Startup profile · funding coverage
Giskard funding, valuation and investors
Open-source and enterprise testing platform for LLM applications, including quality, bias and adversarial security checks.
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Funding, valuation & investors
Answer-first snapshotLatest funding
Seed (2022)
€1.5M; do not treat as USD · Date not disclosed
Latest known valuation
Not publicly disclosed
Total disclosed equity funding
Not publicly disclosed
Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.
Current status
private
seed · Paris, France
Overview
Paris-based Giskard develops AI testing and red-teaming software for LLM and machine learning systems, evaluating unsafe outputs, vulnerabilities, robustness and quality. Its public record describes a €1.5M seed in 2022 led by Elaia, and support from the European Innovation Council that included public investment and grants.
VCT separates EUR-denominated capital commitments from grants and does not convert European funding into USD using arbitrary exchange rates. For teams considering the tool, open-source scanner capabilities should be compared with paid platform features and workflow integration.
Why Giskard is interesting
Giskard is a practical alternative to Dreadnode and Promptfoo for teams that want developer-accessible automated AI testing; crucially, grant funding is not equivalent to a priced venture round.
Product & use cases
Automated LLM and ML security testing, quality assessment, model scanning, red-team evaluations and governance reports.
- Run LLM application security test suites
- Identify quality failures or prompt injection risks
- Automate regression testing when changing models or prompts
Funding history (newest first)
Seed (2022)
€1.5M; do not treat as USDSource: Read source · giskard.ai
EIC Accelerator (2023)
European support includes investment; clarify equity versus grant termsSource: Read source · giskard.ai
Competitive landscape
Edge: Accessible testing framework with open-source components; commercial support and evaluator coverage should be checked independently.
The closest alternatives differ by whether they investigate existing telemetry, orchestrate incidents, evaluate AI agent behavior, or prevent adversarial exploitation. Selection should be based on representative task-level tests, deployment permissions, data control and total ownership cost.
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Dreadnode direct
Offensive AI security testing
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Promptfoo direct
LLM red-team and evaluation testing
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Mindgard direct
AI vulnerability research and automated testing
Industries
Market / IPO context
Editorial / static context — not a live quote.
FAQs about Giskard
Practical answers founders, operators, and investors typically search for.
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Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.