Case Study
From fragmentation to focus: How enterprise centralized LLM use across teams
A unified AI interface that gives 8,000+ employees access to leading language models while cutting costs, simplifying experimentation, and centralising control across the enterprise.
8000
Active users
5
LLM providers
80%
API cost reduction
70%
Faster Model Integration
Customer
Employees count
8000+
INDUSTRY
Digital media, marketing services, and software
Technologies
Librechat
Kubernetes
Open AI
Anhtropic
Google Vertex AI
Docker
Python
Typescript
INTRODUCTION
A global digital platform faced a new kind of challenge – democratizing access to generative AI across the enterprise.
As interest in large language models (LLMs) surged, different teams began using their own tools and subscriptions, leading to cost inefficiencies, duplicate efforts, and data governance concerns. The lack of a centralized solution meant that innovation was happening in silos, with no easy way to manage or scale the use of multiple AI providers.
OBJECTIVE
Our customer was dealing with a common enterprise challenge: teams across the organization were independently implementing various AI tools with overlapping capabilities, creating subscription based solutions that were both expensive and difficult to manage.
The primary objective was to consolidate everything into a centralized pay-per-use model.
Challenges
Fragmented usage of AI tools across teams.
High subscription(s) costs.
Lack of visibility and governance.
Complex LLM integration.
Our Approach
We explored three options to solve enterprise-wide AI fragmentation: adopting a single commercial platform, customizing open-source tools, or building a new solution. Each was evaluated for cost, speed, scalability, and long-term maintenance.
We recommended an open-source-first strategy and chose LibreChat as the foundation. Integrated with the company’s Kubernetes hosting, we delivered a working MVP in weeks, enabling immediate internal use. Together with stakeholders, we iterated on the platform, added support for major AI providers, and implemented fine-grained access controls. This approach standardized LLM access, cut costs by 80%, and scaled securely with minimal overhead.
Benefits
We developed a centralized platform based on open-source project LibreChat which aggregates leading LLM providers and self-hosted models, enabling employees to experiment through a single, branded interface.
Replaced subscription-based access with a flexible pay-per-API-call approach, reducing costs and enabling budget-friendly experimentation.
Integrated corporate identity systems to enforce role-based access and secure team-specific environments.
Built on top of an open-source foundation with custom UI improvements and additional features to provide an intuitive, frictionless experience for non-technical users.
Deployed the platform with a CI/CD-enabled backend for streamlined maintenance, rapid updates, and long-term scalability.
Enabled usage of the OpenAI API compatible locally hosted LLMs through the chat interface.
Results
Smart LLM Choices
Empowered to access and experiment with multiple LLMs from one platform
Significant cost savings
By eliminating redundant subscriptions and shifting to pay-per-call usage
Improved security and governance
Through centralized access control and monitoring
Accelerated innovation
By enabling faster experimentation across all departments
Sustainable platform
With a flexible architecture ready to support new models and teams without disruption
Conclusion
By focusing on unified access, pay-as-you-go usage, and a secure, user-friendly experience, our customer has created a repeatable playbook for any large organization looking to democratize AI.
Ultimately, our solution became more than a tool—it became the foundation for a company-wide shift in how AI is explored, understood, and used.
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