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Enterprise LLM Platform

The central intelligence layer: the request lifecycle from prompt registry through orchestration to a multi-model hub, with serving and RAG grounding on the path.

Architecture diagram of an enterprise LLM platform: prompt registry, fine-tuning and RAG grounding, orchestration engine, and multi-model hub.

Components

  • Orchestration Engine

    A model on its own generates text; a real task needs planning, memory, tool calls, and state held across turns.

    • A context and memory store holding short-term conversation and longer-term semantic context
    • Task planners that decompose a request into ordered sub-steps and route each to a suitable model
    • Reason-and-act loops that inspect results, recover from errors, and adjust the path

    Multi-step work driven to completion rather than a single answer returned and dropped.

  • Prompt Registry

    Prompts hardcoded across application files can't be governed; one edit silently regresses another tool.

    • A central template library shared between engineering and the teams who own the wording
    • Versioning with git-style rollback, so prompts can be A/B tested and promoted safely
    • Input validation on template variables to blunt prompt injection

    Prompts managed like the production software they are — versioned, reviewed, and changed without a redeploy.

  • Model Hub

    Wiring applications to one provider is a single point of failure and locks out faster or cheaper models as they appear.

    • One API surface that maps across providers, so application code doesn't know which model answered
    • Routing by prompt complexity, cost, and availability
    • Fallback to an alternative or self-hosted model when a provider degrades

    No provider lock-in, and model choice becomes an operational decision rather than a rewrite.

  • Model Serving & Inference

    Running open-weight models in your own infrastructure is where latency and GPU cost quietly get out of hand.

    • Inference engines tuned for throughput and token latency
    • GPU scaling and tensor parallelism to spread a model across devices
    • Quantisation and speculative decoding to cut memory use with little accuracy loss

    Custom weights served inside your own boundary at a cost that scales with use rather than against it.

  • Fine-Tuning & RAG

    A foundation model knows nothing of your data; asked about an internal contract, it guesses.

    • Semantic context assembly: retrieve, rank, and inject document chunks into the prompt
    • Embedding generation that turns corporate data into vectors capturing meaning
    • Targeted fine-tuning for domain jargon, formats, and nomenclature

    Answers grounded in enterprise sources and traceable back to them, rather than confident invention.