A self-hosted governance and access-control layer for enterprise LLM use. Chat, dashboards, apps and reports all stay within what each employee is permitted to see.
Employees already use public AI tools, and the company barely sees what is sent. Confidential information, strategy and trade secrets can end up in third-party models and eventually reach competitors.
Customer, financial and personal data can leak by accident, into public AI models or even to colleagues who should not see it.
Per-seat licensing is expensive, and as people want different models for different jobs, both cost and sprawl grow.
None of it is controlled, approved or auditable. There is no record of who accessed what, with which model, and why.
Key information is scattered across databases, documents, spreadsheets and internal systems. Turning it into analysis takes manual work and adds risk.
KPI tracking, plan-versus-actual variance and ad hoc analysis take time and expertise. Dashboards are prepared on a schedule, and new ones are slow to create.
Policies, procedures, manuals and know-how still live in files and in people's heads, so the organization cannot fully use what it knows.
Building internal software takes months and many iterations. Fit to purpose is uncertain, and key IT general controls such as privacy, retention and access rights are often skipped.
Before access, every resource is limited by classification, department and permission level.
Always secure, always within the user's permissions.
Chat with your own databases and knowledge base. Every SQL search and retrieval stays within the user's permissions.
Describe what you need in chat and shape a shareable dashboard on real data.
Reports, findings and recommendation papers written in chat can be shared, refreshed on a schedule and republished with new information.
Follow key topics and metrics. Scheduled summaries land in a shared database that others can own and follow.
Business users build role-based internal apps on governed data, with no code and no unmanaged shadow IT.
Each sensitive resource is requested, approved by an assigned owner and recorded, so accountability is complete.
Sensitive columns are masked, tokenized or blocked at the SQL level.
SAML or OIDC single sign-on for the user portal, with admin access protected separately.
Eight resource types move through Draft, Requested, Approved or Rejected, Close requested and Closed. Who requested, approved and routed each one, and when, is fully recorded. Primary and backup approvers are assigned, so nothing is approved by whoever happens to be available.
Every column is marked Open, Masked or Excluded, and anything missing from the catalog is Excluded by default. Masked values are tokenized per conversation, so the model reasons about the data without ever seeing whose it is.
Rules that a classification number cannot express, such as "HR chat must use internal services only" or "information above a confidentiality level must not go to a remote model", are enforced automatically in chat and dashboards.
SAML or OIDC single sign-on for the user portal. Separate logs cover general audit, database searches, knowledge base access, LLM usage and cost, and a dedicated masking-compliance report.
| The usual way | With Essential AI | Benefit | |
|---|---|---|---|
| Building dashboards | Data specialists build dashboards in tools such as Power BI or Tableau, under expensive enterprise licenses. | Business users build and publish the same kind of dashboard themselves, with no extra tooling or licenses. | Much faster, no extra licenses |
| Building apps | A gap between business and IT, project-management overhead and unpredictable time to market. IT general controls such as dev and production separation and access control are often unproven. | Business users model and release role-based internal apps, guided by the App Builder and reviewed by an admin. | Faster, with fewer resources |
| Knowledge visibility | Knowledge sits in hidden documents and training material, or needs a separate license for tools such as Confluence. | One governed knowledge base, searchable directly from chat and scoped by department and permission, with no separate wiki license. | One cost, instantly findable |
| Token and model cost | Per-seat pricing costs the same regardless of use, and enterprise licenses that protect data cost more. | Self-hosted and vendor-agnostic. Route each department to the lowest-cost model that meets policy, set per-user quotas, and keep sensitive data away from remote providers when needed. | Lower, more predictable cost |
| Shadow AI and data leakage | Employees use public AI tools outside company control, and sensitive prompts and files can leave the organization unseen. | All chat and data-search traffic goes through governed, department-scoped channels. Sensitive data is masked or blocked before it reaches a remote model. | Uncontrolled leakage closed off |
| Access governance | Permissions are granted directly, without an end date and irregularly, so access grows unchecked and is rarely reviewed. | Permissions are granted after approval and are time-bound. Each resource type has primary and backup approvers, so every grant has an owner. | Continuous access hygiene |
| Model and vendor flexibility | Locked into one AI vendor's price, plan and data-processing terms, and switching means changing the whole platform. | Built to work with any vendor. Route chat, dashboards and apps per department to an internal or remote LLM, and change vendor without touching the rest of the platform. | No vendor lock-in |
| Secrets and credentials | Database connection strings and API tokens often sit in plain configuration files and shared documents that nobody reviews. | Connection strings and vendor tokens are encrypted at rest and masked in the interface, with written IT general controls documentation. | Lower credential-leak risk |
Essential AI brings chat, dashboards, apps and reports together on the data you already have, governed from the first day.