Public LLMs were never designed to protect your competitive advantage—yet every prompt your team types into them can quietly become part of someone else’s edge. Medullar gives you the same AI power inside a private environment where your knowledge stays yours, your permissions stay intact, and your competitors stay in the dark.

Every time someone on your team pastes a client deck, contract clause, or roadmap into a public chatbot, they are doing more than “getting help from AI.” They may be donating hard‑won intellectual property, trade secrets, and relationship context into a system they don’t control—and can’t audit or unlearn.

Public LLMs are built to learn from data at scale. That’s their superpower, and your risk. When your prompts and uploads are used to improve the model, your proprietary insight can be blended into the very same system your competitors query for “market trends,” “best‑practice pricing,” or “winning proposal structure.”

In other words: your “private” work can resurface as someone else’s AI‑assisted answer.

Public chatbots optimize for general usefulness, not for your confidentiality, regulatory exposure, or long‑term strategy. That misalignment shows up in a few critical ways:

• Your data may be used for training - Many public LLM providers reserve the right to use user interactions to improve models, which can include prompts, files, and follow‑up instructions. Even with opt‑out options, the default behavior and fine print are easy to miss at scale.
• Once it’s in, it’s hard to get out - LLMs cannot easily “forget” individual data points, which clashes with requirements like GDPR’s right to erasure and internal data‑retention policies. That means a single overshared document can remain statistically embedded in the model long after you’d prefer it gone.
• Models don’t understand your boundaries - Public LLMs don’t know which parts of your prompt are sensitive, which are client‑confidential, or which are internal‑only. They treat everything as a potential signal for better predictions.
• Competitors can benefit from your signals - When fine‑tuning or ongoing training draws on user traffic across many organizations, one company’s proprietary patterns can inform another company’s “AI‑generated” strategies and language.

The net effect: you’re quietly training a shared brain that your competitors can also tap—without sharing any of their own data back with you.

Most enterprises have already tried the obvious fix: send a security memo telling employees not to put confidential data into public tools. But in practice, that’s nearly impossible to enforce.​

Knowledge work today is messy. Sales teams want faster proposals. Legal wants help drafting language. Consultants need to summarize voice‑of‑customer interviews, synthesize research, and pressure‑test narratives. When the “fastest answer” sits one browser tab away, people will use it—especially under deadline.

Relying on individual judgment for every copy‑paste is not a security strategy. It’s a hope. And as AI usage grows, so does the surface area for accidental leakage and compliance violations.

You don’t solve that with more policies. You solve it by giving people a safer, better alternative that feels just as powerful.

Medullar is an AI‑powered productivity platform built for organizations that want the benefits of generative AI without handing their crown jewels to a public model. Instead of pushing your data into someone else’s training pipeline, Medullar brings AI to your existing systems, on your terms.

Here’s what that means in practice:

• Private by design - Medullar runs on enterprise‑grade infrastructure and uses encryption to secure your knowledge at rest and in transit. Your data stays in your connected systems; Medullar’s federated and universal search query across sources without ripping and replicating everything into a separate data lake.
• Your data is never used to train public models - Medullar does not send your content to public models for training or reuse. You can choose which LLMs to use for tasks (e.g., ChatGPT, Claude, Llama), but Medullar mediates those interactions with strict controls and does not allow your proprietary data to become part of a shared public corpus.
• Built around your access controls - Medullar respects the permissions you already have in tools like Google Workspace, Microsoft 365, and other connected apps. If a user couldn’t see a file in the source system, they won’t see it in Medullar—whether they’re searching, asking natural‑language questions, or generating new content.
• AI that understands your work, not the entire internet- By combining your internal content with curated external web data, Medullar gives teams context‑aware answers that reflect how your firm talks, sells, and serves clients. Instead of generic text, you get output grounded in your actual knowledge base.

The result: you get the speed and creativity of modern LLMs inside a secure, governed environment instead of a public black box.

Medullar Spaces turn scattered documents, chats, and links into shared, AI‑ready knowledge environments where each answer is shaped by who’s asking and what they’re allowed to see.

Within a Space, teams can:

• Upload and organize high‑value content - Bring in contracts, pitch decks, project archives, research, email threads, and more—without moving the source of truth out of your systems. Medullar indexes what it needs to answer questions while keeping data anchored to original repositories.
• Ask questions in natural language - “What were the key risks in our last three SOWs with this client?” or “Summarize our playbook for mid‑market renewals” become instant queries, not hours of manual digging. AI‑generated summaries and syntheses pull from just the content that a given user has permission to access.
• Co‑create with clients in controlled Spaces - Invite clients into dedicated Spaces where they can safely collaborate on documents, plans, or roadmaps without exposing the rest of your environment. Every answer respects role‑based access, so internal notes stay internal while client‑facing materials remain clean and on‑brand.​
• Capture and reuse institutional knowledge - As teams work, Medullar helps you curate and refine the content you trust—turning tribal knowledge into reusable assets instead of leaving it buried in old threads and folders.

Think of Spaces as your private vaults of context, insight, and history—continuously augmented by AI, but never surrendered to a public model.

Public AI tools gave everyone a taste of what’s possible, but they were never built for the realities of enterprise confidentiality, compliance, and client trust. If your teams are still pasting sensitive work into public chatbots, you’re not just accelerating their output—you’re subsidizing a shared model your competitors can tap.

Medullar lets you flip that equation. You keep the speed, creativity, and automation of modern LLMs, but you apply them to a secured layer of your own knowledge, guarded by your existing controls and never repurposed as someone else’s training data.

If you’re ready to stop training the competition and start building a durable AI advantage, it’s time to bring your work into Medullar.