LLM Ops Services for Enterprise AI Reliability

Operationalize, monitor, and scale large-language-model deployments with enterprise-grade governance, observability, and lifecycle automation.

Radiansys delivers LLM Ops Services that unify deployment, monitoring, evaluation, and retraining pipelines, ensuring AI remains accurate, secure, and cost-efficient over time.

Deploy and orchestrate LLMs across hybrid or multi-cloud environments (AWS, Azure, GCP, CoreWeave).

Monitor model health using real-time metrics, latency, accuracy, cost, and drift.

Automate retraining with CI/CD pipelines and continuous feedback loops.

Govern AI operations with auditability, access controls, and compliance frameworks.

How We Implement LLM Operations

At Radiansys, our LLM Ops framework is designed for enterprise-grade reliability, observability, and governance. We combine DevOps automation with AI engineering precision to ensure large language model deployments remain scalable, secure, and compliant throughout their lifecycle, from initial deployment to continuous monitoring and retraining. Every implementation is built to support production workloads across regulated environments while maximizing model performance and ROI.

Deployment Automation

We standardize model deployment using containerized environments (Kubernetes, Docker) and infrastructure-as-code (Terraform, Pulumi). Each model is deployed as a microservice with API endpoints for seamless integration into enterprise systems, including CRMs, ERPs, and collaboration tools. Our auto-scaling architecture handles variable traffic loads, ensuring high availability while minimizing compute waste.

01

Monitoring & Observability

Every LLM deployment includes deep observability dashboards for token usage, latency, hallucination rates, and accuracy drift. We integrate Prometheus, Grafana, and OpenTelemetry to visualize health metrics in real time. Custom alerts and thresholds enable teams to identify performance regressions early and take corrective action before they impact users.

02

Evaluation & Feedback Loops

To maintain quality and trust, we embed evaluation harnesses that score outputs on faithfulness, coherence, and toxicity using BLEU, ROUGE, and custom semantic similarity metrics. Human-in-the-loop feedback is continuously integrated to improve response relevance and reduce hallucinations. Performance data is stored for trend analysis and fine-tuning triggers.

03

Retraining Pipelines

Our CI/CD pipelines automate model versioning, testing, and promotion to production. Using Airflow, MLflow, and LangSmith, we enable continuous retraining based on fresh data and user feedback. Approval workflows govern each retraining cycle to preserve model integrity and traceability.

04

Security & Governance

We embed SOC 2, HIPAA, and GDPR-aligned controls across every layer of the Ops stack. Data in transit and at rest is encrypted using AES-256 and TLS 1.3. Identity management is handled through SSO and RBAC/ABAC, which restrict access based on roles and tenure. Audit logs and governance dashboards provide complete visibility for compliance officers and security teams.

05

Cost & Performance Optimization

We reduce inference and training costs through the use of GPU pooling, quantization, and mixed-precision techniques. Dynamic resource allocation and model sharding ensure optimal throughput without over-provisioning. Our Ops dashboards track token usage and compute spend, giving enterprises control over budget and efficiency at scale.

06

Continuous Improvement & Support

Post-deployment, our managed LLM Ops service provides continuous support, model health audits, and governance reviews. We schedule periodic retraining and bias audits to keep models aligned with evolving data and compliance requirements, ensuring that AI remains a trusted, high-performance asset throughout its lifecycle.

07

Use Cases

Model Lifecycle Automation

Connect LangChain agents to enterprise databases and document stores for instant, citation-backed answers. Ideal for research teams and policy assistants.

AI Monitoring & Compliance

Gain transparent oversight with dashboards tracking model drift, bias, latency, and hallucination rates. Automatic logging and explainability layers help maintain SOC 2, HIPAA, and GDPR compliance across all AI systems.

Multi-Model Orchestration

Operate multiple LLMs, OpenAI, Anthropic, Hugging Face, or custom models, through a unified control plane. Easily manage A/B testing, routing, and performance optimization in hybrid cloud setups.

Cost-Efficient AI Scaling

Operate multiple LLMs, OpenAI, Anthropic, Hugging Face, or custom models, through a unified control plane. Easily manage A/B testing, routing, and performance optimization in hybrid cloud setups.

Business Value

Operational Excellence

Automate complex workflows with tool-using LLM agents, reducing manual effort by up to 60%.

Compliance & Control

Integrated governance frameworks enable secure model operations and audit transparency for regulated industries.

Reduced Operational Costs

Automation and GPU optimization significantly lower infrastructure costs without compromising accuracy.

Scalable AI Lifecycle

Continuous feedback and CI/CD pipelines keep LLMs aligned with real-world data and business goals.

FAQs

LLM Ops extends MLOps for large language models, covering deployment, monitoring, evaluation, and retraining to ensure long-term reliability.

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