People Intelligence Platform

Uncover the skills, gaps, and potential your HRIS will never show you.

HR doesn’t have a tools problem. It has a data problem. Get Ikigai collects HR data, maps skills, identifies gaps, and turns every insight into a concrete next step: hire, reskill, promote, or plan — compliant by design.

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What changes when you use Get Ikigai

The outcomes that make HR measurable, strategic, and ready for what’s next.

From gut feel to data-driven decisions

Make people decisions with the same confidence finance makes budget decisions. A complete, always-current picture of every skill, gap, and potential.

From reactive to proactive

Restructuring, growth, new markets — every change hits your workforce. See the impact in real time: what capabilities you have, what’s missing, and what to do about it.

Do more with the people you have

Fill roles internally before recruiting externally. Develop instead of hiring. Invest L&D budget where it actually moves the needle.

End-to-end talent management

Need assessment, Recruiting, onboarding, development, internal mobility, compensation architecture — connected in one platform.

HR doesn’t have a tools problem. It has a data problem.

Deep, structured intelligence across your entire organisation — gathered through conversations, not questionnaires.

1

Collect

Individual personalized AI chats to gather deep data from employees, teamleads, leadership, and talents.

2

Understand

All data synthesised into connected insights, always up to date. Skills mapped, gaps identified, weighted, and projected over time.

3

Act

Going further than dashboards — showing every stakeholder the next relevant action to achieve their goals.

ikigai · System overview Open in new tab →

Human in the Loop by design.

AI proposes and enables. Humans give input and decide. Every step of the way, people stay in control.

Three reasons Get Ikigai delivers where traditional HR projects stall.

Faster than any HR process before

AI chat instead of forms and workshops — data in days, not months. Async, pausable, no scheduling required.

Richer than self-assessment alone

Employees, managers, and leadership in one consistent picture. Bias and blind spots counteracted and reduced.

Simple enough that people actually use it

15–30 minute chat, immediate next actionables, automated applications → Minimal friction, high interaction rates.

See what happens end-to-end.

Four scenarios. From AI interviews to findings to actions.

Sales Scaling
Career Pivot
Grading & Compliance
Cloud Transformation

VP Sales needs 5 AEs. The AI catches a problem.

Scenario · illustrative case study

Manager Interview
VP Sales: “I need 5 senior AEs with enterprise SaaS experience, fluent German/English, 50% travel, 500K+ ARR track record.”
AI follow-up: “Which of these requirements actually predict success?”
Reality Check
AI Finding: This profile = ~120 candidates in DACH. ~30 reachable at your salary band. Meanwhile, your top AE doesn’t match 3 of these 5 “must-haves.” Your requirements describe a wish list, not your success profile.
Employee Data
Interviews with current AEs: actual travel is 20% (not 50%), SaaS matters less than consultative selling, and 2 people on the Customer Success team expressed interest in sales.
Source: 8 employee + 2 CS team interviews
Actions & Result
TRANSFER Move 2 from CS to Sales (87% and 79% match)
HIRE Open 3 AE positions with adjusted requirements (pool: ~30 → ~450)
DEVELOP 4-week sales methodology onboarding for internal transfers

Result: 2 filled internally (faster, cheaper). 3 external hires with realistic requirements = faster pipeline, better fit.

Marketing analyst wants to move into data. The system finds the path.

Scenario · illustrative case study

Employee Interview
Sarah builds campaign dashboards in SQL, automates reports in Python, analyzes customer segments. She says: “I want to build the systems underneath, not just the reports.”
Matches Found
Junior Data Engineer — SQL and Python transfer directly. Gap: Airflow + cloud data services, closable in 3–4 months.

Analytics Engineer — Strong data modeling overlap. Gap: dbt + warehouse architecture, 4–5 months.
Qualifying Chat
When Sarah clicks “Express Interest”, the AI asks deeper questions: “You mentioned Python — have you worked with data quality frameworks?” This deepens her profile for that specific match.
Actions & Result
MATCH Sarah sees both roles with one-click application
DEVELOP Plan: Airflow (4 wk) + dbt (3 wk) + cloud services (6 wk)
BACKFILL Marketing reporting capacity gap flagged

Result: Sarah stays instead of leaving. Marketing gets advance notice. Hiring manager gets a candidate who already knows the data.

