Building a Secure, Cost-Efficient AI Portal for Company-Wide LLM Access and Agent Creation & Sharing

Background

AI adoption inside most organizations doesn’t begin with strategy. It begins with experimentation and more likely AI use in consumer apps.

In this SMB, employees had already discovered the productivity lift that large language models could provide. Some were using public tools informally. Others had secured access to approved enterprise models through separate departmental licenses. The results were promising and experimentation was increasing.

The company was facing a familiar but uncomfortable reality: AI was already embedded in daily workflows, just not in a coordinated or governed way.

Leadership wasn’t trying to slow things down. They weren’t skeptical of AI’s value. In fact, they believed it would become foundational to how their teams operated.

What concerned them was possible data leakage, many models and costs, limited visibility, limited sharing and limited visibility into use.

Challenge

At first glance, the problem looked simple: provide secure access to LLMs. In practice, it was more nuanced.

Employees were already using AI tools. That meant any centralized solution had to be compelling enough to replace what they were doing on their own. If it introduced friction, they would revert to unofficial tools. If it restricted model choice too heavily, adoption would stall.

At the same time, the security team had legitimate concerns. Sensitive internal information was being entered into third-party tools with unclear data handling policies. Even well-intentioned use posed risk. 

Layered on top of that was the risk of cost inefficiency. The solution needed to support current and future models.

The company needed a solution that accomplished four things simultaneously:

• Centralize and secure all LLM interactions
• Intelligently manage and understand spend
• Enable employees to build reusable AI workflows
• Create a flexible foundation for future AI expansion

This required more than access control. It required a platform.

Solution

The Architecture

The core design principle was simple: every AI interaction should flow through a secure, governed gateway.

Instead of allowing direct connections to model providers, we built a centralized AI portal that sits between users and any LLM. This portal integrates directly with the company’s corporate identity system through SSO, ensuring that every interaction is authenticated and tied to an existing role. There are no personal API keys, no unmanaged accounts, no anonymous usage.

From there, the system orchestrates requests across multiple model providers and connected systems and data structure. Rather than committing to a single vendor, the portal supports different LLMs under a unified interface. This model-agnostic design allows administrators to manage availability centrally while giving users flexibility.

Model routing and usage tracking occur within the portal layer. Administrators can monitor consumption by user, team, or model type, creating cost transparency that previously did not exist. High-cost models can be restricted to specific roles. New models can be introduced without redesigning the system.

The architecture is intentionally extensible. The AI ecosystem evolves rapidly. By decoupling users from direct provider integrations, the company gains agility. Swapping, adding, or modifying models becomes an administrative decision rather than a technical overhaul.

In many ways, the portal functions less like a chatbot interface and more like internal AI infrastructure.

Enabling User-Created Agents

Secure access alone would not unlock the real value. One of the primary goals was to allow teams to build and share their own AI agents — structured, reusable configurations that encapsulate prompts, behavior, and context.

Within the portal, users can create agents tailored to specific tasks. These agents can be shared with individual colleagues, teams, or across the entire company, depending on permissions.

However, this flexibility operates within clearly defined governance boundaries. Sharing is role-based. Access to integrated data sources respects existing permissions. Administrative controls determine which external systems can be connected.

This balance is subtle but important. The system encourages experimentation while ensuring that privilege boundaries remain intact. An employee does not gain access to new datasets simply by interacting with an agent. Identity propagation ensures that underlying data-level security is maintained.

Secure Integration with Internal Systems

The most meaningful AI applications draw from internal knowledge. To support this, the portal allows administrators to configure connections to approved internal data sources and systems. These integrations are not open-ended. They are established and governed centrally, with clear alignment to the company’s data storage and security standards.

When a user incorporates an internal dataset into a conversation or agent workflow, access checks occur against their existing permissions. The AI layer does not override established controls. It respects them.

This was a critical requirement for security approval. The portal could not become an alternate path to sensitive data. Instead, it had to function as an extension of the existing identity and data governance model. By aligning the AI layer with established security frameworks, the company avoided creating a parallel ecosystem of risk.

Governance and Cost Control

AI costs can expand quickly when usage is decentralized. By consolidating access through the portal, the company gained real-time visibility into consumption patterns. Usage can be analyzed by individual, by team, or by model type. Administrators can adjust availability, implement guardrails, or optimize routing strategies based on actual behavior.

Equally important, the portal reduced the incentive for shadow AI. When employees have a secure, flexible, officially supported environment that meets their needs, unofficial workarounds naturally decline.

Governance, in this case, did not require heavy enforcement. It required a better alternative.

Results

The implementation did more than centralize access. It shifted the organization’s relationship with AI.

Unofficial usage decreased because employees preferred the sanctioned platform. Licensing inefficiencies were reduced by consolidating access under a usage-based model. Security concerns around unintentional data exposure were mitigated through identity enforcement and controlled integrations.

Perhaps most significantly, the company is now positioned to evolve. New models can be onboarded quickly. New internal systems can be integrated without architectural disruption. Teams can develop and share agents as needs expand. The foundation supports experimentation rather than constraining it.

For an SMB, this flexibility matters. They do not have the luxury of rebuilding infrastructure every year as technology changes.

Instead of chasing AI tools, they now operate a platform.