AI proposes. You decide.

Who to staff, who to promote, what to pay. Castena works out the answer from your performance, staffing, compensation, and recruiting data. Every recommendation comes with the evidence chain behind it, so you can simulate the decision before you commit to it.

Evidence-grounded. Human-gated. Audit-ready.

0
specialized AI agents
RecruitingStaffingPerformance reviewsTalent profilesCalibrationCompensation1-on-1 prepTraining

You ask one question. Castena routes it to whichever agents it needs, and they work out the answer together.

Castena Copilot

Start a real conversation with our team

From Score-and-Rank to Intelligent Staffing

Frustrated team using spreadsheets for hiring

Rule-Based Matching

Score, rank, done

  • Top scores win: nothing else gets asked.
  • Blind to people: burnout and resignation risk never enter the math.
  • Same seniors every time: juniors never get the growth project.
  • No memory of why: ask why someone was picked last quarter. Silence.
Efficient team collaboration using Castena

Castena Hybrid Engine

Score, reason, decide

  • The AI asks the next question: top score, yes. But 95 percent resignation risk. Swapped out, with the reason on record.
  • Understands plain language: "these two work badly together right now" is enough, and the team is rebuilt.
  • The bench grows: mentor pairings drawn from what reviews actually say, not from the org chart.
  • Every decision has a receipt: reasons cite their sources, ready for any audit.

And in Castena, nothing takes effect without a named human approving it.

What the AI sees that never fits in a spreadsheet cell.

Every staffing decision depends on knowledge no skills profile holds. In Castena, managers write it down as a note, and the AI must cite it whenever it changes a recommendation because of it.

These two can't seem to collaborate effectively right now
Ready for the lead role, but only with a mentor through phase one
Just carried a brutal go-live, needs a lighter quarter
The client asked for someone by name

Notes are never a shadow file. Every note is attributed to its author, visible in the audit trail, and citable down to the source. What influences a decision is always on the record.

The numbers decide who is possible. Your knowledge decides who is right.

Manager note
Team note: collaboration friction

Saved as evidencedoc_3f9a12e8
Decision record

AI weighing the note against skills, availability and workload...

Build the Perfect Team in Minutes

Phase 1: A deterministic engine scores every candidate across 7 weighted dimensions — skill match, availability, cost, experience, competency, certifications, and timezone.
Phase 2: An AI layer reasons over everything the numbers cannot capture — retention health, workload balance, team chemistry, mentorship fit, and cross-project allocation.

Deterministic Only
Pure algorithmic scoring — no AI reasoning
Step 1
Project Phoenix — Proposed Team
Sarah ChenIC3
Backend Engineer · $12,400/mo
91
/100
Jennifer LeeIC4
Lead Frontend · $10,800/mo
88
/100
Marcus WebbIC3
Backend Engineer · $11,200/mo
86
/100
Monthly cost$34,400 / $30,000
115% utilization · $-4,400 headroom
What the algorithm cannot see:
  • • Marcus Webb's retention risk: 89% — talent assessment shows alignment gaps
  • • His sprint task velocity decreased — workload overload risk invisible to the score
  • • Unresolved conflict with Jennifer Lee from a prior code review — no metric captures this
Hybrid — AI Reasoning Layer
Scores + qualitative judgment on real work signals
Step 2
Project Phoenix — AI-Adjusted Team
Sarah ChenIC3
Backend Engineer · $12,400/mo
91
/100
Jennifer LeeIC4
Lead Frontend · $10,800/mo
88
/100
Marcus WebbIC3Excluded
Backend Engineer · $11,200/mo
86
/100
Marcus excluded by AI — retention risk (89%) and workload overload alerts
Robert TaylorIC2AI Pick
Junior Backend · $7,800/mo
74
/100
Robert substituted in — mentored by Jennifer Lee to close skill gaps
Monthly cost
$31,000 / $30,000↓ $3,400 saved
103% utilization · $-1,000 headroom
What the AI added beyond the score:
  • • Retention risk enforcement — excluded Marcus (89% risk) to protect project continuity
  • • Mentorship pairing — Robert under Jennifer's guidance accelerates his IC3 readiness
  • • Budget optimized — $3,400/mo freed for future project headroom

Scoring Engine

Why Marcus ranks higher but gets excluded

The deterministic score only ranks. The AI reasoning layer reads the evidence behind the numbers and adjusts the team before the proposal reaches you.

