MLOps & LLMOps Services for Enterprise AI Systems

Deploy, monitor, and optimize AI in production — with engineering discipline that keeps every model accurate, cost-efficient, and reliable long after launch day.

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

87% of ML models never make it to production. Of those that do, most degrade silently within months — without anyone noticing until the business consequences are visible.

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 — and keep AI accurate, efficient, and trustworthy over time.

MLOps governs traditional ML models. LLMOps governs large language models. Both are essential in 2026.

MLOps

MLOps applies DevOps engineering principles to the ML lifecycle — bringing CI/CD, version control, automated testing, and monitoring to model development and deployment.

Experiment tracking and model versioning

Automated 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 disciplines unique to LLMs — prompt engineering governance, RAG quality management, hallucination monitoring, and the cost controls that make LLM economics work at scale.

Prompt versioning, testing, and regression tracking

LLM evaluation frameworks (accuracy, safety, cost)

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 everything between data and production

Manual ML workflows are the enemy of reliable production AI. We build automated pipelines that take models from experiment to production — with version control, automated testing, staged rollout, and rollback — so deployments are repeatable, auditable, and safe.

End-to-end training pipelines — data validation, feature engineering, model training, evaluation, and packaging automated
CI/CD for ML — automated testing on every model change: unit tests, integration tests, performance regression, and bias checks
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 detect data drift, concept drift, and performance degradation — generating alerts before business outcomes are affected, and triggering automated retraining when drift exceeds 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 LLMs need to stay trustworthy

LLMs in production fail in ways traditional ML doesn’t — hallucinations, prompt regressions, RAG quality degradation, and cost spirals. We build LLMOps frameworks that catch these failures systematically before they reach users or exceed budgets.

LLM evaluation pipelines — automated scoring on accuracy, groundedness, coherence, safety, 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-hardened

Pipeline Layer

What Gets Built Here

Data & Feature
Layer

Data validation, feature engineering, feature store management, and training dataset versioning. The foundation that determines model quality — garbage in, garbage out, no matter how good the pipeline.

Experiment & Training Layer

Experiment tracking, hyperparameter optimization, model training orchestration, and evaluation. Every experiment logged, compared, and reproducible — no more ‘which version produced that result?’

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

Staged rollout (canary, blue/green, shadow), traffic splitting, A/B testing infrastructure, and automated rollback triggers. Deployments that are safe by default — not accidents waiting to happen.

Monitoring & Alerting Layer

Real-time performance dashboards, drift detection, anomaly alerts, SLA monitoring, and PagerDuty/Slack integration. The first team to know about model degradation should be your engineers, not your clients.

Retraining & Feedback Layer

Automated retraining triggers, hard negative mining pipelines, human feedback integration (RLHF), and continuous improvement loops. Models that get better over time, not just at launch.

Vendor-agnostic. Best-tool-for-the-job. No forced migrations.

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

Concrete use cases drawn from live deployments across our client portfolio.

Healthcare AI Operations

Medical coding model monitoring — detect when payer rule changes cause accuracy degradation before claims are affected

Clinical NLP pipeline ops — monitor RAG retrieval quality and hallucination rates in clinical documentation systems

Prior auth model retraining — automated pipelines that update models as payer criteria and clinical protocols evolve

ImpactRCM.AI agent operations — continuous monitoring and optimization of live RCM agent performance metrics

Financial Services AI

Fraud detection model ops — detect concept drift as fraud patterns evolve, retrain before detection rates fall

Credit scoring pipeline — regulatory model validation, explainability reporting, and bias monitoring in production

LLM compliance monitoring — track hallucination and factual accuracy rates in regulatory document generation

Risk model CI/CD — automated revalidation and staged deployment for models in regulated environments

Enterprise LLMOps

RAG platform quality ops — track retrieval accuracy, answer groundedness, and citation quality across knowledge bases

Prompt regression testing — automated test suites that run on every prompt or model update before production

Token cost governance — per-team and per-use-case cost tracking with budget alerts and model routing optimization

