Generative AI is at the heart of Giskard so our product interacts with all state-of-the-art Generative AI tools and AIOps/LLMOps tools.
In terms of technical stack, we use Python, Typescript and modern frameworks/tools such as Cursor, FastAPI, Next.js, Tailwind, Playwright, Sentry, OpenAI, Anthropic, and more.
We foster a culture of cross-domain collaboration, curiosity, scientific & technical excellence, and shipping fast in direct contact with our user community and our enterprise customers.
AI Research
50.0%
Software Engineering
35.0%
Product & Design
15.0%
R&D team members
Python
Keycloak
FastAPI
Pydantic
Next.js
TypeScript
Tailwind CSS
React
GitHub
Docker
AWS
Research excellence
We work on frontier AI research problems at the intersection of Generative AI safety & security, with experienced researchers from physics, neuroscience & applied AI.
Open-Source
We develop Giskard, one of the most popular open-source Python libraries to test the quality & security of AI systems, with 5K+ stars on GitHub.
Ecosystem
We integrate with all cutting-edge AIOps/LLMOps and Generative AI tools: Anthropic, OpenAI, Cursor, Hugging Face, NVIDIA, Databricks, and more.
Our team is organized in autonomous, cross-disciplinary product squads, combining complementary profiles: AI Researchers, Software Engineers, Product Designers. The goal of each squad is to take care of specific product areas and customer problems, with a strong autonomy on how to design the technical solutions to these problems.
In terms of work methodology, we follow core principles of the agile manifesto with a focus on flexibility and ownership to ship fast. We also foster habits of open & async communication, with dedicated time to focus & learn.
LLM Agent Red-Teaming / Evaluation
Over the last 2 years, the biggest AI breakthrough has been Large Language Models (LLMs) such as ChatGPT and Claude. Despite their impressive performance, these AI models raise many risks in terms of errors (hallucinations), biases and security vulnerabilities when deployed for critical enterprise applications.
As part of an R&D consortium with Mistral, Artefact, INA and BnF, Giskard got funding from the French government (France 2030 strategic plan) to develop novel methods for evaluating LLM-based agents.
We have a 2-year R&D roadmap to work on this exciting frontier problem, with the end-goal of shipping open-source LLM evaluation & mitigation tools. This new field of ML research, sometimes called "red-teaming" (evaluation) and "blue-teaming" (mitigating) will play a critical role to help AI builders create better Generative AI systems.
LLM Agent Guardrails
This R&D project focuses on developing next-generation guardrails for LLM agents that operate with near-zero latency while providing comprehensive protection against hallucinations, toxic content, and security vulnerabilities.
These guardrails are aimed to be highly customizable, through a modular component system that allows LLM engineers to enforce specific safety measures without compromising the underlying agent's inference time, and meeting specific industry requirements (finance, manufacturing, retail, infrastructure).
You can get an offer in 4 weeks 🚀
HR fit interview: 15 minutes
Technical exercise: 10 days to complete
Technical interview: 45 minutes
Reference calls: 2 persons
Final interview with founders: 45 minutes
