Kontext

๐Ÿ“‚ Cybersecurity๐Ÿ“‚ Artificial Intelligence๐Ÿ“‚ Software Enterprise๐Ÿ“ Munich๐Ÿ—“๏ธ Founded: 2025

Munich startup, runtime security for AI agents. $4M seed in 2026 led by 42CAP.

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Podcast Episode

๐ŸŽ™๏ธ Deep Dive

Metycle, 50skills, Kontext โ€” โ‚ฌ131.7M for recycled metals, $6M for HR AI and $4M for agent security โ€” Daily News September 24, 2026

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About Kontext

The Munich startup sits between AI agents and the tools they use, running as a local daemon that checks every tool call (including from coding agents such as Claude Code and Codex) against policies defined at multiple levels (organisation, group, user, agent, repository, branch). It can observe, block, or require approval before execution and records every decision for audit. The product started with coding and customer-support agents but is shifting toward supporting agents customers run themselves, with adoption concentrated mostly in financial services (mid-sized and large banks and fintechs). In September 2026, Kontext closed a $4 million seed round led by 42CAP, with participation from a16z CSX and HTGF; the valuation was not disclosed.

The Story

Founded in Munich in early 2025 by Jens Ernstberger (PhD in system security and applied cryptography, prior venture-capital experience at a16z Crypto) and Michel Osswald (electrical engineering background, moved into cybersecurity), both with backgrounds in secure computing, applied cryptography, and AI systems.

How Kontext works

Business Model

Tiered B2B SaaS (free Starter, Pro, Scale) plus enterprise contracts, aimed mainly at development and security teams in regulated companies.

Revenue Model

SaaS subscriptions: free plan for developers, $149/month for teams (Pro), $499/month for higher volume and retention (Scale), custom enterprise pricing.

Products & Services

Core Use Cases

  • Pre-execution control of AI-agent actions
  • Enforcement of contextual policies
  • Audit trails for coding agents and cloud workloads

Market & Clients

Key Customers

Security and software-development teams; enterprises deploying AI agents, with early adoption concentrated among mid-to-large banks and fintechs.

Geographic Presence

DE

How Kontext competes

Competitive Advantages

  • Decision-making before execution
  • Combined context of identity, task, and resource
  • Observe mode before enforcement

How Kontext grows

Growth Strategy

Expand the engineering team; support more agents and environments; convert free developers into team and enterprise plans.

Distribution Model

Free plan for developers; SaaS subscriptions for teams ($149/month Pro, $499/month Scale); custom enterprise contracts.

Moat (Defensibility)

Multi-level, task-aware policy engine; runtime action and decision data collected in production.

Regulatory Context

EU AI Act and governance obligations; GDPR and data minimisation in logs; audit and security requirements for regulated sectors such as financial services.

Key Risks

  • False positives that interrupt work
  • False negatives that allow harmful actions
  • Performance overhead and fragile integrations
  • Competition from cloud and identity platforms

Strategic Insights

  • Early customer concentration in financial services suggests AI-agent controls find their first paying demand where mistakes carry an immediate regulatory or financial cost.
  • Shifting from a coding-agent focus to broader support for customer-run agents is a typical demand-driven product pivot rather than the original thesis.

Funding & Investors

Total Funding

{'amount': 4000000, 'currency': 'USD', 'amount_display': '$4M'}

Funding Rounds

  • 2026 - : $4M
    Lead: 42CAP

Key Investors

42CAPa16z CSXHTGF

Founding Team

Founders

Jens ErnstbergerCo-founder and CEO
Michel OsswaldCo-founder

Key Executives

  • Jens Ernstberger - CEO

Key Metrics

Seed$4 milioni (2026)
Prezzo piano Pro$149 al mese (2026)

Lessons from Kontext

  • Public, tiered pricing (free, team, enterprise) can be a credibility lever for a still-young security product, making perceived value tangible.
  • In security products, starting in observe mode before enforcement can reduce adoption friction, letting policies be validated against real traffic before blocking actions.

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Sources

๐ŸŽ™๏ธ Scalable Podcast โ€” European startup stories ยท ๐Ÿ‡ฎ๐Ÿ‡น in Italian
Spotify ๐ŸŽง Apple