Managed operations for your AI workloads in production. Monitoring, governance, and cost control across ML models, GenAI, and autonomous agents.
Your team can build an AI agent in an afternoon. Getting it into production, governed, monitored, and accountable takes months of work nobody has capacity for. No governance framework for what agents can and cannot do. No audit trail for autonomous decisions. No risk classification. Compute costs discovered in the monthly bill, not in real time.
A dedicated AI operations model closes that gap. Firemind takes operational responsibility for your AI workloads in production - monitoring performance, governing behaviour, managing costs, and resolving incidents. Your team doesn't have to carry that.
What changes
AI workloads in production are not all the same. Firemind operates ML and inference workloads, GenAI and LLM workloads, and AI agent workloads - with tooling and expertise calibrated to each. All within the same closed-loop model that runs your cloud, VMware, and database estate.
Ready to see this running for your AI workloads?
A 30-minute conversation is all it takes to scope a contained pilot.
A contained pilot runs alongside your existing operations. No disruption, no lock-in, and a validated business case in 8 weeks.
Start with a defined set of AI workloads. Firemind operates in parallel from week one.
At week 8, results are measured side-by-side against your pre-pilot baseline.
If the business case is proven, expand scope at your pace. If not, no obligation.
These outcomes come from live AI operations deployments. Every AI decision logged with a full reasoning chain. Cost overruns caught as they happen. Budget and compute limits enforced per model, per agent, per invocation.
Proven in production
AI workloads operated in production. Outcomes measured, not estimated.
CES reduced speech-to-text costs by 85% using AI operations on AWS, replacing AWS Transcribe while maintaining accuracy and Welsh language support.
We can discuss outcomes from live AI operations engagements in a scoping call.
Contact usHealthcare, financial services, energy, manufacturing. We operate AI in regulated industries.
Contact usFAQ
We operate three categories: traditional ML and inference pipelines, GenAI and LLM workloads (RAG pipelines, chatbots, knowledge assistants), and AI agents that reason, plan, and take actions via MCP. Most organisations run a mix. Our operating model covers all three under one service.
Bedrock AgentCore and Claude Managed Agents provide dashboards, traces, and policy primitives. Firemind provides the operating model on top: the team that responds to incidents, tunes policies based on real-world behaviour, optimises costs, and evolves governance as your AI estate grows. The platforms build and deploy. We operate and improve.
Every engagement starts with monitoring, incident management, and audit-ready logging. From there, scope scales: cost management and quality evaluation, through agent behavioural governance and lifecycle management, to 24/7 support with guaranteed SLAs. Pricing is based on service scope and workload complexity. We agree scope and commercial model upfront.
Two things: service scope and workload complexity. Low complexity covers Pulse workflows and single-model GenAI applications. Medium covers MLOps pipelines, multi-model workflows, and agentic workloads. High covers container-based architectures, multi-cloud, and custom model hosting. Increases are triggered by defined events: adding workloads, increasing complexity, extending hours, or adding scope.
It depends on the risk classification you have defined. Low-risk anomalies trigger automated remediation. High-risk events escalate to a human before any action is taken. Every event is logged with its reasoning chain: what triggered it, what the agent decided, what it did, and what happened next.
Yes. We operate any MCP-enabled agent, including those built with Claude, custom LLM agents, and multi-model architectures. We also operate non-agentic workloads regardless of model or framework, as long as they run on AWS, Azure, or Google Cloud.
No. The governance framework, risk classification, and monitoring are best established before agents reach production, not retrofitted after an incident. Starting with a foundational operating model means your agents are not stuck in staging.
ISO 27001:2022 certified. EU data residency (Frankfurt). Assessed for EU AI Act compliance. ITIL-compliant change management, incident management, and audit logging. All data stays in your cloud account. No credential storage, no data egress.
Yes. The service runs inside your own cloud account. No data leaves your environment. No credentials stored externally. Audit-ready logging captures every AI decision with a full reasoning chain. For healthcare organisations, the service supports workflows aligned with GDPR, HIPAA, and EMA data governance requirements. For financial services, it supports MiFID II and FCA audit trail obligations. ISO 27001:2022 certified. EU data residency in Frankfurt.
