LLM-Ops

LLM-Ops addresses the operational reality that language models in production behave differently than language models in development. Model outputs drift over time as the underlying model is updated by providers. Costs scale non-linearly with usage patterns that were not anticipated during design. Guardrails that worked during testing fail on production input distributions. Latency that was acceptable in a demo is unacceptable in a user-facing product. These are not edge cases — they are the normal operating conditions of a production AI system, and they require purpose-built operational infrastructure to manage.

Model evaluation is the foundation of LLM-Ops. Before a model goes to production, it must be evaluated against task-specific metrics — not generic benchmarks. A model being used to extract structured data from legal documents must be evaluated on that task, with a dataset that reflects the actual distribution of documents it will encounter. A model generating clinical documentation must be evaluated on clinical accuracy metrics. Without task-specific evaluation, you cannot know whether the model meets the performance bar required for your use case, and you cannot detect when it stops meeting that bar.

Guardrail enforcement is the compliance layer of LLM-Ops. Guardrails define what the model is permitted to generate and what actions an agent is permitted to take. In regulated industries, guardrails are not optional — a model that can be prompted to generate non-compliant outputs, or an agent that can be instructed to take unauthorized actions, is a compliance liability. Guardrails must be implemented architecturally (input/output validation layers, tool call validation), not purely as prompt instructions that a sufficiently creative user can circumvent.

We ship every AI and agentic deployment with LLM-Ops infrastructure as standard — task-specific evaluation frameworks, production monitoring dashboards, drift detection, cost tracking, and guardrail enforcement layers. We do not ship AI systems without the ability to observe and measure what they are doing in production. Compliance-specific guardrails are implemented through ALICE and validated against your regulatory framework before go-live.

Compliance-Native Architecture Guide

Design principles and a structured checklist for building software that is compliant by default — not compliant by retrofit. Covers data architecture, access controls, audit trails, and vendor due diligence.