Strategic people intelligence

HR is the only business function without real performance data. We change that.

Get Ikigai connects business strategy to people strategy — in real time. Making people decisions based on people data, not gut feel.

Company-wide skill map in weeks, not months.

Book a Demo See how it works

Two deadlines every people leader is already worried about.

Regulation

EU Pay Transparency Directive — 2026

Every role needs an auditable grading justification based on competencies, effort, responsibility, and working conditions. Most companies are still running this in spreadsheets. The clock is ticking and consulting projects take 6–12 months.

Market shift

AI-generated CVs are breaking recruiting

Every application now looks perfect on paper. Keyword matching is dead. Traditional pipelines surface noise, not signal. Talent Acquisition leads need a new way to actually understand candidates — and an internal-first view of who they already have.

Less consulting. More clarity. Faster.

Outcomes, not feature lists. Every tile is something your board actually asks for.

Know your real capabilities

Full skill visibility across the organisation, built from actual conversations with employees and managers — not forms, not self-assessments, not CVs.

Cut recruiting costs significantly

Internal candidates surfaced before external hiring starts. Every internal fill saves ~120K. Reality-checked requirements grow your pipeline 3–5x.

Make L&D spend defensible

Every training euro tied to a business-critical gap. "This learning path avoids 3 external hires worth 360K" is a sentence you can take to a budget meeting.

EU compliance, automated

Pay equity dashboards and audit-ready grading reports. EU Pay Transparency Directive compliance in 4 weeks instead of a 12-month consulting project.

AI interviews replace forms and spreadsheets

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

ikigai · System overview Open in new tab →

What this actually looks like

AI interviews your employees and managers — separately, about the same roles. Compares what they say. Turns every finding into a concrete action: hire, develop, transfer, regrade. Works from week one. No big project required.

1. AI Interviews (Separately)
Ik
You mentioned data pipelines. What tools do you use? How complex are they?
LS
Mostly Python and SQL. Some Airflow. I built the real-time scoring pipeline last quarter.
Ik
Tell me about that scoring pipeline — how many data sources? What happens when it breaks?

Employee: 15–30 min about their work, goals, interests.

Manager: 20–30 min about team roles, needs, strategy.

Both about the same roles — independently.

2. Compare Both Perspectives
Employee says
"I build and maintain data pipelines — it's engineering work"
Manager says
"She handles our analytics — reports and dashboards"
AI Finding
Role mismatch. Employee does engineering, classified as analytics. Grading, development plan, and team capacity are all wrong.

Where they agree = confirmation. Where they disagree = the most valuable finding in the system.

3. Concrete Actions (1 Click Each)
REGRADEReclassify: Analytics P3 → Data Eng. P4 (justification generated)
DEVELOPNew career path: cloud engineering (matches stated goal)
BACKFILLAnalytics gap created → job req auto-generated

Every finding links to actions. Click and it generates the job req, development plan, or regrading justification.

The AI becomes every manager's strategic partner

Managers don't just see dashboards — they interact with an AI that knows their team, their gaps, and their goals. Here's what that looks like across different challenges.

Recruiting
Transformation
Team Development
Project Staffing

Manager runs recruiting end-to-end

No more waiting for recruiters to coordinate. The manager handles briefing, reviewing, interviewing, and deciding — with AI support at every step.

1
AI Briefing: 20-min chat. AI asks structured questions, gives reality checks ("this combo = 120 candidates in DACH").
2
Internal First: System surfaces internal candidates before posting — including people who'd fit with training.
3
Interview Prep: Per candidate: AI-generated briefing with key questions and strengths to probe.
4
Post-Interview: AI captures structured feedback. Candidate ranking updates dynamically.
5
Decision: Candidates ranked by fit with all data aggregated. Manager decides with full picture.

What the manager gets

Realistic job requirements (no more unicorn wishlists)
Internal candidates surfaced before external search
Prepared for every interview — candidate-specific questions
Dynamic ranking that updates with each new data point
Self-sufficient — recruiter shifts from coordination to strategy

Manager navigates a transformation

Department restructuring, new technology adoption, market expansion — the AI helps the manager understand what their team can handle and what they need.

1
Strategy Chat: Manager describes the transformation goal. AI maps it to required capabilities.
2
Team Assessment: Shows which team members have relevant skills and who's interested in the direction.
3
Gap Timeline: Which capabilities are missing, when they become critical, and what the sources are.
4
Action Plan: Per gap: develop, hire, or borrow — with costs and timelines for each option.
5
Scenario Simulation: "Develop all internally" vs. "hire" — side-by-side comparison.

What the manager gets

Clear picture of team readiness for the change
Gaps traced back to specific strategic goals
Quarterly action plan — not a one-time slide deck
Cost and timeline comparison for every option
Data to justify headcount requests to leadership

Manager develops their team strategically

Not generic training catalogs — personalized development plans tied to team goals and individual career aspirations.

