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LLMOps, MLOps & Content Safety
— guardrails that hold

Production discipline for GenAI — prompt shields, content safety, evaluation harnesses and release gates that keep models useful, safe and on budget under real traffic.

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LLMOps, MLOps & Content Safety LLMOps, MLOps & Content Safety
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Topics we cover
Content safetyPrompt ShieldPrompt injection defenceJailbreak detectionGuardrailsGroundedness detectionHallucination detectionPII redactionOutput filteringLLM evaluation harnessRelease gatesLLMOpsMLOpsAI observabilityRed-team regressionSafety benchmarks
Status quo · late 2026

What has changed — and why it matters now

01

Injection is the top risk

Indirect prompt injection through documents, web content and tool results is the most exploited LLM weakness; retrieval and MCP outputs must be treated as untrusted input.

02

Guardrails are layered

Input shields, retrieval validation, output classifiers, tool allow-lists and human approval — no single filter is enough.

03

Safety is measured continuously

Red-team suites run in CI; safety, groundedness and refusal metrics are tracked like uptime.

How we approach it

Our method for LLMOps, MLOps & Content Safety

01Threat model the applicationOWASP LLM Top 10, MITRE ATLAS; data flows, tools, trust boundaries.
02Design guardrailsPrompt shields, content classifiers, PII redaction, groundedness checks, tool permissions.
03Build the harnessGolden sets, adversarial sets, LLM-as-judge with calibration, safety benchmarks.
04Gate releasesQuality, safety, cost and latency thresholds enforced in CI/CD for prompts, models and agents.
05MonitorLive classifiers, drift, abuse detection, incident response.
In depth

Guardrail layers

Input

Prompt shields, jailbreak classifiers, PII detection, rate limits.

Retrieval & tools

Document sanitisation, injection scanning, tool allow-lists, MCP gateway policies.

Model

System prompt hardening, structured outputs, model routing to safer models for sensitive tasks.

Output

Content safety categories, groundedness and citation checks, format validation.

Human & audit

Approval gates for consequential actions, full traces, reversible operations.

What you get

Deliverables

LLMOps & MLOps practicePrompt ShieldContent safetyEvaluation harnessesRelease gates
Technology-agnostic

Platforms we work across

We design for the outcome first and choose the platform second — across Microsoft, AWS and Google Cloud, and every major AI provider.

Azure AI Content Safety & Prompt ShieldsAWS Bedrock GuardrailsGoogle Model ArmorAnthropicOpenAI ModerationNVIDIA NeMo GuardrailsLlama GuardLangfuse / Arize
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Ready to talk about LLMOps, MLOps & Content Safety?

One email reaches the architects and engineers who will do the work. We reply within one working day.

info@uspc.co.uk Contact page