AI Platform Foundations (LLMOps Setup)

Reference Architecture for LLM Applications

Building LLM features without a coherent architecture leads to duplicated infrastructure, inconsistent behaviour, and cost surprises. We deliver a reference architecture covering the gateway layer, request routing, response caching, and workload isolation that serves as the foundation for every LLM-powered feature your teams build.

The architecture defines how requests enter the system, how they are routed to the appropriate model or provider, where caching is applied to reduce redundant inference calls, and how workloads are isolated to prevent a runaway prompt from affecting unrelated services.

Fry Express designs the architecture to be opinionated where consistency matters and flexible where teams need autonomy. It is documented, versioned, and maintained as a living artefact, not a one-time diagram.

Provider Abstraction, Fallbacks, and Cost-Aware Model Selection

Locking into a single LLM provider creates risk. We implement a provider abstraction layer that lets your applications switch between models and providers without code changes. Fallback chains ensure that if a primary provider degrades, traffic routes to an alternative with minimal latency impact.

Cost-aware model selection goes further: requests are routed to the most cost-effective model that meets the quality requirements for a given task. A simple classification task does not need the same model as a complex reasoning chain. The routing logic is configurable and observable, so teams can see which model handled each request and at what cost.

This layer protects you from provider outages, pricing changes, and capability shifts. It also gives you leverage in vendor negotiations because migration is a configuration change, not a rewrite.

Secure Environment Setup With Access Controls and Network Boundaries

AI workloads handle sensitive data, proprietary prompts, and expensive compute. We establish secure environment configurations with secrets management, role-based access control, network boundaries, and data access policies that are enforced from the infrastructure level.

Secrets are injected at runtime, never stored in repositories or environment variables accessible to application code. RBAC ensures that only authorised services and users can invoke models, access vector stores, or modify prompt configurations. Network boundaries prevent AI workloads from reaching systems they have no business communicating with.

Fry Express applies the same security rigour to AI infrastructure that you expect for any production system. The controls are automated, auditable, and integrated into the deployment pipeline.

Budget Guardrails and Cost Telemetry From Day One

AI cost overruns are easiest to prevent when controls are built into the platform from the start, not bolted on after the first surprise invoice. We wire budget guardrails and cost telemetry into the platform layer so that every LLM call is metered, attributed, and subject to configurable spend limits.

Per-team, per-feature, and per-environment budgets are enforced automatically. When a budget approaches its limit, alerts fire and, if configured, traffic is throttled or routed to cheaper models. Cost telemetry feeds into your existing observability dashboards so that spend is visible alongside latency and error rates.

This is not a reporting layer added after the fact. It is a platform capability that every team inherits by default when they build on the foundation.

RAG Baseline, Deployment Pipelines, and Operational Readiness

Retrieval-augmented generation is a common pattern, but getting it production-ready requires more than a vector database and an embedding model. We deliver a RAG platform baseline that covers ingestion pipelines, chunking strategies, index management, and retrieval quality validation.

Deployment pipelines handle the full lifecycle: code changes, prompt updates, index rebuilds, and model swaps all follow a consistent, automated path from development to production. Every deployment is reversible, and the pipeline enforces the evaluation gates defined in your quality framework.

Operational readiness means the platform ships with monitoring, alerting, runbooks, and clear ownership assignments. On-call engineers know what to check, how to diagnose common failures, and when to escalate. Fry Express does not consider a platform delivered until the team operating it can do so independently.

These deliverables provide the infrastructure layer that makes LLM and agent features a sustainable engineering practice rather than a collection of experiments. Teams build on a shared, secure, cost-aware foundation and ship with confidence from the first feature onward.