1
Team Overview: Each team member's profile: skills, goals, progress, and perspective differences.
2
Gap Identification: Where are they vs. role requirements? Vs. their career goal?
3
Development Plans: AI-generated personalized plans with L&D matched to gaps, quarterly milestones.
4
Progress Tracking: Completed training updates skills automatically. Manager sees who's on track.

What the manager gets

Knows each team member's actual capabilities and ambitions
Spots where employee and manager see the role differently
Personalized development plans — not one-size-fits-all
L&D tied to business need
Retention signal: employees who see a path stay

Manager staffs a project with the right people

Need specific skills for a 3-month project? The system finds them across the organisation — including people you didn't know existed.

1
Define Need: Manager describes the project and required capabilities in a chat.
2
Cross-Team Search: AI searches across all departments based on deep interview data, not job titles.
3
Availability Check: Shows current workload and their manager's contact info for loan discussions.
4
Temporary Assignment: System tracks the project loan. Both managers see the arrangement.

What the manager gets

Find talent in departments you've never talked to
Skill-based matching, not "who do I know" networking
Internal freelancer market — borrow before you hire
Cross-department collaboration that wouldn't happen otherwise
See it on your data →

Faster, richer, and actually used.

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 reduced because both sides describe the same role independently.

Simple enough that people actually use it

15–30 minute chat, role-based views, one-click applications. Minimal friction, high completion rates.

Trusted by people who've seen every HR tool.

“[TODO: Pull a real quote from a pilot customer or advisor here. Something that speaks to the shift from spreadsheets to live, actionable workforce intelligence — and to the speed of getting there.]”
— [Name], [Role], [Company]

Supported by

[Partner 1]
[Partner 2]
[Partner 3]
[Partner 4]
[Partner 5]

See what happens end-to-end.

Four scenarios. From AI interviews to findings to actions. Realistic, illustrative, grounded in the platform.

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? What does a typical week look like?"
Reality Check
AI Finding: This profile = ~120 candidates in DACH. ~30 reachable at your salary band. Meanwhile, your top AE (Julia Meier) doesn't match 3 of these 5 "must-haves" — she came from B2C with no SaaS background. 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 — both have strong product knowledge and client relationships.
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 (78%) — SQL and Python transfer directly. Gap: Airflow + cloud data services, closable in 3–4 months.

Analytics Engineer (72%) — 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? How do you handle schema changes in your pipelines?" 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 for an external data role. Marketing gets advance notice. Hiring manager gets a candidate who already knows the data.

EU Pay Transparency compliance. 4 weeks, not 12 months.

Scenario · illustrative case study

Data Collection
AI interviews employees and managers about every role. 252 employees, ~3 weeks.
No consultants scheduling workshops. No forms. No managers struggling with HR terminology.
AI Generates
Complete job architecture: 6 function families, 6 levels (P1–P6), salary bands, and an audit-ready justification for every grading, based on EU criteria: Competencies, Effort, Responsibility, Working Conditions.
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 — may need sub-levels
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, and a PDF.

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 — derived from employee, manager, and strategy data.

See how it works for your usecase

Each usecase page has a walkthrough video, interactive prototype, and a concrete example — all in one place. Pick the one closest to your biggest problem.

Start with your most pressing usecase.

Every module is standalone. Data gathering and setup in a week. Results immediately. 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. No big project required.

Live in week one
Works standalone. Integrates with your existing HRIS/ATS/LMS.
Weeks 2–4

First results

Interview completion, first findings, first actions. Reality-checked requirements, surfaced internal candidates, initial grading architecture — whatever your starting module delivers.

Tangible outputs in weeks
Every finding links to a concrete one-click action.
Whenever

Add layers as needed

Add strategy input. Add more modules. Add more integrations. Same interview data, more value. No re-implementation, no duplicate work.

Ongoing, continuously improving

We add the intelligence. You keep your stack.

Built for European enterprises from day one. Every concern a skeptical buyer has — answered.

Human in the loop

AI recommends, humans decide

Every action is a suggestion a person approves. Critical for works councils, compliance teams, and anyone who has to explain the decision later.

Privacy

GDPR by design

Data residency in the EU. Role-based access. Right-to-erasure baked in. DPAs and ToMs ready for your legal team.

Integrations

Works with your HRIS, ATS, and LMS

Bidirectional connectors for Workday, SAP SuccessFactors, Personio, BambooHR, Greenhouse, Lever, and more. We add intelligence without ripping anything out.

Betriebsrat

Betriebsrat-compatible

No individual evaluation of employees. All analytics aggregated and anonymised. Transparent to employees, auditable by works councils.

WorkdaySAP SuccessFactorsPersonioBambooHRGreenhouseLeverTeamsSlackCustom API

Built by HR operators and AI engineers.

We got tired of spreadsheets and 12-month consulting engagements, so we built the tool we always wished we had.

Meet the team →

Priced around your organisation.

Not a seat count. Start with one module, add more as you grow. A 30-minute call is enough for an indicative number.

See pricing approach →