We research where AI systems break, turn hard AI topics into clear explainers, and fine-tune and align open language models for businesses and non-profits.
We red-team AI systems and research vulnerabilities in AI products, from model behavior to the infrastructure around it.
We explain dense research and hard AI topics in plain language, as videos and training. We already produce walkthroughs that make new methods understandable to non-specialists, so your team learns what a model does and why.
We fine-tune and align open language models: supervised fine-tuning, preference optimization (DPO and RLAIF), and the data pipeline behind them. You get a model shaped to your domain and values, with the improvement measured, not asserted.
A focused team that does the work itself. We stay close to the current literature and bring methods from research into production.
We work with open weights so you keep control of your model and data. We measure before and after, so an improvement is a number you can check.
A security researcher and engineer with nearly 20 years in the field. His work spans AI and machine-learning security, red-teaming of LLM applications, vulnerability research with several published CVEs, threat intelligence, and systems security. He studied at the University of Toronto.
Our field guide to how AI is built and run, the Careers Building AI series, and recovered security research from IBM X-Force. Plain-language explainers you can read at your own pace.
Open Learn ›Short video series that make dense AI easy to follow, from how a model is built to the business of AI and the jobs behind it. No math wall.
Browse the series ›Our research launchpad: frontier-tech breakthroughs, the next trillion-dollar ideas, the AI-security startup landscape, and live market signals, all in one place.
Open the hub ›Tell us the model, the task, and what "better" looks like for you. We'll tell you whether we can move the number. Reach us on LinkedIn or X.
New explainers most weeks. Follow @zashraf1337 on X to catch them as they post.