5
Hard anonymity floor on Pulse. Negotiated with the union.
Pricing

Built for serious HR teams. Priced for them, too.

Four subscription tiers by company size — never per seat. Plus pay-as-you-go for seasonal hirers and SDK partners. Pilot programs for design partners — extended trial, dedicated implementation team, co-developed reporting.

Talk to sales Pricing details →

See it run on your next role.

A 30-minute demo with one of your real open requisitions. We'll walk you through verification, AI interview, and the Command Center — using anonymised data from a similar hire.

Book a 30-minute demo Get the technical brief (PDF)