Imagine a world where your developers can focus on innovation, not repetitive tasks. With Dell AI Code Assistant, that world is here.
Introduced earlier this year and demonstrated in action at Dell Technologies World, it’s a fully on-premises solution validated on Dell AI Factory infrastructure helping customers deliver secure and automated development workflows in real-world scenarios.
In addition, you can now try it out for yourself and explore key use cases like code creation, debugging, testing, refactoring, and documentation in the hands-on lab through the DemoCenter catalog (just a few of the supported use cases).
Why On-Premises Matters
Keeping your code, data, and workflows within your private environment delivers the control and security needed to protect your most valuable assets, including source code. With no data leaving your ecosystem, this ensures complete compliance and reduces risks.
A recent cost analysis further tips the scale toward on-premises deployment. According to an Enterprise Strategy Group study comparing an on-premises deployment on Dell AI Factory with public cloud options, the analysis found that running workloads on-premises was up to 2.6x more cost-effective at supporting inferencing than the public cloud (over a four-year period).¹
But security and cost savings are just the beginning. Let’s explore how this solution simplifies software development and empowers your team to boost productivity and reduce bottlenecks.
Simplifying Software Development
This isn’t just forward-looking innovation. It’s a solution available today, designed to solve the challenges of software engineering that often strain teams, such as onboarding new developers that require extensive time and guidance or manual code reviews that monopolize senior developers’ time.
Built together with Tabnine’s Enterprise platform, it’s purpose-built to integrate smoothly with your existing organizational tools and development workflows. Designed as a modular and flexible architecture, the solution supports most Integrated Development Environments (IDEs) and is compatible with the majority of coding languages and Large Language Models (LLMs).
How Dell AI Code Assistant Enhances Secure Development Workflows
AI Agents represent the next leap forward for automating workflows and starting to solve many of these software development challenges. This solution features a comprehensive suite of AI agents purpose-built to help automate critical workflows across every step of the software development life cycle. From onboarding a new developer or a developer onboarding into a new project (planning phase) to creating and testing the code (finishing the project).
- Understand and Explore Codebases: AI-driven insights help onboard new developers faster by analyzing code structures and identifying key best practices.
- Automate Code Creation: Generate clean, formatted, and tailored code directly from project tickets, such as those in Jira.
- Accelerate Testing and Debugging: Automate test plans and error identification to get code production-ready quickly.
- Streamline Code Reviews and Refactoring: Save time during code review and simplify refactoring outdated or complex codebases.
By empowering developers in each of these areas, they are spending less time on monotonous tasks and more time driving creativity and innovation.
Ready To Revolutionize Your Development Process?
This is more than just a tool; it’s the future of development, today. Easily get started on your journey to empower your developers with a validated solution that accelerates productivity and integrates effortlessly into your existing tools.
In addition to the virtual lab demo, we also offer deeper technology dives or pilot programs through our Customer Solution Centers – so you can learn how to quickly unlock value with minimal disruptions for your organization.
Contact a Dell Technologies representative or visit Dell.com/AI to learn more!
1 Based on Enterprise Strategy Group research commissioned by Dell, comparing on-premises Dell infrastructure versus native public cloud infrastructure as a service, April, 2025. Analyzed models show a 70B parameter LLM leveraging RAG for 5k users being up to 52% more cost effective than public cloud and 65% more cost effective than API-based services over four years and a 70B parameter LLM leveraging RAG for an organization of 50k users being up to 62% more cost effective than public cloud and 75% more cost effective than API-based services over four years. Actual results may vary.