EU Pay Transparency compliance — job architecture designed and filled by the AI.

Scenario · illustrative case study

Data Collection
AI interviews employees and managers about every role.
AI Generates
Complete job architecture: 6 function families, 6 levels (P1–P6), salary bands, and an audit-ready justification for every grading.
Findings
4 pay equity issues caught:
1. Female engineers at P4 earn 8.2% less than male peers
2. Two sales roles perform identical work, banded 15K apart
3. One engineer above band ceiling
4. Sales P4 has 30% salary spread
Actions & Result
CORRECT Adjust 2 salaries: +4,800 and +6,200 EUR
UNIFY Merge two equivalent sales functions
REVIEW 4 flags in HR queue with one-click resolution

Result: Full compliance in 4 weeks. Auditable justification per role. Typical consulting: 6–12 months, 150K+ fees.

CEO says “cloud-native by 2027.” What does that mean for people?

Scenario · illustrative case study

CEO Interview
“Migrate all infrastructure to cloud-native by Q4 2027.”
Manager Interview
“My team can handle the application layer, but we have zero Kubernetes experience.”
Employee Data
2 of 14 have Docker experience. 1 used Terraform before. 3 expressed interest in cloud/DevOps.
Gap confirmed from all three sides.
Actions & Result
DEVELOP Q2: Cloud training for 3 internal candidates
HIRE Q2: 2 Senior Cloud Engineers
HIRE Q3: 3 mid-level
SCENARIO “All internal” = +5 months, -375K. “All external” = -5 months, +225K, market risk.

Result: A costed, quarter-by-quarter plan the CEO can sign off on.

Unlock end-to-end people intelligence with one data foundation unlocking 6 use cases.

Not sure where to start → Talk to us!

Start with your most pressing use case.

Every module is standalone. Data gathering and setup in a week. Expand whenever you’re ready — the same interview data powers every module.

Week 1

Pick your starting point

Recruiting? Pay grading? Workforce planning? Whichever is most urgent. Setup, integrations, and AI interview rollout happen in parallel.

Live in week one
Weeks 2–4

First results

Interview completion, first findings, first actions. Every insight is presented and translates to a relevant action.

Tangible outputs in weeks
Whenever

Add layers as needed

Add strategy input. Add more modules. Same interview data, more value. No re-implementation.

Ongoing, continuously improving

Works with the tools you already use.

SAP SuccessFactorsWorkdayPersonioGreenhouseSmartRecruitersLMS via API

Built for Europe. Compliant by design.

GDPR by Design

Data stays in Europe. No exceptions. Consent management, right to access and deletion, role-based access control, full audit trails.

EU AI Act Ready

Designed as a high-risk system from day one. Human-in-the-loop, bias detection, technical documentation. No emotion recognition — never built, never will.

Works Council Ready

Full audit trails. Who asked what, who saw what, who decided what — captured end-to-end for Works Council and external audits, making decisions transparent and arguable, protecting employee privacy.

EU Pay Transparency

June 2026 deadline. Job architecture, grading reports, and pay gap analysis — delivered in weeks, not months.

FAQ

How long does implementation take?
Your first skill map is ready within days. No lengthy consulting project. No 6-month rollout.
What about our Works Council?
Full audit trails. Who asked what, who saw what, who decided what — captured end-to-end for Works Council and external audits, making decisions transparent and arguable, protecting employee privacy by taking careful control over the shared and aggregated data. Raw chat data will never be visible.
Does it integrate with our existing HRIS?
Yes — SAP SuccessFactors, Workday, Personio, and others via API. Read-first, sandboxed architecture means your existing data is never at risk.
How is our data protected?
GDPR by design. Data hosted in Europe. No data used for model training. Full encryption at rest and in transit. Audit trails for every data access.
We already have Personio / SAP. Why do we need Get Ikigai?
Your HRIS records what happened. Get Ikigai reveals what’s possible. It’s the intelligence layer on top — not a replacement.