Palmyra vs. GPT vs. Claude: Exploring Enterprise LLMs

Large language models (LLMs) have truly remodeled all areas of business. They have made customer service more efficient, data collection has become easier, and some routine operations and processes have almost completely switched to automation. All these are great achievements. However, most enterprises still don’t understand how to use LLMs correctly and what potential they actually hide. A much larger number of tasks can benefit from using AI. And that is why AI companies are creating not just large language models, but enterprise LLMs that specialize in helping businesses.

But even here, everything is not so simple. If you decide to use an enterprise solution, you need to make the right choice. There are now quite a lot of competitors in this business segment, so some companies may end up lost when choosing. Today, we will compare the most popular of their available enterprise LLMs and help you find out which one is best suited for your problem. Read on!

What is an enterprise LLM?

Since defining the subject is the first step in every discussion, we’ll start with that. An enterprise LLM is a language model built or adapted specifically for use within organizations rather than for a general audience. These models meet the unique needs of businesses, especially when it comes to:

• Data privacy and security: Enterprise LLMs are built to handle sensitive data with strict safeguards.

• Customization: They can be fine-tuned/prompt-engineered to understand a company’s internal jargon, processes, or tone.

• Scalability a

  nd integration: Enterprise LLMs are not afraid of high-volume usage and integrations across many tools and teams.

• Compliance: They meet industry standards like SOC 2, HIPAA, GDPR, and ISO 27001.

• Support: Vendors typically offer dedicated customer support for integrating the model into your business processes.

Many business areas are using these solutions. Medicine, finance, education, commerce, you name it. These industries have long accepted AI as a tool in their work and successfully use it to accomplish their tasks and improve their metrics. For example, there are already health-LLMs that can achieve more than an 80% accuracy in diagnosing diseases.

Benefits of enterprise LLMs

Enterprise LLMs would not exist if they didn’t have a set of benefits that are attractive to companies of all sizes. The most useful and obvious include:

Source: Market.us

• Next-level data security: You can deploy an enterprise LLM in secure environments (on-premises or in private clouds) to make sure all business data is protected from leaks and usage for external model training.

• Domain-specific knowledge: If your business uses specific terminology, applies custom processes, and has a unique way of dealing with paperwork, you can fine-tune the model to match the tasks, whether it’s legal language, financial reports, or internal product specs.

• Workflow automation: A lot of workflows in modern business fall into the category of routine and repetitive. Examples can i

  nclude drafting emails, summarizing documents, or even writing code. LLMs can automate all of them across departments to reduce the load on your employees and free their time for more relevant assignments.

• Seamless integration: Enterprise-grade models usually come equipped with APIs, SDKs, and connectors, so the integration into your CRMs and ERPs will go as smoothly as possible.

• Data-based decision making: They can analyze internal data, generate accurate summaries, and spot trends or anomalies for more informed executive decisions.

• Better team collaboration: Integrations with tools like Slack, Notion, or Teams help LLMs provide instant answers to all the questions your team has, so their communication will be more productive.

Besides the advantages we named above, there are some less obvious benefits your business can get. For example, LLMs can help you generate content (emails, FAQs, policy docs) that perfectly matches your brand voice. Or, with some level of control, LLMs can be used by non-technical staff to prototype ideas without engineering bottlenecks.

Use cases of enterprise LLMs

Now, let’s talk briefly about where exactly enterprise LLMs can come in handy. Here are some of the most prominent use cases depending on the industry you work in:

Healthcare: Clinical notes summarization

LLMs can help doctors and nurses summarize their notes and patient records into standardized formats. It will enable easier diagnostics and billing, and give healthcare professionals more time to actually treat their patients. Also, AI can generate discharge summaries and follow-up instructions more efficiently.

Logistics: Intelligent routing instructions

LLMs will give you more control over the routes of your fleet. With the help of real-time traffic, weather, and delivery data, AI can generate instructions so the drivers can avoid route accidents or congestion areas. It will result in faster deliveries and higher customer satisfaction.

Education: Personalized learning support

Every student is different, and not all of them can keep up with the material at an equal pace. An LLM can adapt explanations to each student’s level and suggest supplemental resources so they can get all the knowledge they need. Also, people already use e-learning AI assistants that fully simulate tutoring sessions without minimal human intervention.

Finance: Compliance monitoring and report drafting

You can save hours of manual labour that are usually spent on compliance reports review. AI can analyze changes in laws and regulations and draft tailored reports based on them. It can also assist fraud analysts by pinpointing suspicious transactions and providing relevant legal context.

Retail: Product description generation at scale

If your business is operating in retail and e-commerce, you definitely need SEO-optimized and brand-aligned product descriptions for your catalogs. And if you manage a huge store, you will need a lot of these descriptions. An LLM can help you with that and write a huge volume of descriptions in minutes. Even in multiple languages.