Dimension
Marcus WebbDet. pick
Robert TaylorAI pick
Skill Match/30
26
21
Availability/20
19
20
Cost Efficiency/15
13
15
Experience/15
14
9
Competency Fit/10
9
6
Certifications/5
3
2
Timezone/5
2
1
Total86/10074/100
DecisionSelected by algorithm — excluded by AISubstituted in by AI

What the reasoning layer reads

Objective Output Evidence

Project milestones, task completions, peer kudos content — the AI reads the qualitative outcomes, not just aggregate stats.

Team Chemistry

Past collaboration history, unresolved conflicts, seniority balance — reasoned over holistically.

Growth & Career Fit

Whether an assignment hits an IC milestone or builds long-term team strength.

Risk the Score Misses

Retention health, workload balance, concurrent project overload — none of which fit in a score column.

One project, from empty to approved.

Five Capabilities. One Platform.

Hybrid AI + Deterministic Engine

Phase 1: A 7-dimension deterministic engine scores every candidate on skill match, availability, cost efficiency, experience, competency, certifications, and timezone fit.

Phase 2: An AI reasoning layer reviews actual, provable outcomes from your project deliverables and talent tools to evaluate key qualitative signals — like team fit, retention health, workload balance, mentorship pairing, and cross-project allocation.
An industry-agnostic engine that auto-generates objective performance reviews from closed deliverables in your systems of record (project tools, CRM, support desk) and peer kudos — no more word-of-mouth.
Model merit raise cycles under strict budget pools, automatically optimizing for compa-ratio alignment, pay equity, and retention risks.
Succession depth trees map organizational readiness, while retention risk analysis flags at-risk employees with actionable retention plan recommendations.
Upload multiple candidate CVs, extract skills and professional experience automatically, and evaluate candidate fit scores side-by-side across active projects.

Unified Talent Operations.

Castena is completely industry-agnostic, integrating seamlessly with your engineering, sales, customer support, and HR systems of record (like Workday and Personio) to form a unified, evidence-based performance record.

GitHub Integration

Sync completed repository milestones, closed issues, pull request merges, and peer appreciation updates

Jira Software Integration

Track sprint milestone progress, completed task delivery, and high-level project targets

Salesforce CRM

Sync department-level closed-won deal values, total sales revenue, and team quota achievements

Zendesk Support

Sync overall support SLA compliance rates, average customer satisfaction scores, and queue volumes

Workday HCM Integration

Import organizational structure, reporting lines, and target base salary bands

Personio Integration

Sync team composition details, contract types, tenure milestones, and holiday capacity calendars

Excel Data Ingestion

Bulk-ingest historical team performance metrics, merit cycle budgets, and skills matrices from spreadsheet templates

Don't see the tools your team uses? We build custom integrations in 48 hours.

Built for European
Labor Standards

Castena is designed from the ground up to respect employee privacy rights and comply with the strictest data protection regulations, including GDPR/DSGVO and German labor law.

GDPR / DSGVO Datenschutz

We apply strict Datenschutz guidelines and data minimization. Castena tracks no sensitive PII or private employee details, keeping all evaluations focused strictly on objective performance deliverables.

BDSG § 26 & Works Council Friendly

Designed to align with German co-determination laws. Performance evaluation relies strictly on objective business outcomes (sprints, targets, KPIs) rather than intrusive behavioral surveillance (keystrokes, active hours).

Anti-Profiling (GDPR Art. 22)

Our AI operates strictly as an advisory copilot. There is no automated profiling or automated employment decisions; managers retain final approval and ownership over all staffing and performance calibrations.

Data Sovereignty

EU-region processing today, fully on-premise language models on the roadmap. Platform and data stores deploy in your environment, and every recommendation is documented in a printable, works-council-ready decision dossier.

Strict Data Minimization · No Behavioral Surveillance · Human-in-the-Loop Oversight

Join Our Beta Program

Be part of our exclusive early adopter program and help shape the future of governed talent operations. Get early access to our MVP, influence product development, and secure founder pricing.

Early Adopter Benefits

Dedicated Copilot training pairing for managers and team leaders
Tailored KPI & objective milestone configuration based on actual project deliverables
Priority support channel directly to our core engineering team
Locked founder pricing for subsequent merit planning cycles

Ready to be part of the talent operations revolution?

Talk to Our Team

Have questions about project staffing, raise modeling, bias audits, or talent calibration? We’ll help you explore the best fit for your organization.

Let's Build the Future Together

We're building something revolutionary in agentic talent operations. Whether you're interested in joining our beta program or forming strategic partnerships, we'd love to connect with you.

Email Us

contact@castena.ai

We'll respond within 24 hours

Schedule a Call

Book a 30-minute discovery call

Mon–Fri, 9 AM–6 PM (CET)

Call Us

+49 163 6548862

Direct line to the founding team

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