MLOps Consulting Services & LLMOps Solutions

Our MLOps consulting services deploy, monitor, and optimize AI in production with the engineering discipline that keeps every model accurate, cost-efficient, and reliable long after launch day.

A model that performs on launch day can silently fail 90 days later. We build the operational systems that prevent that.

87% of ML models never make it to production. Of those that do, many experience declining model reliability within months, often without detection until business outcomes are affected.

Data Drift

The data the model sees in production diverges from what it was trained on. Accuracy falls. No alerts fire. Results worsen slowly.

Concept Drift

The relationship between inputs and correct outputs changes. Payer rules shift. Clinical protocols evolve. The model doesn’t know.

LLM Degradation

Prompt templates rot. RAG retrieval quality drops. Token costs spiral. Hallucination rates climb. No one is watching.

MLOps and LLMOps are the engineering disciplines that prevent all three, improving model reliability while keeping AI accurate, efficient, and trustworthy over time.

MLOps governs traditional ML models. LLMOps governs generative AI operations. Both are essential for production AI.

MLOps

MLOps applies DevOps for machine learning across the ML lifecycle, bringing CI/CD, version control, automated testing, and monitoring to model development and deployment.

Experiment tracking and model versioning

Automated model training and evaluation pipelines

Feature store management and data lineage

Model registry and staged deployment

Production monitoring and drift detection

Automated retraining triggers and pipelines

LLMOps

LLMOps extends MLOps with operational practices for generative AI operations, including prompt governance, RAG quality management, hallucination monitoring, and cost controls for production LLMs.

Prompt versioning, testing, and regression tracking

LLM evaluation frameworks for accuracy, safety, cost, and fairness assessment

RAG pipeline quality monitoring and optimization

Hallucination detection and output validation

Token cost tracking, budgeting, and optimization

Fine-tuning pipeline management and validation

From automated deployment pipelines to LLM quality operations — built into every AI system we deliver.

ML Pipeline Engineering & CI/CD

Automate every stage of the ML model lifecycle

Manual ML workflows slow deployment and introduce operational risk. We build automated pipelines that move models from experimentation to production with version control, automated testing, staged rollouts, and rollback mechanisms, making every deployment repeatable, auditable, and production-ready.

End-to-end model training and evaluation pipelines with automated data validation, feature engineering, packaging, and deployment.
CI/CD for DevOps for machine learning, including automated unit testing, integration testing, performance regression, and bias validation.
Model registry — versioned model artifacts with metadata, evaluation results, lineage tracking, and deployment history
Blue/green and canary deployments — staged rollout with automated traffic splitting, monitoring, and rollback triggers
Toolchain: MLflow, Kubeflow, Weights & Biases, DVC, GitHub Actions, and cloud-native ML pipelines (SageMaker, Vertex, Azure ML)

Production Monitoring & Drift Management

Know when your models start failing before users do

Production AI degrades silently. We build monitoring systems that protect model reliability by detecting data drift, concept drift, and performance degradation, generating alerts before business outcomes are affected and triggering automated retraining when drift exceeds defined thresholds.

Data drift detection — statistical tests on feature distributions comparing production data to training baseline in real time
Prediction drift monitoring — track output distribution shifts and model confidence degradation over time
Performance metrics dashboards — precision, recall, F1, latency, throughput, and business KPI tracking per model
Automated retraining triggers — threshold-based and schedule-based pipelines that retrain, evaluate, and deploy without manual intervention
Hard negative mining — systematic capture of model failures to enrich training data and close accuracy gaps over time

LLM Evaluation & Quality Operations

The operational discipline behind generative AI operations

LLMs in production fail in ways traditional ML doesn’t. Hallucinations, prompt regressions, RAG quality degradation, and rising token costs all impact production performance. We build LLMOps frameworks that detect and manage these issues before they affect users or budgets.

LLM evaluation pipelines: Automated scoring for accuracy, groundedness, coherence, safety, fairness assessment, and task-specific KPIs.
Prompt regression testing — detect when model updates or prompt changes degrade output quality before production deployment
RAG quality monitoring — retrieval relevance scoring, citation accuracy tracking, and chunk quality assessment
Hallucination detection and guardrails — output validation layers that flag, filter, or escalate ungrounded responses
Token cost operations — per-call cost tracking, budget alerts, model routing by cost/quality tradeoff, and monthly spend forecasting

Six stages. Every one automated, monitored, and production-ready

Pipeline Layer

What Gets Built Here

Data & Feature
Layer

Data validation, feature engineering, feature store management, and training dataset versioning. The foundation of every ML model lifecycle, ensuring consistent, high-quality data from training through production.

