Talk to Your Data: How Adam Brings AI to the Enterprise

Every organization sits on a mountain of data spread across dozens of systems: databases, SaaS platforms, internal tools, infrastructure. The problem has never been a lack of data. It's that accessing it requires knowing the right query language, the right API, the right person to ask.

What if you could just ask a question in plain English and get an answer?

That's Adam.

What Is Adam?

Adam (Adam Data Access Manager) is Eden's AI-powered conversational interface for enterprises. It connects to anything in your organization that's API-accessible (SaaS applications, internal databases, infrastructure systems) and lets you interact with all of it through natural language.

No SQL. No switching between dashboards. No waiting on an engineer to pull a report. Just ask.

"Which deployments failed in the last 24 hours and what do the logs say?"

"Show me our top 10 customers by revenue this quarter."

"How many open tickets do we have grouped by priority?"

Adam writes the queries, pulls the data, and presents it clearly, all within a single conversation.

Under the hood, Adam uses a tool integration layer that connects to your endpoints over a standard transport protocol. Managed Eden endpoints are automatically discovered, and organizations can also register custom tool endpoints for any internal service they want to expose. The result is a single conversational surface across every connected system: databases, APIs, and anything else your organization runs.

Built on Zero Knowledge

Most AI solutions want to ingest everything about your organization upfront. Adam takes the opposite approach.

Adam operates on a zero knowledge model. It knows nothing about your systems except what you tell it. There's no bulk data ingestion and no semantic training on your internal systems. Instead, Adam builds understanding the same way a new employee would: through interaction.

As you use Adam, it extracts memories from completed conversations: incident resolutions, runbooks, resource quirks, and approval patterns. Each memory is scoped to the individual user and organization that created it, with full provenance tracking so you always know how a piece of knowledge was learned. Memories are retrieved through semantic similarity search when relevant to future conversations, not baked into a model.

This isn't a limitation. It's a design choice. Zero knowledge means there's no sprawling index of your proprietary data sitting inside a model. Knowledge is built incrementally, scoped to individual users, and grounded in real interactions.

Least Privilege by Default

AI access control is one of the hardest problems in enterprise adoption. Give the model too much access and you've created a security liability. Give it too little and it's useless.

Adam solves this with a least privilege model backed by your existing credentials. If you don't have access to a system, Adam doesn't have access. If you only have read access, Adam only has read access. There's no separate permission layer to manage. Your organization's existing access controls are the access controls.

Eden enforces this through a two-level RBAC system:

• Control plane permissions govern what you can see and configure: whether you can view an endpoint, modify its settings, or remove it entirely.
• Data plane permissions govern what you can do with the data: read, write, or execute operations against connected systems.

Both layers must pass before any tool call is executed. On top of that, every conversation has a tool approval mode that determines whether tool calls require explicit user approval, auto-execute for read-safe operations only, or auto-execute everything. The default is to ask. Least privilege, enforced at every step.

This means deploying Adam doesn't require rethinking your security posture. It inherits it.

PII Protection and Admin Controls

Access control is only half the equation. Even when a user has legitimate access to a system, there's information the AI shouldn't surface freely.

Adam addresses this with built-in PII detection and redaction. Sensitive data (Social Security numbers, passwords, API keys, bearer tokens, JWTs, AWS access keys, and credential-bearing URLs) is identified through pattern matching before it ever reaches the model. The semantic layer also scans database schemas for columns likely to contain PII (emails, phone numbers, financial data, government IDs) and flags them, excluding those columns from value sampling and redacting foreign key references to restricted tables.

The principle is straightforward: just because you can see something doesn't mean the AI should surface it in a conversational context.

Beyond automatic redaction, administrators get granular control over what Adam can and cannot do:

• System prompt management. Define and update the instructions that guide Adam's behavior across the organization.
• Skill management. Create, activate, deactivate, and scope custom skills to specific endpoints or use cases.
• Tool safety classification. Categorize operations by risk level (safe, moderate, dangerous) so that destructive actions like DELETE or DROP require higher approval thresholds than a SELECT.
• Schema-level access control. Restrict which tables and columns are visible to which users, with filtered schemas enforced before the model ever sees the data.
• Credential isolation. API keys are never returned in full through any API response. Authentication information is resolved at runtime and never shared with the model.

No passwords, usernames, or API keys are ever exposed to the conversational layer. Security isn't an add-on. It's the architecture.

See It in Action

Watch Adam navigate a live healthcare environment, querying patient records, redacting PII, and breaking down data into cohorts, all through natural conversation.

What This Looks Like in Practice

In our demo, we connected Adam to 10 different services in a healthcare environment: patient records, billing and insurance data, and several other clinical and administrative systems.

A non-technical user can start with something as simple as "Show me what we're connected to" and get a plain-language summary of every available service. From there, the conversation flows naturally:

1. Explore. "Which patient has the most records?"
2. Drill down. "Tell me more about this patient." (PII like SSNs are automatically redacted)
3. Analyze. "What's the distribution of patients by age and gender?"
4. Refine. "Break that into cohorts by age range."

Each step generates real queries against real data. Adam uses the semantic layer to understand your schema (table relationships, column types, foreign keys) and assembles that context before generating a query. The results come back formatted and readable. Just answers.

And because Adam remembers, the next time you reference "that patient database" or "the billing system," it already knows what you mean.

Why This Matters for Organizations

The gap between "having data" and "using data" is enormous in most organizations. Reports take days. Dashboards answer last quarter's questions, not today's. The people closest to the problems rarely have the technical skills to query the systems themselves.

Adam closes that gap without compromising on security:

• No new infrastructure to deploy. Connect your APIs and go. Adam supports multiple LLM providers (OpenAI, Anthropic, Ollama, OpenRouter) so you can use the models you already trust.
• No retraining required. If you can ask a question, you can use Adam.
• No security trade-offs. Zero knowledge, least privilege, PII redaction, and admin controls come standard.
• No vendor lock-in on your data. Adam queries your systems in real time. Your data stays where it is.

Get Started

We're launching a waitlist for early access to Adam. If your organization is sitting on data that's hard to reach, we'd love to show you what's possible when you can just ask.

Join the waitlist and follow our blog for deeper dives into how Adam works under the hood.