Some practical and high-impact use cases can be applied to any industry. Document processing, customer support chatbots, internal search, predictive analytics, content creation—any business from any industry can use LLMs for tasks like these.

Which enterprise LLM to use

Finally, the main question—if you decide to implement an enterprise LLM for whatever task you deem fair, which one should you use? Let’s take a look at the most powerful and popular solutions so you can find what you are looking for.

Palmyra

Palmyra is a family of enterprise-grade LLMs developed by Writer, a company that specialises in gen AI solutions for business purposes. These models offer advanced capabilities in natural language processing (NLP), reasoning, and multimodal understanding.

Source: Writer

Writer introduced the Palmyra LLMs as a solution to the growing demand for secure and customizable enterprise-level AI tools. The Palmyra family includes models tailored for different use cases (Palmyra X5, Palmyra Fin, Palmyra Med, and Palmyra Creative). They are built to integrate into business workflows without struggles and provide organizations with a smart way to handle complex tasks.

Their core capabilities include:

• Long-context processing: Palmyra X5 supports a context window of up to 1 million tokens. 

• Advanced reasoning: The models have adaptive reasoning abilities for complex decision-making and sequence planning tasks. 

• Multimodal input: Palmyra models can handle several input types, like text and images.

• Tool calling and integration: They can interact with external systems through API calls, so you can integrate them with tools you already use.

• Multilingual support: These models can understand 30 languages, which makes them suitable for global enterprises. 

• Domain-specific models: Variants like Palmyra Fin and Palmyra Med are offering specialized knowledge and compliance with industry standards in finance and healthcare, respectively.

The Palmyra models are perfect for enterprise content generation, customer support automation, and data analysis. And their integration into platforms like Amazon Bedrock is only making them more accessible for enterprises that want to level up their operations. 

GPT

OpenAI's GPT (Generative Pre-trained Transformer) models are a series of LLMs that are designed to understand and generate human-like text. Right now, the latest iteration of this family is GPT-4.1. Businesses that want to integrate enterprise-grade GPT into their processes can access these solutions through platforms like ChatGPT Enterprise and the OpenAI API. These platforms give companies top-notch security, privacy, and administrative features.

Source: OpenAI

The GPT’s main features are:

• Exceptional language understanding: These models are among the best in completing almost all language-related tasks.

• Multimodal processing: GPT-4o can process and create text, audio, and visual outputs in real time.

• Customization: You can fine-tune GPT models with the help of proprietary data to make the model more relevant to your needs.

• Security: ChatGPT Enterprise offers data encryption, SOC 2 Type 2 compliance, and control over data retention.

The GPT models will be most efficient in customer support automation, content creation, internal knowledge management, and multilingual translation.

Claude

Claude is a family of LLMs built by Anthropic, an AI research company founded by former OpenAI researchers. These models were developed with a focus on their safety and usability. Anthropic introduced Claude in March 2023 as a chance to create helpful, honest, and harmless AI systems. The Claude 3 family, the most modern one, includes three models:

• Claude 3 Haiku (fast)

• Claude 3 Sonnet (balanced)

• Claude 3 Opus (complex)

For business needs, Anthropic launched Claude Enterprise with features like a 500,000-token context window and a GitHub integration.

Source: Anthropic

Here’s the list of these models’ main features:

• Advanced reasoning: Claude perfectly handles cognitive tasks, including mathematical problem-solving, logical reasoning, and multi-step workflows.

• Vision analysis: The models can decipher and analyze handwritten notes, graphs, and photographs for better data interpretation.

• Code generation: Claude can give your team a hand in creating websites in HTML and CSS, converting images into structured JSON data, and debugging complex codebases.

• Extended context handling: Claude Enterprise supports a 500,000-token context window for in-depth comprehension.

Besides content creation and customer support, Claude can be used for legal documents summarization, software development assistance, and enterprise collaboration. And it’s also integrated into Amazon Bedrock, just like Palmyra.

Summary: Side-by-side comparison

Here’s a final comparison between these three enterprise LLM solutions that will help you make your final decision.

Which one should your business use?

Each of these enterprise LLM solutions offers unique and undeniable strengths:

• Palmyra excels in long-context processing and offers domain-specific models.

• ChatGPT Enterprise provides better language understanding with multimodal features and customization options.

• Claude Enterprise is famous for its large context handling, excellent reasoning, and strong data privacy and security focus.

Your final choice should be based on your tasks, your need for native integrations, and your industry’s requirements (and don’t forget your budget).

To sum up

Using enterprise LLM solutions is a sure step towards the evolution of your business. They will help with automation processes and free up your team's efforts for more important business development. Whatever model you ultimately choose, if used correctly, it will upgrade your business and become a good assistant for your team.