Multi-LLM management — monitoring and performance comparison across OpenAI, Anthropic, Azure, and self-hosted models

Manufacturing & Operations AI

Quality inspection model ops — monitor accuracy drift as product lines, materials, and defect profiles evolve

Predictive maintenance retraining — automated pipelines triggered when equipment telemetry distributions shift

Supply chain ML pipeline — demand forecasting model monitoring with scheduled retraining against seasonal patterns

Computer vision model registry — versioned deployment management for multi-site visual inspection systems

90%+

Model accuracy maintained at 90-day post-deployment

60%

Reduction in LLM token costs via optimization

10×

Faster model deployment with automated CI/CD pipelines

Zero

Silent model failures with continuous monitoring in place

AI Engineering &
Platform

The infrastructure MLOps pipelines run on — compute, serving, and deployment architecture

Generative AI & LLM Solutions

LLMOps governs the production operation of every LLM system
we build.

ML & Predictive
AI

MLOps is what keeps every predictive model accurate and reliable after deployment.

AI Governance & Responsible AI

Governance frameworks that integrate with MLOps monitoring and audit pipelines.

Deployment is day one. Operations is everything after. Let’s build the systems that keep your AI working.

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 AI and machine learning development services that combine deep machine learning and deep learning expertise with production-grade MLOps. As a trusted machine learning service provider, we help organizations move models from experimentation to scalable production, delivering measurable business impact, accuracy, and long-term value.

MLOPS & LLMOPS

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

Why most AI systems degrade in production?

Data Drift

Concept Drift

LLM Degradation

MLOPS vs LLMOPS

Two disciplines. One shared goal: AI that stays accurate.

MLOps

Experiment tracking and model versioning

Automated 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 (accuracy, safety, cost)

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 MLOps & LLMOps pipeline

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 tool chain

Tools & platforms we work with

Where this works?

MLOps & LLMOps in action - by industry

Healthcare AI Operations

Medical coding model monitoring — detect when payer rule changes cause accuracy degradation before claims are affected

Clinical NLP pipeline ops — monitor RAG retrieval quality and hallucination rates in clinical documentation systems

Prior auth model retraining — automated pipelines that update models as payer criteria and clinical protocols evolve

ImpactRCM.AI agent operations — continuous monitoring and optimization of live RCM agent performance metrics

Financial Services AI

Fraud detection model ops — detect concept drift as fraud patterns evolve, retrain before detection rates fall

Credit scoring pipeline — regulatory model validation, explainability reporting, and bias monitoring in production

LLM compliance monitoring — track hallucination and factual accuracy rates in regulatory document generation

Risk model CI/CD — automated revalidation and staged deployment for models in regulated environments

Enterprise LLMOps

RAG platform quality ops — track retrieval accuracy, answer groundedness, and citation quality across knowledge bases

Prompt regression testing — automated test suites that run on every prompt or model update before production

Token cost governance — per-team and per-use-case cost tracking with budget alerts and model routing optimization

Multi-LLM management — monitoring and performance comparison across OpenAI, Anthropic, Azure, and self-hosted models

Manufacturing & Operations AI

Quality inspection model ops — monitor accuracy drift as product lines, materials, and defect profiles evolve

Predictive maintenance retraining — automated pipelines triggered when equipment telemetry distributions shift

Supply chain ML pipeline — demand forecasting model monitoring with scheduled retraining against seasonal patterns

Computer vision model registry — versioned deployment management for multi-site visual inspection systems

What we optimize for ?

The outcomes that define operational AI

90%+

60%

10×

Zero

Why CaliberFocus?

What makes our approach different?

Domain-Aware Monitoring

LLMOps as a First-Class Discipline

Built Into Every Deployment

Toolchain Depth Without Lock-In

Connected services

Everything MLOps & LLMOps keeps running

AI Engineering & Platform

Generative AI & LLM Solutions

ML & Predictive AI

AI Governance & Responsible AI

Is your AI running or just deployed?

Data & Feature
Layer

AI Engineering &
Platform

ML & Predictive
AI

Enhancing
Clinical Care,
Fewer Readmits!