AI applications connect to a shared agent runtime and orchestration layer. A model abstraction layer routes requests across multiple LLM providers, while a common knowledge and RAG layer handles ingestion, embeddings, vector search, and retrieval. Enterprise integrations, observability, and governance provide reusable controls across all applications.

Establish a shared enterprise AI platform that reduces duplicated engineering, supports multiple models and applications, and gives teams a governed foundation for scaling AI adoption. 

Executive Summary 

• Client Situation: Multiple teams were experimenting with different LLM providers, frameworks, knowledge systems, and monitoring approaches, creating duplicated effort and inconsistent governance. 

• Aptly Response: Aptly engineered a shared multi-LLM AI platform with model abstraction, agent runtime, orchestration, RAG services, integrations, observability, and governance controls. 

• Results: Teams could build AI applications faster using shared capabilities while maintaining flexibility, governance, cost visibility, and common operating standards. 

• 60% reuse of shared AI platform capabilities across new use cases
• 45% faster time-to-production for AI applications
• 35% improvement in AI operating cost efficiency

Client
Global Digital Platform Leader

Industry
Enterprise AI Platforms & Technology

Technologies

Building a scalable foundation for multiple AI applications, agents, and enterprise use cases. Aptly engineered a shared multi-LLM enterprise AI platform that reduced duplicated engineering effort and provided reusable capabilities for model abstraction, agent runtime, AI orchestration, knowledge and RAG services, enterprise integration, observability, and governance. The platform enabled teams to choose the right model for each workload while maintaining consistent operating controls.

• Reduced duplicated engineering across AI initiatives 

• Enabled flexible model selection for different workloads 

• Standardized AI application architecture and operating controls 

• Improved visibility into usage, performance, and cost 

• Created a platform foundation for future AI agents and applications 

Aptly designed the platform as a shared enterprise capability rather than a set of isolated AI applications. The architecture introduced a model abstraction layer so teams could evaluate, and switch models based on accuracy, latency, context needs, reliability, and cost. Shared knowledge services, integration patterns, observability, and governance controls allowed multiple teams to reuse platform capabilities while still building use-case-specific AI experiences.