Where AI Innovation Meets Real Application
We've been deep in the trenches of language model research since 2019, building practical training programs that actually prepare people for this rapidly changing field. No hype, just solid technical education.
Our Journey Through AI Evolution
The Beginning
Started as a research consultancy when GPT-2 was still controversial. We saw the potential but also the gap between academic papers and practical implementation. Our first workshops had 12 participants - mostly computer science PhDs curious about transformer architectures.
Industry Shift
Companies started calling with real projects. We pivoted from pure research to hands-on training, developing our first structured curriculum around fine-tuning and prompt engineering. The demand caught us completely off guard - good problem to have.
Scaling Up
ChatGPT changed everything overnight. Suddenly everyone needed AI literacy. We redesigned our programs to focus on practical applications while maintaining technical depth. Our alumni network grew to over 800 professionals across various industries.
Looking Forward
This year we're focusing on multimodal systems and specialized domain applications. Our September 2025 cohort will be our most comprehensive yet, covering everything from model architecture to deployment at scale.
Meet Our Lead Researcher
Casimir Fawcett has been working with neural language models since before they were cool. He's the kind of person who reads research papers for fun and somehow makes complex topics feel approachable. His background spans computational linguistics, distributed systems, and about a decade of helping technical teams implement AI solutions that actually work.
Before starting AkquiVest, Casimir worked at research labs where he watched countless brilliant ideas never make it past proof-of-concept. That's what drives our approach - we teach the technical foundation but always with an eye toward real-world implementation.
What Sets Our Approach Apart
We've learned a lot about what works and what doesn't in AI education. Here's what makes our programs different from typical online courses or academic theory.
Hands-On Technical Depth
Our curriculum goes beyond surface-level introductions. You'll work with actual model architectures, debug training processes, and handle the messy real-world challenges that come with deploying language models in production environments. We use current tools and frameworks that industry teams rely on daily.
Industry-Connected Learning
Our network includes technical leaders from companies successfully using AI in production. Guest speakers share recent project experiences, common pitfalls, and emerging patterns. You'll learn not just how to build models, but how to integrate them into existing business systems effectively.
Small Cohort Focus
We deliberately keep our programs small - usually 15-20 participants per cohort. This allows for personalized feedback, detailed code reviews, and the kind of in-depth discussions that large courses can't support. Every student gets direct access to instructors and meaningful peer connections.
Real Project Experience
Each program includes a capstone project where you'll work with actual datasets and business requirements. Previous participants have built content generation systems, developed specialized chatbots, and created analysis tools that their employers now use in production.
The projects aren't artificial assignments - they're designed to mirror the complexity and constraints you'll face in professional AI development roles.
Continuous Support Network
Learning doesn't stop when the formal program ends. Our alumni community actively shares resources, discusses new developments, and collaborates on projects. Many participants have found job opportunities through connections made during our programs.
We also host quarterly technical meetups where alumni can present their work, get feedback on current challenges, and stay current with rapidly evolving AI technologies.
Ready to Dive Deep into AI?
Our next comprehensive program starts in September 2025. If you're serious about developing real expertise in large language models, let's talk about whether it's a good fit for your goals.
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