Experiment & Training Layer

Experiment tracking, hyperparameter optimization, model training and evaluation, and orchestration. Every experiment is logged, compared, and reproducible for consistent deployment decisions.

Registry & Packaging Layer

Model registry with versioned artifacts, evaluation metadata, approval workflows, and deployment readiness gates. Every model that reaches production has a documented lineage and sign-off trail.

Deployment & Serving Layer

Canary, blue/green, and shadow deployments with traffic routing, A/B testing, and automated rollback triggers. Safe, repeatable deployments built for production AI.

Monitoring & Alerting Layer

Real-time dashboards, data drift detection, anomaly alerts, SLA monitoring, and PagerDuty or Slack integration. Detect issues before users experience degraded performance.

Retraining & Feedback Layer

Automated retraining pipelines, hard negative mining, RLHF integration, and continuous improvement workflows that keep models accurate throughout their lifecycle.

Vendor-agnostic expertise across the MLOps and LLMOps ecosystem. We recommend the right tools for your architecture, operational requirements, and deployment strategy.

Domain	Tools & Platforms We Work With
Experiment Tracking	MLflow · Weights & Biases · Neptune · ClearML
Pipeline Orchestration	Kubeflow · Apache Airflow · Prefect · ZenML · Metaflow
Model Serving	BentoML · Triton Inference Server · vLLM · TorchServe · Seldon
Feature Stores	Feast · Tecton · Hopsworks · AWS Feature Store · Vertex Feature Store
LLM Evaluation	RAGAS · TruLens · LangSmith · Braintrust · Confident AI
Monitoring & Drift	Evidently AI · Arize · Fiddler · WhyLabs · Grafana + Prometheus
Cloud ML Platforms	AWS SageMaker · Azure ML · Vertex AI · Databricks MLflow
Data Version Control	DVC · LakeFS · Delta Lake · Apache Iceberg

Real-world examples of how our MLOps consulting services improve model reliability, automate AI operations, and support production AI across regulated and enterprise environments.

Healthcare AI Operations

Medical coding model monitoring to detect payer rule changes before claim accuracy declines.

Clinical NLP pipeline operations for RAG quality monitoring and hallucination detection.

Prior authorization model retraining pipelines as payer criteria and clinical protocols evolve.

ImpactRCM.AI agent operations with continuous performance monitoring and optimization.

Financial Services AI

Fraud detection model monitoring to identify concept drift before detection accuracy declines.

Credit scoring pipelines with regulatory validation, explainability reporting, and production bias monitoring.

LLM compliance monitoring for hallucination detection and factual accuracy.

Risk model CI/CD with automated validation and staged production deployment.

Enterprise LLMOps

RAG platform quality monitoring for retrieval accuracy, grounded responses, and citation quality.

Prompt regression testing before every prompt or model release.

Token cost governance with budget controls and intelligent model routing.

Multi-LLM operations across OpenAI, Anthropic, Azure OpenAI, and self-hosted models.

Manufacturing & Operations AI

Quality inspection model monitoring as product lines, materials, and defect profiles evolve.

Predictive maintenance retraining triggered by equipment telemetry changes.

Supply chain ML pipeline monitoring with scheduled retraining for seasonal demand shifts.

Computer vision model registry for version-controlled multi-site deployments.

Measured from production environments, not vendor benchmarks.

90%+

Model reliability maintained 90 days after deployment

60%

Reduction in LLM token costs via optimization

10×

Faster deployment through automated CI/CD pipelines

Zero

Silent model failures with continuous monitoring

AI Engineering &
Platform

Building AI infrastructure for MLOps with scalable compute, model serving, and deployment architecture.

Generative AI & LLM Solutions

Generative AI solutions supported by generative AI operations for reliable deployment, monitoring, and continuous optimization.

ML & Predictive
AI

Production MLOps services that improve model reliability through continuous monitoring, retraining, and lifecycle management.

AI Governance & Responsible AI

Governance frameworks integrated with MLOps monitoring, audit trails, compliance controls, and responsible AI operations.

Production AI requires continuous monitoring, optimization, and governance. Our MLOps consulting services make it happen.

Industries we serve

We collaborate with global technology leaders to deliver secure and scalable growth-driven digital solutions. Our partnerships strengthen our ability to innovate, accelerate transformation, and drive measurable business impact for our clients.

