HOW WE WORK
OUR PROCESS
DISCOVER
With a focus on the highest ROI use cases, we dive into your organization, goals, and data and help you de/refine the problem.
DEVELOP
We leverage and protect your IP by customizing open-source solutions, developing APIs to unify data for your AI and LLM solutions.
DEPLOY YOUR AI
We collaborate with your team to deploy models into production, ensuring ongoing maintenance is efficient, cost-effective, and minimally disruptive to operations.
EXPERTISE YOU CAN COUNT ON
YOUR DELIVERY TEAM
Batman has Robin. Sherlock Holmes has Dr. Watson. You have Rotational.
Our mission is to help organizations build and deploy new enterprise capabilities with generative AI and machine learning. We help companies leverage their domain expertise and data to build custom LLMs, applications, and AI systems for business impact with the goal of maximizing ROI, controlling costs, and prioritizing trust.
- +25
- SATISFIED CLIENTS
- 50 YRS
- COLLECTIVE EXPERIENCE
- 5
- INDUSTRY VERTICALS
OUR PHILOSOPHY
HUMAN-IN-THE-LOOP
We believe AI should enhance human workflows by automating repetitive, routine tasks, especially those prone to inconsistent outcomes. This allows humans to focus on high-value creative work. Our AI solutions prioritize trust and explainability, ensuring transparency and reliability in decision-making processes.
CLIENT FEEDBACK
TESTIMONIALS
Rotational is committed to customer success. We work side-by-side with our clients to deliver on their most important priorities. We strive to “meet clients where they are”. We build for business impact.
OUR PRIORITY
PRIVACY & SECURITY
Rotational is deeply committed to privacy and security. AI offers tremendous potential benefit to humanity, provided it is used properly, and enterprises commit to being good data stewards. To that end, we have committed to the US-EU Data Privacy Framework (DPF) and prioritize privacy and security in all our product and services work.
Thought Leadership
Recent Rotations
Recapping PyTorch: Key Takeaways from the 2024 Conference
I spent last week in San Francisco meeting up with the Rotational team to attend PyTorch Conference. If you’re an LLM developer and didn’t make it this year, here are some of my key highlights and takeaways.
Teaching LLMs With Continuous Human Feedback
If you’ve worked with generative AI models you know they can be fickle and sometimes fail to meet the expectations of users. How can we move towards models users trust and see clear value in? Let’s engineer a user-feedback loop!
Data Access Controls for Generative AI
Anyone who has worked on a POSIX system is used to fine-grained data access rights for files and directories. In a world of generative AI, do we need a new permissions model for files used to train LLMs and serve live inferences?
