Secure and Scale Your Models with AI Ops and Governance Services

An AI governance framework defines how AI systems are approved, owned, monitored, and reviewed. It helps reduce shadow AI, clarify accountability, manage model risk, and create consistent rules for data access, human review, and responsible AI use.

An LLM gateway is a control layer between users, applications, agents, tools, and model providers. LLM gateway implementation helps manage model routing, authentication, logging, rate limits, token/cost controls, fallback handling, and provider flexibility.

LLMOps services help teams manage LLM applications after launch. This includes prompt and model versioning, evaluations, deployment workflows, model monitoring, fallback handling, incident tracking, and continuous performance improvement.

LLM observability gives teams visibility into prompts, responses, latency, errors, costs, usage patterns, retrieval quality, hallucination risk, and agent actions. Tools like Langfuse and LangSmith can support tracing, debugging, evaluation, and monitoring across AI workflows.

AI policy & guardrails define what AI systems can access, what actions they can take, which use cases are approved, and when humans need to review outputs. They can also help reduce risks such as prompt injection, sensitive data exposure, unsafe responses, and unauthorized agent actions.

Deployed can support AI compliance services by helping teams create AI inventories, risk classifications, audit trails, documentation, oversight workflows, and control mapping. For the EU AI Act, we support compliance readiness, while legal interpretation or certification should involve qualified legal counsel.

Yes. Where relevant, Deployed can align responsible AI consulting and model risk management work with frameworks such as NIST AI RMF and ISO/IEC 42001. These frameworks help guide risk management, governance, accountability, monitoring, and continuous improvement.