Insight-Driven Tools. Built to Adapt.

Success with machine learning starts by asking the right questions—not just building advanced models. Our machine learning solutions are designed to help businesses turn raw data into smart decisions, real-time insights, and practical improvements across operations.

Whether you're optimizing supply chains, automating decision workflows, or forecasting demand, we develop machine learning and analytics strategies that are transparent, scalable, and purpose-built for your business.

How We Deliver Value

We focus on results—not just theory. With every engagement, we prioritize clarity, speed to value, and ease of adoption for your internal teams.

Our clients benefit from:

Fewer manual steps and faster workflows

Models that produce insights—not just dashboards

Tailored solutions that grow with your data and users

Predictive analytics that improve operations and planning

Thoughtful change management to ease team adoption

A framework to scale machine learning without infrastructure gaps

What We Do Best

We apply machine learning in ways that support real work—not just technical demos. Every deployment is tailored to your data, your teams, and your goals.

Here’s what we deliver:

End-to-end AI ML development services designed around business operations
Custom AI ML solutions that align with your technical environment and priorities
Real-world use of machine learning in business analytics for smarter decision-making
Scalable pipelines and models powered by AWS, GCP, and hybrid platforms
Clear, actionable machine learning analysis for data-backed insights
Seamless integration of AI and machine learning in business processes
Automation that reduces manual effort while increasing precision
Custom tools that support AI and learning and development across teams
Full support for post-launch model tuning, governance, and optimization

Industries We Support

We understand that machine learning solutions in business vary by industry. We tailor our approach to suit regulatory needs, user demands, and operational complexity in:

Healthcare

Patient risk scoring, treatment recommendation models

Finance

Fraud detection, credit scoring, customer segmentation

SaaS

Feature prediction, churn modeling, usage forecasting

Logistics

Real-time inventory tracking, route optimization, forecasting

Technology Stack & Tools

We build using modern tools and open frameworks so your models remain portable and future-proof.

Frameworks

PyTorch, TensorFlow, Scikit-learn

Data Flow

Kafka, Airflow, Databricks, Snowflake

Cloud

AWS SageMaker, GCP Vertex AI, Azure Machine Learning

Visualization

Looker, Power BI, Tableau

MLOps

MLflow, Kubernetes, CI/CD Pipelines

Our Approach

Transparent. Focused. Effective.

You don’t need to be a data scientist to understand our process. We work in the open—with full context and collaboration.

Discovery

Map business goals to machine learning opportunities

Design

Select the right models, tools, and cloud platforms

Build

Develop, test, and validate with clean, structured data

Deploy

Launch with full documentation and user training

Refine

Improve over time based on feedback and metrics

Why Stratis Cloud?

We believe machine learning solutions should support business-not become the business. Our solutions are designed to work alongside your teams, tools, and timeline.

Why clients trust us:

Deep experience delivering AI and ML solutions across industries

Transparent planning and delivery—no overcomplication

Support for in-house handoff or long-term partnership

Proven success with scaling AI solutions in business settings

Tools that your team can manage without relying on specialists

Why Stratis Cloud?

We don’t ship black-box models-we build artificial intelligence solutions you can trust and understand. Our teams stay involved throughout the project lifecycle, ensuring that what we build actually fits into your workflows.

Here’s what sets us apart:

01 Deep experience with AI in enterprise and mid-sized environments
02 Transparent, milestone-driven delivery approach
03 Custom solutions built around your infrastructure and data readiness
04 Support for scaling across AWS, GCP, and Azure
05 Long-term partnerships—not one-and-done projects

FAQs

What Our Clients Ask

Yes. We often use external data enrichment or rule-based models where training data is limited.

Absolutely-we build with deployment and scaling in mind from day one.

Most projects reach MVP within 6-8 weeks, depending on complexity and scope.

Yes. We avoid black-box deployments and provide dashboards and explainability layers.

Machine Learning Solutions

Insight-Driven Tools. Built to Adapt.

How We Deliver Value

What We Do Best

Industries We Support

Healthcare

Finance

SaaS

Logistics

Technology Stack & Tools

Frameworks

Data Flow

Cloud

Visualization

MLOps

Our Approach

Transparent. Focused. Effective.

Discovery

Design

Build

Deploy

Refine

Why Stratis Cloud?

Why Stratis Cloud?

FAQs

What Our Clients Ask

Can you help with small datasets?

Can you help with small datasets?

Do you support MLOps or DevOps integration?

Do you support MLOps or DevOps integration?

How long does it take to go live?

How long does it take to go live?

Can we see what the models are doing?

Can we see what the models are doing?

Let’s Talk About Your Goals

Follow Us On

Other Links

Our Services

Connect With Us

Open Hours:

Our Gallery

Open Hours: