Why Every Company Needs Its Own Private Large Language Model (LLM) - The Time Is Now.

To me, our time and zeitgeist feels eerily like 2006 all over again. That was the year Facebook was born and, while many point to Google as a watershed moment for the world we live in, I am inclined to start in 2006 for a host of reasons. ?Social changed our world more profoundly than the cotton gin or print-press in my opinion. It wasn’t about finding stuff but connecting people and the world and in the process Facebook in essence built the largest most connected nation on Earth, for good, bad or otherwise.

While Verasoni was in its infancy, I took a little more than a year to study the new landscape, players, implications and opportunities to level set my understanding of what's real and what's hype. That year of study has proven to be an excellent investment because it continues to pay hefty dividends for Verasoni 's clients.

Yet here we are again…Fast forward about 20 years and AI is quickly pushing our world in unexpected ways. ChatGpt launched in spectacular fashion in 2022, soon after it unexpectedly got its teeth kicked-in by Deepseek and one can imagine competitors on the horizon who will likely replace both platforms or force them to change much like Google did to Yahoo, Facebook to MySpace and Netflix to Blockbuster. In the race to leverage AI, the question is no longer “whether” companies should adopt LLMs, but “how” they can do so in a way that aligns with their unique needs and goals. For many, the answer lies in building their own. So, like I did back in 2006, I put pen to paper (those do exist you know, and they work quite well!), and I took 2023-24 to study AI, the hype, potential and realities.?

Here are my findings & predictions:

1. Private LLMs & AI Models will Prevail

Why? Public LLMs are trained on vast amounts of publicly available data, but they are not designed to handle sensitive or proprietary information securely. When companies input confidential data—such as customer information, trade secrets, internal strategies, or even marketing brochures—into a public LLM, they risk exposing that data to third parties. Many public LLMs retain user inputs to improve their models, which could lead to unintended data leaks or breaches. That means your business is consenting to giving public LLMs to use your information as they please.

Much like every company having its own website or network, every company will have its private LLM and or AI agent(s). And much like websites and the digital brand experience are attached at the hip, AI when done right will attract clients and make them stickier. For industries like healthcare, finance, banking, and legal services, where data privacy is paramount, relying on public LLMs is a non-starter. Other industries that rely on sales and service will see exponential demand for AI from the market because proprietary LLM, on the other hand, can be tailored to operate within a company’s secure infrastructure, ensuring that sensitive data never leaves the organization’s control.

2. Customization for Industry-Specific Needs

Public LLMs are general-purpose tools designed to cater to a wide range of users and industries. While they are versatile, they often lack the depth and specificity required for niche industries or specialized tasks. For example, a pharmaceutical company may need an LLM that understands complex medical terminology, regulatory requirements, and drug development processes. A public LLM may struggle to provide accurate, context-aware responses in such scenarios. By developing its own LLM, a company can train the model on industry-specific datasets, ensuring that it delivers highly relevant and accurate outputs. This level of customization is impossible with off-the-shelf public LLMs. For example AI will be integrated heavily into sales, especially on the B2B side putting customers more in control than ever before because customers will create buying agents who can find, source and negotiate for products faster and more efficiently than ever.

3. Intellectual Property and Competitive Advantage

We live in a knowledge-driven and attention economy where intellectual property (IP) is a competitive advantage. Public LLMs are trained on publicly available data, meaning they are limited because they cannot provide insights or solutions that are truly unique to a company. Moreover, using a public LLM to generate content or strategies could inadvertently expose a company’s IP to competitors. A proprietary AI, trained on a company’s internal data for its stated purposes, can generate insights and solutions that are unique to the organization. This not only protects IP but also enables the company to leverage its proprietary knowledge to innovate and stay ahead of the competition.

4. Control Over Model Behavior and Outputs

Public LLMs are designed to be neutral and broadly applicable, which means they may not align with a company’s specific values, tone, or brand voice. For instance, a company with a strict compliance framework may need an LLM that adheres to specific guidelines and avoids generating content that could lead to legal or reputational risks. With a proprietary LLM, companies have full control over the model’s training data, fine-tuning, and outputs. This ensures that the model behaves in a way that aligns with the company’s goals, values, and regulatory requirements.

5. Avoiding Vendor Lock-In & Dependency

Relying on public LLMs ties a company to the policies, pricing, and availability of third-party providers. If a public LLM service changes its terms, increases costs, or experiences downtime, the company’s operations could be disrupted. Additionally, public LLMs may not always be available in regions with strict data sovereignty laws. By developing its own LLM ecosystem, businesses can reduce its dependency on external providers and maintain full control over its AI infrastructure. This not only ensures continuity but also provides greater flexibility to adapt to changing business needs.

6. Ethical and Regulatory Compliance

As AI regulations evolve, companies are increasingly required to ensure that their AI systems are transparent, ethical, and compliant with local laws. Public LLMs, which are often opaque in their training data and decision-making processes, may not meet these requirements. China is leading the way here; the US and Europe are laggards. A proprietary LLM allows companies to implement ethical AI practices, such as bias mitigation, explainability, and compliance with regulations like GDPR or CCPA. This level of control is essential for building trust with customers and stakeholders.

7. Long-Term Cost Efficiency

While developing a proprietary LLM requires an initial investment, it can be more cost-effective in the long run. Public LLMs often operate on a pay-per-use model, which can become expensive as usage scales. Additionally, companies may need to spend extra on fine-tuning or integrating public LLMs into their workflows. A proprietary LLM, once developed, can be optimized for the company’s specific needs, reducing ongoing costs and providing a higher return on investment over time.

What else?

History has never been more clear on winners who have embraced technology in a meaningful way and losers who didn’t or were late to the game. For companies that operate in specialized industries, or seek to maintain a competitive edge, developing a proprietary LLM is not just an option—it’s a necessity and reality. A proprietary LLM offers unparalleled advantages in terms of data security, customization, IP protection, and regulatory compliance. It empowers companies to harness the full potential of AI while maintaining control over their most valuable assets.

More to come, but for now, I hope this piece was helpful.