Manual deployment scripts nobody trusts
Model updates get pushed by hand, with no rollback plan if something goes wrong in production.
MLOps & AI Infrastructure
Keep Your AI Systems Reliable Monitored, and Ready to Scale
Novatore Solutions builds the operational backbone behind production AI pipelines, deployment automation, and monitoring that keep models and agents reliable as usage grows. We help teams move past one-off scripts into governed, repeatable AI infrastructure that doesn't break quietly under real load.
Challenges
WHAT GETS IN THE WAY
CHALLENGES WE SOLVE
The reasons AI systems that work in a demo start breaking down in production.
No visibility into what the agent did
Without monitoring, accuracy quietly degrades over weeks or months before anyone notices something's off.
Inference costs that spiral unnoticed
Usage scales without cost tracking, and the first sign of a problem is a surprising cloud bill
AI usage nobody can audit
Without logging and access controls, no one can reconstruct who used a system, on what data, or why.
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Service
We Offer MLOps & AI Infrastructure Services
From first deployment pipeline to a fully governed, cost-visible AI operations layer your team can run confidently.
Model Deployment Pipelines
We build automated pipelines for deploying, versioning, and rolling back models and AI services safely.
AI Monitoring & Observability
We set up dashboards and alerting that track accuracy, latency, cost, and usage across AI features.
Data Pipeline Automation
We automate the data flows that feed models and agents, keeping inputs fresh and reliable.
AI Governance & Access Control
We implement permissioning, audit logs, and usage tracking so AI systems stay accountable as they scale.
Incident Response Readiness
We build runbooks and alerting so your team can react fast when a model misbehaves.
Cost & Performance Tuning
We optimize infrastructure choices to control inference and compute costs without sacrificing reliability.
Process
How We Build Your Custom AI Infrastructure
A three-phase engagement that moves from infrastructure audit to a reliable AI operations layer.
Phase 1: Assess Infrastructure
Timeline: 1-2 weeks
We review your current AI deployment setup, data pipelines, and monitoring gaps before recommending any changes.
Deliverables:
- Infrastructure audit
- Risk list
- Improvement plan
- Cost baseline
- Governance gap review
Phase 2: Build the Pipeline
Timeline: 3–6
We implement deployment automation, monitoring, and governance controls directly around your existing AI systems.
Deliverables:
- CI/CD for models
- Monitoring dashboard
- Access-control setup
- Data pipeline automation
- Rollback procedures
Phase 3: Operate & Optimize
Timeline: 6 weeks and ongoing
We support ongoing operations, tuning performance and cost as usage patterns evolve over time.
Deliverables:
- Operating runbook
- Monthly cost report
- Incident response plan
- Optimization backlog
- Quarterly review
Benefits
What You Gain
Zero Downtime Updates
Deploy model and agent updates without downtime or manual scripts run by hand.
Catch Drift Early
Spot accuracy or performance drift through monitoring, before a customer notices it first.
Auditable AI Usage
Keep AI usage logged and traceable for compliance review and internal governance.
Predictable Costs
Control compute and inference spend as usage scales, instead of a monthly surprise.
Built to Recover Fast
Runbooks and alerting mean incidents get caught and resolved before they compound.
Use Cases
MLOps Tailored To Your Industry And Business Goals
AI in Healthcare
Infrastructure built for auditability We build AI infrastructure for HealthTech platforms where every model decision touching patient data needs a clear audit trail.
Log every AI system interaction with patient-adjacent data.- Monitor model accuracy for clinical-support and intake features continuously.
- Build access controls that satisfy HIPAA-aware governance needs.
- Set up incident response specific to healthcare-context AI failures.
Case Studies
Proven Builds, Not Just Prototypes
Projects Of Novatore Solutions
InsureTech MLOps
Cova
Cova is an AI-based web application connecting insurance brokers with insurers. Novatore supported the infrastructure behind the platform, keeping the underlying AI matching logic reliable and monitored as broker and insurer usage grew over time.
OUTCOME
Monitoring and deployment practices that kept the AI matching engine stable as usage scaled across more brokers and insurers.
Challenge: The AI matching logic needed to stay reliable as more brokers and insurers came onto the platform.
Solution: Novatore built monitoring and deployment practices around the existing matching engine.
Result: Stable, monitored AI matching performance through platform growth.
FinTech MLOps
Credifi
Credifi runs AI-assisted underwriting features inside a Next.js platform for Australian finance professionals. Novatore's infrastructure work built deployment and monitoring practices for a financial-services context where reliability and traceability matter most.
OUTCOME
Deployment and monitoring practices that keep AI-assisted underwriting features stable and traceable for a regulated FinTech context.
Challenge: AI-assisted underwriting features needed infrastructure that could satisfy financial services reliability and audit expectations.
Solution: Novatore implemented deployment and monitoring practices built for a regulated context.
Result: Stable, traceable AI features supporting real underwriting workflows.
AI Agents MLOps
Hexagon CX
Hexagon CX runs an AI calling agent for continuous outbound lead generation. Novatore's infrastructure practices keep the conversational system monitored and reliable as it runs lead-generation conversations continuously, without manual oversight of every call
OUTCOME
Monitoring and deployment practices supporting reliable, continuous operation of an always-on AI calling workflow.
Challenge: An always-on calling agent needed infrastructure reliable enough to run without constant manual oversight.
Solution: Novatore built monitoring practices to support continuous, unattended operation.
Result: A calling workflow that runs reliably around the clock.
Tools & Technologies
Technologies We Build With
Our strategy engagements are grounded in the same tools our delivery teams use, so roadmaps reflect what's actually achievable with today's AI platforms.
FAQ
Frequently Asked Questions
What clients typically ask before investing in MLOps and AI infrastructure.
Yes. Even API-based AI features need monitoring, versioning, and cost controls as usage scales lighter, but necessary.
Yes, we build within AWS, Azure, or GCP depending on where your systems already live.
We set up automated evaluation checks and usage dashboards that flag drops in accuracy or response quality.
No, even a single production AI feature benefits from basic monitoring to avoid silent failures.
We build rollback procedures into the pipeline so a bad deployment reverses quickly, with the team alerted.
We build cost dashboards and tiering strategies so spend is visible and predictable, not a surprise.
Yes, we regularly onboard existing AI infrastructure, audit it for gaps, and bring it to a governed standard.
Your Go To Business Partner
Why Novatore
An AI-Native technology partner
We combine strategy and delivery to build what actually ships, from roadmap to production.
Teams Which Deliver
Strategists, engineers, and QA specialists work as one team to turn plans into software fast.
Full-stack technical depth
React, Next.js, Node.js, Flutter, and Laravel expertise, paired with modern AI and cloud tooling, covers the full delivery lifecycle.
US, UK, and Canadian Clients
We work inside the timelines, communication norms, and compliance expectations our North American and UK clients require.
Backed by 38+ shipped platforms
38+ products delivered from concept to production across HealthTech, FinTech, retail, and SaaS, with proven experience.
We Are Just One Click Away
Stop guessing Where To Start Explore Our Services
Book a free 30-minute call with Novatore Solutions. No roadmap pitch, no build commitment, just straight answers
