MLOps & Governance-as-a-Service
Run your AI like a governed service, not a science project.
From "it works on a laptop" to distinct, defensible MLOps for enterprises. We deliver managed enterprise MLOps services, monitoring, drift detection, and the MLOps compliance that regulated teams need to keep models, agents, and workflows reliable 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.
For mid-market healthcare, life sciences & regulated businesses
- Model health
- 98.2%
- Drift risk
- Low
- SLA adherence
- 99.7%
- Open incidents
- 0
across the governed portfolio
monitored continuously
against agreed thresholds
with runbooks on standby
All systems operational, every change logged, owned, and audit-ready.
Under management
The reality
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, hand-rolled, undocumented, and impossible to repeat.
No monitoring
Issues are discovered only when users complain or auditors start asking questions.
No rollback strategy
Manual fixes, hidden dependencies, and crossed fingers when something breaks.
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?
- No single view of which models are live where.
- Drift and quality issues noticed only when users complain.
- Compliance and audit teams are nervous about AI systems.
- Engineers are re-deploying models manually each time.
- No clear rollback strategy when things break.
- Documentation is scattered or nonexistent.
Our GaaS offering exists to prevent your AI stack from becoming a pilot graveyard.
What's included
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.
Your AI journey
Where GaaS fits, and where you join us.
Whether you're just starting or already have models in production, we meet you where you are.
Stage 1
AI Readiness & Governance Assessment
Map your maturity, risks, and opportunities.
Stage 2
Build: LLMs & Agentic Workflows
Design and implement custom models and agentic automation.
Stage 3
You are hereRun & 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.
Your stack
Designed for your stack, not ours.
We operate on top of your environment, AWS, Azure, Databricks, your existing MLOps tooling. Pick a layer to see what we plug into and how we keep it governed.
Cloud & infrastructure
We operate inside the cloud you already run, aligned to your identity, networking, and security controls, rather than dragging you into an unfamiliar ecosystem.
We work with
- AWS
- Azure
- GCP
How engagements run
A structured way to keep AI reliable and governed.
Phase 01
Onboarding & Baseline
We inventory your models, workflows, and environments. We define SLOs/SLAs, metrics, and alert thresholds with your team, and agree on incident and escalation paths.
Phase 02
Day-to-Day Operations
We monitor key metrics and logs, respond to alerts, coordinate with your team, and run documented runbooks. We keep documentation up to date.
Phase 03
Improvement Cycles
Monthly or quarterly reviews with stakeholders. We identify new risks, opportunities, and tuning paths, and roadmap new features or deprecations.
Phase 04
Expansion
We add new models and workflows into the governed portfolio over time, scaling operations as your AI footprint grows.
Governance
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
Who changed what, when, and why.
We emphasize explainability of operations, every change captured in logs and documentation.
Your compliance and audit teams get the visibility they need to trust and defend your AI systems.
Who it's for
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 AssessmentWhat you get
Outcomes you can expect from GaaS.
Move from ad-hoc scripts and undocumented behavior to governed, observable AI services.
98.2%
model health across the governed portfolio
99.7%
SLA adherence against agreed thresholds
0
open incidents, caught early, not in an audit
Representative operating posture for a governed AI portfolio under management.
Fewer Surprises
Issues in performance, drift, or availability are caught early.
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.
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.
How we price
Flexible, retainer-first engagements.
MLOps & GaaS is typically structured as a retainer based on the number and complexity of models and 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 engagementsStraight answers
Common questions about MLOps & Governance-as-a-Service
Do you host our models, or use our infrastructure?
We prefer to operate on your infrastructure whenever possible. This keeps your data in your governed environment and avoids vendor lock-in. We can work with AWS, Azure, Databricks, or your on-prem setup.
Can you work with our existing MLOps / monitoring tools?
Yes. We integrate with existing tools where practical, MLflow, Weights & Biases, your CI/CD pipelines, monitoring stacks, and more. We're not about forcing rip-and-replace; we augment what you have.
How do you handle PHI/PII in logs and monitoring?
Logs and monitoring are designed with data minimization and privacy in mind. We avoid logging sensitive content where possible, use redaction patterns, and ensure access controls match your compliance requirements.
Can we start small with just one critical workflow?
Absolutely. Starting with one or two workflows or models is common. This lets us prove value and build trust before expanding scope. Many clients grow the portfolio over time as confidence increases.
What happens if we decide to bring operations fully in-house later?
We document everything, runbooks, architectures, configurations, procedures, so you can internalize operations over time if desired. Our goal is to make you more capable, not dependent.
Go deeper
Related resources on MLOps, governance, and AI operations.
Start here
Want your AI to run like a real, governed service?
We keep models, agents, and workflows reliable, observable, and audit-ready. Bring your current AI stack to a 30-minute working session, no commitment required.
+1-732-433-5564 · info@kriv.ai · East Brunswick, NJ
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