LLMOps
Ship Faster. Break Less.
A cloud-neutral operating model to build, deploy, govern, and continuously improve ML, GenAI, and Agentic AI solutions.
Most AI initiatives don't stall because the model is bad — they stall because nobody can ship it safely. A prompt that worked perfectly in the demo drifts once it's live. A model's accuracy degrades quietly over months without anyone noticing. Nobody can say for certain which version answered a customer's question, or prove after the fact that it wasn't hallucinating. LLMOps is the operating discipline that closes that gap — the same rigor MLOps brought to predictive models, adapted for prompts, retrieval pipelines, and the agents built on top of large language models.
OUR NUMBERS
8
Stages across the LLMOps lifecycle
5
Phases in every delivery cycle
2
Disciplines, one governed platform
1
Operating model for ML + GenAI
Why It Matters
- AI pilots often fail simply because deployment is manual, inconsistent, and hard to repeat.
- Weak governance and unnoticed model drift quietly erode reliability over time.
- Unmanaged prompts and security gaps create real, avoidable operational risk.
- Unclear ownership makes accountability impossible the moment something goes wrong.
MLOps vs. LLMOps
Same Discipline, Different Playbook
Problem Framing
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MLOps: Predictive outcomes, labels, model risk tier
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LLMOps: User task, policy boundaries, safety risk tier
Data Lifecycle
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MLOps: Training data, features, drift management
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LLMOps: Knowledge corpus, embeddings, prompts, logs
Development
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MLOps: Feature engineering, hyperparameter tuning
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LLMOps: Prompt engineering, RAG design, agent routing
Deployment
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MLOps: Model endpoint, batch scoring, A/B tests
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LLMOps: Prompt/model versions, guardrails, rate limits
Operations
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MLOps: Data/model drift, degradation, retraining
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LLMOps: Cost per interaction, safety incidents, feedback
The LLMOps Lifecycle
Use Case & Risk Tier
Journey, policy, threat model, ROI
Data & Knowledge
Docs, APIs, embeddings, retrieval freshness
Prompt / Agent Design
Prompts, tools, memory, orchestration plan
Model Selection
Build vs buy, fine-tune, distill, benchmark
Evaluation & Safety
Golden sets, judges, red-team, hallucination tests
Release & Guardrails
Prompt versioning, filters, rate limits, canary
Operate & Optimize
Latency, cost, quality, routing, cache
Feedback & Improve
Human review, incidents, retraining, RAG refresh
Key Capabilities
LLM application architecture
Model selection and evaluation
Prompt engineering framework
Retrieval-Augmented Generation design
Vector database integration
Prompt versioning and testing
Model performance monitoring
Response quality evaluation
Hallucination control mechanisms
Security, governance, and access control
AI usage monitoring and auditability
How We Deliver It
Discover
Use cases, data, risk profile, and success metrics are defined before anything gets built.
Build
Feature, model, prompt, RAG, and agent pipelines are put together as reusable components.
Validate
Quality, security, bias, hallucination, and cost checks run before anything ships.
Release
CI/CD, approvals, model/prompt registry, and controlled deployment take it live.
Operate
Continuous monitoring, retraining, evaluation, optimization, and governance keep it healthy.
Where This Applies
Demand Forecasting
Fraud Detection
Churn Analysis
GenAI Assistants
Document Intelligence
Call Summarization
RAG Chatbots
Workflow Automation
Typical Deliverables
LLMOps architecture
Prompt management framework
RAG implementation design
Evaluation and testing framework
Monitoring and governance dashboard
Security and guardrail configuration
Operational runbook
Business Benefits
Governed AI Adoption
Every model and prompt ships through the same approval, audit, and access-control path.
Higher-Quality Responses
Structured evaluation and hallucination checks catch bad outputs before users see them.
Lower Operational Risk
Clear guardrails replace ad hoc prompt management and untracked model usage.
Full Traceability
Every release, retrain, and response is logged, versioned, and auditable.
Faster AI Delivery
Reusable pipelines take use cases from pilot to production faster.
Secure Data Integration
Enterprise data connects into RAG and agent workflows without compromising security.
Atlassian Platinum Solution Partner