Your Data.

Your Data.

Your Data.

Our Fortress.

Our Fortress.

Our Fortress.

Enterprise AI isn't just about what the model can doโ€”it's about where your proprietary knowledge stays. We built Denada to be a walled garden for your team's collective intelligence.


Denada is built for Corporate Enterprise Users. Here's how we keep your AI interactions and data, walled and secured.

01 โ€ข TECHNICAL BACKGROUND

Inference vs. Training

Inference vs. Training

Inference vs. Training

There are two key areas to understand when discussing AI and LLM security:


โ€ข Pre-training โ€” when the model learns language, patterns, and relationships

โ€ข Inference โ€” when the model responds to your prompts in real time


During pre-training, LLMs are trained on massive amounts of public data, generating billions of statistical โ€œweightsโ€ that help the model understand relationships between words and concepts. These weights allow the AI to associate ideas โ€” for example, connecting โ€œQueenโ€ with โ€œKing,โ€ or โ€œKingโ€ with โ€œLeBron.โ€ In simple terms, the model learns patterns and relationships across language.


When you interact with an LLM during inference, you are sending data to the model and receiving responses based on those learned relationships. This interaction is temporary โ€” the model does not โ€œlearnโ€ from your conversation in real time like a human would. Persistent learning only occurs if a provider stores interaction logs and later uses them to retrain the model.


That distinction matters. Denadaโ€™s enterprise agreements are designed to prevent customer data from being retained or used for future model training.

The Public Risk

RLHF Feedback Loops

On public LLMs, if your team adds proprietary data like โ€œCompany Product Launch Eventโ€, the model links those words together, creating a chance the LLM will respond with your product launch details to anyone who queries your company name.

Your inference data is fed back into the global training set, updating weights for the world.

The Denada Method

Zero-Retention Inference

Your data enters our secure enclave, retrieves context from your private Vector Database (RAG), and generates an answer. It never touches the global weights.

Fortunately Denada still learns; but the "learning" is isolated strictly to your private retrieval index, making Denada better for your team only.

02 โ€ข THE LEAK

The Leaky Bucket

If anyone on your team uses public models (they do), they are currently engaging with a public cloud. Every proprietary snippet is "fuel." But to make matters worse, data is siloed per employee. No one else benefits, and if that employee leaves, their best prompts and context leave with them.

Double Jeopardy: Knowledge is siloed and when an employee leaves, your IP remains in the public cloud, but disappears from your company.

03 โ€ข FORTRESS DENADA

The Walled Hive Mind

Every time a team member uses Denada, it does get smarterโ€”but only for you. We create a secure "Hive Mind" where your team's collective insights are shared across your specific instance. It's shared intelligence without external exposure.

04 โ€ข SECURITY & COMPLIANCE

Audit Ready. Scrutiny Proof.

We don't just ask for trust; we prove it. Our infrastructure is logically isolated by customer. With SOC2 Type II compliance and full admin visibility, your legal team can breathe easy knowing your IP is walled off and encrypted.

Logical Isolation

ZERO TENANT CROSSOVER

SOC2

TYPEII

Audit

FULL LOGS

Public AI vs. Denada

Public AI vs. Denada

Public AI vs. Denada

Protect your most valuable asset

BUILT FOR THE ENTERPRISE. WALLED + ZERO CROSSOVER.