Case Studies

We used generative AI to automate documentation, compliance checks, and medical coding. The solution improves accuracy, cuts manual effort, speeds turnaround, and ensures regulatory compliance in clinical use.

0 +

200 +

When patient data was summarized clearly, documentation felt less burdensome. With CaliberFocus, clinician satisfaction rose from 58% to 81% without changing how teams work.

Dr. Rebecca HallCMIO, Healthcare

Better documentation and fewer audit issues delivered real savings. With CaliberFocus, billing compliance improved to 98.6%, reducing risk while easing the burden on clinicians.

Sophia WilliamsCTO, Clinical care

We gained clear visibility into student performance. Engagement rose, scores improved, and administrative effort dropped by nearly 30 percent, giving educators time to teach.

Andrew MillerDirector, Education Technology

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CaliberFocus delivers MLOps consulting services and LLMOps services that help organizations monitor, optimize, and govern AI systems throughout the entire model lifecycle. From ML model lifecycle management and DevOps for machine learning to Generative AI operations, model reliability, and MLOps maturity assessments, we build operational frameworks that keep AI accurate, efficient, and production-ready.

MLOPS & LLMOPS

Deploying a Model Is the Start. Keeping It Accurate Is the Work.

Production AI Challenges That Impact Model Reliability

Data Drift

Concept Drift

LLM Degradation

MLOPS vs LLMOPS

Two disciplines. One shared goal: Reliable AI operations.

MLOps

Experiment tracking and model versioning

Automated model training and evaluation pipelines

Feature store management and data lineage

Model registry and staged deployment

Production monitoring and drift detection

Automated retraining triggers and pipelines

LLMOps

Prompt versioning, testing, and regression tracking

LLM evaluation frameworks for accuracy, safety, cost, and fairness assessment

RAG pipeline quality monitoring and optimization

Hallucination detection and output validation

Token cost tracking, budgeting, and optimization

Fine-tuning pipeline management and validation

What we build

Three core operational capabilities

ML Pipeline Engineering & CI/CD

Production Monitoring & Drift Management

LLM Evaluation & Quality Operations

The full pipeline

End-to-end ML model lifecycle management

Pipeline Layer

What Gets Built Here

Data & Feature Layer

Experiment & Training Layer

Registry & Packaging Layer

Deployment & Serving Layer

Monitoring & Alerting Layer

Retraining & Feedback Layer

The MLOps & LLMOps toolchain

Enterprise MLOps tools & platforms we work with

Where MLOps consulting services deliver results?

MLOps & LLMOps across enterprise industries

Healthcare AI Operations

Medical coding model monitoring to detect payer rule changes before claim accuracy declines.

Clinical NLP pipeline operations for RAG quality monitoring and hallucination detection.

Prior authorization model retraining pipelines as payer criteria and clinical protocols evolve.

ImpactRCM.AI agent operations with continuous performance monitoring and optimization.

Financial Services AI

Fraud detection model monitoring to identify concept drift before detection accuracy declines.

Credit scoring pipelines with regulatory validation, explainability reporting, and production bias monitoring.

LLM compliance monitoring for hallucination detection and factual accuracy.

Risk model CI/CD with automated validation and staged production deployment.

Enterprise LLMOps

RAG platform quality monitoring for retrieval accuracy, grounded responses, and citation quality.

Prompt regression testing before every prompt or model release.

Token cost governance with budget controls and intelligent model routing.

Multi-LLM operations across OpenAI, Anthropic, Azure OpenAI, and self-hosted models.

Manufacturing & Operations AI

Quality inspection model monitoring as product lines, materials, and defect profiles evolve.

Predictive maintenance retraining triggered by equipment telemetry changes.

Supply chain ML pipeline monitoring with scheduled retraining for seasonal demand shifts.

Computer vision model registry for version-controlled multi-site deployments.

What we optimize for ?

The outcomes that define production AI operations

90%+

60%

10×

Zero

Why CaliberFocus?

What sets our MLOps consulting services apart?

Domain-Aware Monitoring

Generative AI Operations Expertise

Built Into Every Deployment

Vendor-Neutral Toolchain Expertise

Connected services

MLOps & LLMOps across your AI ecosystem

AI Engineering & Platform

Generative AI & LLM Solutions

ML & Predictive AI

AI Governance & Responsible AI

Keep your AI performing after deployment

Data & Feature
Layer

AI Engineering &
Platform

ML & Predictive
AI

Enhancing
Clinical Care,
Fewer Readmits!