In today’s rapidly changing legal world, law firms—especially corporate ones—are starting to recognize the value of Private LLMs (Large Language Models). A Private LLM isn’t just about security and privacy; it’s about having a tailored model filled with your firm’s specific data, from client records to crucial legal documents. By combining a Private LLM with output from a public LLM like ChatGPT, you get the best of both worlds: the expertise and scale of a public AI, while retaining full control over what information gets integrated into your firm’s private model.

But what exactly goes into creating a Private LLM for a law firm? Building something on this scale requires pulling from multiple data sources, like your Document Management System (DMS) and subscription-based legal news services like LexisNexis, Westlaw and Bloomberg Law. The trick, however, is ensuring that the Private LLM isn’t clogged with duplicate or outdated documents, a common issue in most law firm DMS. This is where AI can really shine—helping to clean up that data faster and more accurately than any manual process. AI can scan through thousands of documents, compare versions, flag duplicates, and retain the most up-to-date information. Imagine the efficiency boost your firm could achieve with that kind of cleanup.

So, how do you actually build a Private LLM without breaking the bank, especially if you’re a small or medium-sized firm with limited IT resources? That’s where products like Nvidia’s AI infrastructure come into play. Nvidia offers affordable solutions that allow firms of any size to create and maintain their own LLMs, without needing a huge in-house tech team. These solutions can scale with the firm’s needs, meaning you can start small and grow your model over time, incorporating more data as you see fit. Meta’s LLaMA2, an open source LLM generator is free as well and combined with Databricks, which offers infrastructure support for model servicing, optimization and scalability plus the ability to fine tune the model with your data. LLaMA2 can integrate with existing library subscription services and a DMS.

By leveraging this combination of public and private LLMs, law firms aren’t just keeping up with the future—they’re shaping it. For smaller firms, the opportunity is clear: You don’t need massive IT budgets or resources to harness the power of AI. With tools like Nvidia’s and partnerships with managed services providers like Guardrail Technologies, you can stay ahead of the curve, enhancing both the efficiency and security of your legal operations.

In the end, it’s about building a smarter, more responsive law firm that has the flexibility to meet the ever-growing demands of modern legal practice, without losing control over the data that matters most. The future is here, and Private LLMs are the key to unlocking it.

The business opportunity for servicing small to medium-sized law firms using AI is immense. These firms often lack the IT infrastructure and expertise to fully harness the power of public LLMs like ChatGPT, which could revolutionize how they handle legal tasks. By offering managed services that integrate public LLMs into their existing systems, you provide a streamlined solution for document drafting, legal research, and client communication. For firms that may not have the resources to build out extensive IT departments, this managed service doesn’t just set up the AI; it becomes a long-term partner in their operations, offering continuous support, updates, and monitoring. This allows smaller firms to stay competitive and efficient without taking on the burden of managing complex AI systems themselves.

For large corporate law firms, the opportunity extends even further. Managed services can create Private LLMs that are tailored specifically to their unique data, from client files to proprietary legal resources. By building and maintaining these Private LLMs, service providers offer a comprehensive solution, ensuring that the firm’s data is secured and optimized for AI-driven tasks. On top of this, incorporating “guardrails” that safely integrate public LLM outputs into the firm’s private data adds a layer of flexibility and insight, allowing these firms to benefit from both private and public AI solutions. This hybrid model—where Private LLMs are built, maintained, and integrated with public content under careful control—positions managed service providers as indispensable partners for firms navigating the future of legal technology.

Meta LLaMA 2
Meta has introduced LLaMA 2, an open-source LLM (Large Language Model) that offers an incredible opportunity for law firms and enterprises to build their own private LLMs without heavy financial investments. What’s great about LLaMA 2 is that it’s available for free for both research and commercial use. This makes it especially appealing for smaller to medium-sized firms that may not have the IT infrastructure to create their own LLM from scratch. With models ranging from 7 billion to 70 billion parameters, LLaMA 2 can be fine-tuned to your firm’s specific needs, allowing you to customize it with internal data while keeping full control over how it evolves.

If you’re interested in deploying LLaMA 2, it can be accessed and fine-tuned via platforms like Databricks, which offers infrastructure support for model serving, optimization, and scalability. Databricks provides resources like example notebooks and guides on how to fine-tune the model with your data, which is essential for customizing the LLM to meet specific legal or business needs. You can even integrate LLaMA 2 into your firm’s applications, combining it with your document management system (DMS) and subscription-based legal services for a comprehensive AI-driven solution. This approach allows law firms to leverage both private and public AI models safely, without sacrificing data privacy or security.

For more details and hands-on guides, Meta and Databricks offer several resources to help you get started, including demo notebooks and access to infrastructure support ​(Learn R, Python & Data Science Online)​(AI News HQ)​(Databricks).