Panther
Panther
Panther — Private, High-Performance AI Workspaces
Take control of your AI tools with a dedicated, secure environment powered by Modular.
Overview
Panther is Modular’s mid-tier AI Workspace solution, perfect for teams or organizations that need enhanced privacy, stronger performance, and reliable AI tools tailored to their unique workflows. Unlike shared AI platforms, Panther gives you a dedicated environment—with isolated front-end and back-end infrastructure, custom document storage, and high-speed model access—all hosted within Modular’s secure U.S.-based data centers.
Built for professionals, research teams, legal firms, and privacy-conscious businesses, Panther delivers enterprise-level performance without enterprise-level complexity.
Use Cases
- Legal teams analyzing case files and drafting motions
- Research teams automating literature reviews and content analysis
- Marketing departments creating content at scale—securely
- Internal operations teams building custom knowledge chatbots
- Small to mid-sized businesses needing fast, private AI access
Why Choose Modular?
Privacy First
Modular is committed to zero data sharing. Your models, documents, and conversations are never sent to third parties. Panther’s environment ensures your private data stays fully under your control and encrypted at rest.
Security That Scales
Panther is built with dedicated file vaults, enhanced encryption, role-based access controls, and optional VPN security layers. Whether you’re handling sensitive legal documents or proprietary product plans, Panther keeps your data secured and separated.
Sustainable Infrastructure
Modular takes pride in offering AI solutions that are both powerful and environmentally responsible. By using energy-efficient hardware and containerized deployments, Panther delivers high-speed performance with a low carbon footprint—supporting your sustainability goals.
Ready to Explore?
Interested in how Panther can transform your team’s productivity while keeping your data privat
Contact us today for a free consultation and customized proposal.
Panther — Because privacy, performance, and purpose-driven AI shouldn’t be out of reach.
FAQ
Imagine you’re telling a story with a friend who helps decide what happens next. The “temperature” setting controls how *wild or predictable* your friend’s ideas are!
Low Temperature (like 0.1):
Your friend only suggests things that are super obvious or safe, like “then the hero wins easily.” It’s boring but makes total sense—no surprises! This is the temperature you would use for scientific data or any kind of research that should be only fact-based and unbiased.
Medium Temperature (like 0.7):
Your friend balances ideas—they might say, “the hero uses a cool trick to win!” It’s creative but still logical. This is what most people use because it’s just right.
High Temperature (like 1.5+):
Your friend starts getting *weirdly creative*—they might suggest the hero fights a giant marshmallow or suddenly turns into a cat. The story becomes fun but might not make much sense anymore!
Think of temperature like a volume knob for creativity: turn it down for safety, crank it up for crazy ideas (but expect some nonsense). Teachers or game designers use this to decide how “silly” or “smart” an AI should act!
A Large Language Model (LLM) is a powerful type of artificial intelligence system designed to understand and generate human language. It’s made up of a vast number of interconnected virtual “neurons” that can process and generate text. LLMs like GPT-3, for example, can handle a wide range of natural language tasks, such as answering questions, translating languages, writing articles, and even simulating human-like conversations. These models are trained on massive datasets, allowing them to learn patterns and nuances in language, making them valuable tools for various applications in fields like natural language processing, machine learning, and text generation.
Most public language models are trained on vast amounts of data, which is “scraped” from all the data that is fed into the model.
Every time you send something to OpenAI’s ChatGPT, for instance, OpenAI uses your data to “train” its model. The intent is to provide a broader knowledge base, which may be more useful for most common interactions with ChatGPT.
For some uses, a more specific language model may be needed, which is trained on a narrower set of data. E.g., a law office may choose to train its own language model on the kind of data it primarily works with. This will improve the responses that come from the language model, since it reduces the risk of getting unrelated information. Yes, even language models can get confused, or don’t understand the specific context of a question. Training your own language model can help.
The terms “local language model” and “OpenAI” refer to different aspects of language models, so let’s clarify their meanings:
- Local Language Model: This term typically refers to a language model that operates on a local or on-premises system, meaning it runs on your own computer or server, rather than relying on external cloud-based services. Local language models can be fine-tuned and customized to suit specific needs or security requirements, and they might be used for various tasks, such as text generation, translation, or chatbots. They are often used when data privacy and control are paramount.
- OpenAI: OpenAI is an artificial intelligence research organization that has developed various language models, including GPT-3 (and possibly newer versions as of my last knowledge update in January 2022). OpenAI’s models are known for their general language understanding and text generation capabilities. OpenAI provides access to its models through APIs, allowing developers to integrate their models into applications and services.
So, the key difference is that “OpenAI” refers to the organization developing the language models, while “local language model” refers to where and how the model is deployed and used. You can use an OpenAI model both locally and through cloud-based services, depending on your needs and preferences.
AI agents are self-contained bodies of information. They can be used to provide context to Large Language Models (LLMs) and answer questions on a particular topic.
LLMs are trained on a large variety of data. However, to answer a question on a specific topic, or to draw conclusions from a specific topic, they need to be supplied with the context of that topic.
AI agents are an intuitive way to provide that context.
Selecting a agent provides the context of that agent to the LLM. This lets users build agents for specific topics, and then use them to answer questions about that topic, without being “polluted” by information that is not in the agent. This prevents “hallucinations” and answers that are out-of-context.
Large language models (LLMs) are trained on a lot of random information. This makes them great at generating general content. However, very specific information in LLMs may be out-of-date or not exist at all.
Retrieval-augmented generation (RAG) fills in this gap. Instead of trying to piece together a response based on all the information the LLM was trained with, the LLM can now “ask” a specific dataset that knows the up-to-date and topical information.
A WildcatGPT AI agent, or “brain” is such a dataset. You feed it all the specific information you are interested in. The LLM can then find the information in the RAG data, and return meaningful answers from it. The responses will be as up-to-date as the data you have fed the brain, and it can provide the sources, in order to support the validity of the responses.
Generative AI, also known as GenAI, lets users input different prompts to create new content like images, text, videos, sounds, code, and 3D designs. It learns from existing online documents and items. It gets smarter as it trains on more data. It uses AI models and algorithms trained on large, unlabeled datasets, which need complex math and a lot of computing power to build. These datasets teach the AI to predict outcomes similarly to how humans might act or create.
When data privacy and control are paramount, our hosted AI workspace offers a tailored solution. With an LLM, such as Ollama, you can ensure the confidentiality of proprietary information, since the information is only processed locally by the LLM. It is not sent to OpenAI or other outside services for processing, so there is no possibility of your data being used to train someone else’s AI.
However, if you would like to use OpenAI, that is an option, as well.
Large language models (LLMs) are a category of foundation models trained on immense amounts of data. This makes LLMs capable of understanding and generating natural language and other types of content, to perform a wide range of tasks. LLMs can run on a server locally, in order to keep your private data private.
