A
adaption Tech Stack
Real-time adaptive AI systems that evolve without retraining
Adaption builds AI systems that continuously adapt to changing real-world conditions rather than relying on static models and expensive retraining cycles. The tech stack—vLLM, SGLang, TensorRT-LLM, CUDA, Triton, PyTorch, JAX—reveals a heavy focus on inference optimization and low-latency serving, confirmed by pain points around compute budgets and high-throughput deployment. The organization is research-heavy (23 researchers vs. 34 engineers), suggesting algorithmic innovation is core to the product differentiation.
Tech Stack 19 technologies
Core StackC++ Python React TypeScript PyTorch TensorFlow HubSpot Figma Ashby vLLM SGLang TensorRT-LLM CUDA Triton JAX Discord X Notion LinkedIn Recruiter
What adaption Is Building
◆Challenges
- Real-time adaptation
- Ai systems frozen in place
- Efficiency of adaptable data and algorithms
- Real-time evolution of ai
- Removing friction in developer journey
- Adapting data spaces on-the-fly
- Low-latency high-throughput serving
- Inefficient real-time ai
- Severe compute budgets
- Optimizing data for real-time ai
▲Active Projects
- Explore data adaptation across modalities
- Real-time adaptation algorithmic recipes
- Cross-stack optimization
- Data product implementation
- Adaptable data strategy design
- End-to-end developer documentation
- Feedback-driven algorithm design
- Proof-of-concept integrations
- Adaptive interface design
- Efficient adaptive ml systems deployment
Hiring Activity
Steady80 roles · 25 in 30d
Department
Seniority
Company intelligence
