MLOps Governance & Compliance Services
From "laptop" to distinct MLOps for enterprises. We offer enterprise MLOps services ensuring MLOps compliance and regulated MLOps at scale.
- Monitor ML model governance, agents, and workflows with clear ownership.
- Detect drift, anomalies, and failures before they become incidents.
- Align AI model governance with your security, compliance, and audit needs.
- Free your teams from firefighting so they can focus on new value.
Operations Dashboard
Real-time monitoring98.2%
Low
99.7%
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Capabilities:
Most AI Value Dies Between Pilot and Production
Pilots are easy. Production is where risk appears—and where value is either captured or lost.
The pilot trap: Organizations build promising AI experiments, demo them to leadership, and then watch them languish. Moving to production exposes problems that didn't exist in the lab:
- No standardized deployment patterns—each model is a snowflake.
- No monitoring—issues discovered only when users complain or auditors ask questions.
- No rollback strategy—manual fixes, hidden dependencies, and crossed fingers.
In healthcare and other regulated environments, an unmonitored model touching PHI/PII is a risk, not an asset. Audit and incident response expectations are high—and getting higher.
Does this sound familiar?
Our GaaS offering exists to prevent your AI stack from becoming a pilot graveyard.
What MLOps & Governance-as-a-Service Includes
Comprehensive operational support for your AI systems.
Deployment & CI/CD/CT
Standardized patterns for deploying models, agents, and workflows with confidence.
- Versioning, rollback, and automated testing
- Integrations with your existing CI/CD tooling
- Continuous training pipelines where needed
Monitoring & Observability
Track performance, latency, errors, and usage across your AI portfolio.
- Monitor data drift and model behavior
- Alerts when thresholds are breached
- Centralized dashboards for visibility
Governance, Risk & Compliance
Logging, access control, and audit support for regulated environments.
- Inputs, outputs, and decisions logged for audit
- Role-based access and permission management
- Alignment with frameworks like NIST AI RMF
Incident Response & Improvement
Defined procedures when things break, plus structured feedback loops.
- Documented runbooks and escalation paths
- Regular reviews with domain experts
- Continuous tuning and optimization
Start with a small portfolio of models or agents and grow over time as your AI footprint expands.
Where GaaS Fits in Your AI Journey
Whether you're just starting or already have models in production, we meet you where you are.
Build: LLMs & Agentic Workflows
Design and implement custom models and agentic automation.
Run & Govern: MLOps & GaaS
Keep everything running, monitored, and compliant.
Some clients come to us at Stage 3, with existing models that need to be stabilized and governed. Others move through all three stages with our team.
Designed for Your Stack, Not Ours
We operate on top of your environment—AWS, Azure, Databricks, your existing MLOps tooling. We prefer using your infrastructure instead of dragging you into an unfamiliar ecosystem.
Cloud
AWS
Azure
GCP
Data
Databricks
Snowflake
LLMs
OpenAI
Anthropic
Azure OpenAI
Automation
Zapier
n8n
Power Automate
Copilot
Monitoring
Custom Dashboards
Alerting
Logging
Vector DBs
Pinecone
Weaviate
Chroma
How Our GaaS Engagements Run
A structured approach to keeping your AI systems reliable and governed.
Onboarding & Baseline
We inventory your models, workflows, and environments. We define SLOs/SLAs, metrics, and alert thresholds with your team. We agree on incident and escalation paths.
Day-to-Day Operations
We monitor key metrics and logs. We respond to alerts, coordinate with your team, and handle documented runbooks. We keep documentation up to date.
Improvement Cycles
Monthly or quarterly reviews with stakeholders. Identify new risks, opportunities, and tuning paths. Roadmap new features or deprecations.
Expansion
Add new models and workflows into the governed portfolio over time. Scale operations as your AI footprint grows.
Governance You Can Explain to Your Board
Clear controls, documentation, and audit support for regulated environments.
Governance Practices
- Clear mapping of who owns which models and workflows.
- Documented controls over access, changes, and approvals.
- Support for internal audits and external regulators.
- Incident response procedures and escalation paths.
- Regular governance reviews with stakeholders.
Transparency by Design
We emphasize explainability of operations: who changed what, when, and why—captured in logs and documentation.
Your compliance and audit teams get the visibility they need to trust and defend your AI systems.
Who Benefits Most from GaaS
Organizations ready to move from experimental AI to reliable, governed production.
Organizations
- Healthcare providers, life sciences, and regulated mid-market orgs
- Organizations with at least a few models or agents in use or in pilot
- Multi-system workflows (EHR, CRM, ticketing, data warehouses, etc.)
- Teams looking to standardize and govern their AI portfolio
Key Stakeholders
CTO / CIO and Engineering
Need predictable, governed AI operations without growing headcount too fast.
Heads of Data Science / Analytics
Want reliable deployment and monitoring so they can focus on modeling.
Compliance / Risk Leaders
Need assurance that AI systems can be explained and audited.
If you only have early experiments, you may be better served starting with an AI Readiness Assessment.
Learn about AI Readiness AssessmentOutcomes You Can Expect from GaaS
Move from ad-hoc scripts and undocumented behavior to governed, observable AI services.
Fewer Surprises
Issues in performance, drift, or availability are caught early—not during an audit or outage.
Shared Visibility
IT, data, and compliance teams see the same dashboards and documentation.
Lower Operational Burden
Your senior engineers and data scientists can focus on new value, not constant firefighting.
Stronger Compliance Posture
Demonstrate control and oversight of AI systems to regulators, customers, and internal leadership.
Before
Ad-hoc scripts, undocumented behavior
After
Governed, observable, documented AI services
Flexible, Retainer-First Engagements
MLOps & GaaS is typically structured as a retainer based on the number and complexity of models/workflows under management.
We usually begin with a smaller scope and expand as trust and portfolio size grow. This keeps engagements predictable and aligned with the value we deliver.
Learn how we price engagementsCommon Questions
Quick answers about MLOps & Governance-as-a-Service.
Related Resources
Go deeper on MLOps, governance, and AI operations.
From Pilot to Production: Why Most AI Initiatives Stall
Common patterns that keep AI experiments from becoming real, governed services—and how to break the cycle.
View resourceAI Risk & Governance Frameworks for Regulated Industries
A practical guide to NIST AI RMF and related frameworks in healthcare and life sciences.
View resourceHardening Agentic Workflows for a Mid-Sized Hospital
How we moved scattered automation pilots into governed, monitored production services.
View resourceWant your AI to run like a real, governed service?
We help you keep models, agents, and workflows reliable, observable, and audit-ready. Start with a quick conversation—no commitment required.