Models Stuck in Notebooks
Models that work in testing never make it into production because there's no repeatable way to deploy them.
MLOps & AI Infrastructure
MLOps Consulting Services That Keep AI Reliable at Scale
We build the pipelines, deployment automation and monitoring that keep your models and AI agents reliable, affordable and ready to scale, so they don't quietly break under real-world use.
Challenges We Solve
Why AI Struggles
in Production
Getting a model to work once is easy. Keeping it working for thousands of users is where most teams get stuck.
Performance Drops Silently
Without monitoring, accuracy drifts over time and no one notices until customers complain.
Data Science and Engineering Work Apart
Hand-offs between teams slow releases and create fragile, one-off scripts.
AI Costs Grow Out of Control
Unplanned infrastructure and inference usage push cloud bills far beyond budget.
Book A Call
Talk to an MLOps Expert
Talk to an MLOps Expert
Service
MLOps & AI Infrastructure Services
We build and manage the infrastructure, deployment pipelines, monitoring, governance, and optimization needed to run AI systems reliably in production.
MLOps Strategy & Assessment
We review your current setup and design a practical MLOps roadmap for your team and scale.
ML Model Deployment Services
Automated pipelines that take models from training to production safely and repeatably.
Machine Learning Model Monitoring Services
Track accuracy, drift, latency and cost, with alerts before problems reach users.
LLMOps Services
Prompt versioning, evaluation, guardrails and cost tracking for LLM and generative AI apps.
AI Infrastructure Engineering
Scalable cloud and GPU infrastructure on AWS, Azure or Google Cloud, designed for AI workloads.
AI Cost Optimization
Right-size infrastructure and model usage to cut spend without hurting performance.
Process
How We Build Your AI Infrastructure
A three-phase approach that moves you from one-off scripts to governed, repeatable AI operations.
Phase 1: Assess Infrastructure
Timeline: 1-2 weeks
We review your models, data pipelines, cloud setup and release process to find the biggest risks and bottlenecks.
Deliverables:
- Current-state infrastructure review
- Risk and bottleneck report
- Target MLOps architecture
- Tooling recommendations
- Prioritized implementation plan
Phase 2: Build the Pipeline
Timeline: 3–6
We build automated training, testing and deployment pipelines, plus monitoring for every model in production.
Deliverables:
- Automated CI/CD for models
- Model registry and versioning
- Monitoring and alerting dashboards
- Rollback and recovery process
- Infrastructure as code
Phase 3: Operate & Optimize
Timeline: 6 weeks and ongoing
We run, monitor and improve your AI infrastructure, keeping performance high and costs under control.
Deliverables:
- Ongoing monitoring and support
- Drift detection and retraining
- Monthly cost and performance reviews
- Security and access reviews
- Continuous improvement plan
Benefits
The Business Impact of MLOps
Faster Releases
Ship model updates in hours instead of weeks with automated pipelines.
Reliable AI in Production
Catch drift and failures early, before they affect customers.
Controlled Costs
See exactly what your AI infrastructure costs and where to save.
One Workflow for Every Team
Data scientists and engineers work from the same repeatable process.
Audit-Ready Operations
Versioning and logs show which model made which decision, and when.
Use Cases
MLOps Solutions for Your Industry
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
We use proven, production-grade tools and choose the right model for each job based on accuracy, cost and privacy.
FAQ
MLOps Consulting FAQs
What teams usually ask before investing in MLOps.
MLOps (machine learning operations) is the set of practices and tools used to deploy, monitor and maintain machine learning models in production. It matters because models degrade over time as data changes. MLOps automates releases, tracks performance and catches problems early, so AI stays accurate, reliable and cost-effective.
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.
DevOps automates how software code is built, tested and released. MLOps applies the same ideas to machine learning, but also manages data, model training, versioning and monitoring for accuracy and drift. Software behaves the same until code changes; models can get worse on their own as real-world data shifts.
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.
Package the model with its dependencies, test it against real data, then release it through an automated pipeline, usually as an API or batch job on scalable cloud infrastructure. After release, monitor accuracy, latency and cost, and keep a rollback plan and retraining process ready.
Your AI-Native Technology 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
Make Your AI Production-Ready
Tell us how your models run today. We'll show you what it takes to make them reliable, monitored and cost-efficient.
