Use Case: Legal
Use Case: Legal
Wildcat provides a solid, secure entry point for small teams dipping into AI-augmented workflows.
Panther delivers a more powerful, private solution ideal for firms juggling active cases with strict data sensitivity.
Grizzly ensures maximum control, privacy, and performance for those working in high-stakes, high-risk legal environments.
Use Case: Legal
Legal professionals across all specialties—litigation, corporate law, family law, intellectual property, compliance, and more—share a common foundation: the ethical and confidential handling of sensitive client data. Whether you’re drafting motions, reviewing contracts, or preparing for trial, Modular’s AI Workspaces offer a secure and efficient way to integrate AI into your legal workflow.
Wildcat offers a secure, cost-effective entry point for small legal teams beginning their journey into AI-augmented processes.
Panther delivers a more robust, isolated solution ideal for firms managing multiple clients and high-volume caseloads with heightened security expectations.
Grizzly ensures total control and the highest level of performance for firms working in high-stakes or regulated legal environments that demand strict data sovereignty.
Expanded Use Case: Law Firm – Secure Automation for Legal Drafting, Research, and Case Management
A boutique law firm with a team of attorneys and paralegals chooses the Modular AI Workspace suite to streamline operations across civil litigation, estate planning, regulatory filings, and contract review. With a growing workload and increasing sensitivity of digital records—ranging from client agreements and discovery data to confidential advisories—the firm needed a secure, scalable AI solution that upholds professional ethics and data integrity.
Depending on their needs and comfort level with AI integration, the firm can select from three tailored Modular solutions:
Wildcat – Entry-Level Secure AI Workspace
Best for: Firms exploring AI with moderate privacy needs and budget-conscious priorities.
Deployment: Shared but securely isolated environment, hosted within Modular’s U.S.-based, FedRAMP-certified data center.
Security Features: Containerized infrastructure with network segmentation and encryption at rest.
Data Use: All data remains entirely within Modular infrastructure—never shared with third-party cloud providers.
Use Case Fit: Teams use Wildcat to draft documents, generate summaries, interpret case law, and conduct internal research using anonymized and redacted examples.
Limitations: Shared computing resources may lead to minor latency during high-traffic periods.
Ideal for: Small firms or solo practitioners wanting to explore secure AI without major upfront investment.
Panther – Mid-Tier Private AI Workspace
Best for: Firms handling active casework or client matters requiring enhanced confidentiality.
Deployment: Dedicated front-end and backend hosted privately on Modular infrastructure.
Security Features: Isolated storage, encrypted document vaults, and optional role-based access controls.
Data Use: Sensitive case materials—such as client correspondence, contracts, or regulatory filings—are stored securely in private repositories.
Use Case Fit: Panther empowers teams to automate citation retrieval, analyze case histories, and draft specialized documents using firm-specific templates and data.
Performance: Responsive, consistent performance even during peak hours.
Ideal for: Mid-sized firms seeking a private, high-performance AI assistant to enhance productivity while safeguarding client data.
Grizzly – High-Security, Fully Dedicated AI Deployment
Best for: Firms dealing with highly sensitive or high-profile matters, or working in heavily regulated sectors.
Deployment: Fully isolated hardware, hosted in Modular’s secure data center or deployed on-premises for maximum control.
Security Features: Hardware-level isolation, encrypted backups, custom firewall configurations, and optional air-gapped installation.
Data Use: Absolute data sovereignty—no shared resources, with full isolation of AI queries, document storage, and user access.
Use Case Fit: Ideal for firms maintaining proprietary databases, building persistent workflows, or integrating internal legal research tools across jurisdictions.
Scalability: Designed to support large teams, multi-matter workspaces, and long-term archives.
Ideal for: Legal teams requiring zero-trust architecture, advanced AI customization, and full control over infrastructure.
Ready to explore how a private AI Workspace can transform your team’s workflow?
Contact us today for a free consultation—we’ll help you find the right solution based on your goals, security needs, and budget.
Email us at sales@modtechgroup.com or call 888-723-4508 to get started.
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.
