Build Smarter Solutions with Machine Learning - Deployed

Machine Learning

Machine Learning Solutions Built for Scalable Business Impact

Turn your data into predictive insights, intelligent automation, and production-ready ML systems that improve decisions,
reduce risk, and create measurable business value.

Do these machine learning challenges sound familiar?

Data is scattered, inconsistent, or incomplete

Machine learning models need clean, connected, and reliable data. When data lives across disconnected systems or lacks quality controls, model accuracy and usability become difficult to trust.

Use cases are not tied to measurable business value

Many ML projects start with a technical idea instead of a clear business outcome. Without defined KPIs, success metrics, and ROI expectations, it becomes hard to prove value or prioritize the right use cases.

Models perform well in testing but fail in real workflows

A model can look accurate in a controlled environment but still struggle when deployed into applications, dashboards, APIs, or business processes. Integration, latency, security, and adoption often become the real blockers.

Model performance declines after deployment

Customer behavior, market conditions, and operational data change over time. Without monitoring, drift detection, retraining, and governance, ML models can become less accurate and harder to rely on.

Build Machine Learning solutions with confidence

Business-first ML planning

We prioritize machine learning use cases around measurable outcomes, so every initiative is tied to clear business value.

Data foundation before model development

We help prepare the pipelines, quality controls, and architecture needed to build accurate and reliable ML systems.

Production-ready delivery

We design models for real-world deployment, integration, monitoring, and long-term use, not just experimentation.

Governance built in

We support explainability, versioning, drift monitoring, compliance, and responsible model operations from the start.

Machine Learning Solutions Built for Real-World Use

Machine Learning

ML Strategy and Consulting

Identify high-value use cases, assess feasibility, define success metrics, and create a roadmap that connects machine learning investments to measurable business outcomes.

Machine Learning

Data Engineering and Feature Engineering

Prepare reliable datasets, build data pipelines, improve data quality, and create features that help models learn from the right business signals.

Machine Learning

Custom ML Model Development

Build machine learning models for classification, regression, forecasting, recommendation engines, anomaly detection, optimization, and decision support.

Machine Learning

Predictive Analytics Solutions

Use historical and real-time data to forecast demand, identify churn risks, detect fraud, predict maintenance needs, and improve operational planning.

Machine Learning

NLP and Computer Vision Solutions

Automate text, document, image, and visual workflows using natural language processing, document intelligence, image classification, object detection, and visual inspection models.

Machine Learning

Model Deployment and Integration

Deploy models into applications, APIs, dashboards, business workflows, and cloud environments so insights can be used where decisions happen.

Machine Learning

MLOps and Governance

Set up model versioning, CI/CD pipelines, monitoring, drift detection, retraining workflows, explainability, and governance controls for reliable ML operations.

Machine Learning

Managed ML Services and Continuous Optimization

Monitor, tune, retrain, and improve models over time as your data, users, and business conditions evolve.

How we deliver ML solutions

01

01 Discovery and planning

We start by understanding your business goals, operational challenges, available data, and potential ML opportunities. Then we prioritize use cases based on business value, technical feasibility, risk, and expected ROI.

Deliverables: Use case shortlist, ML readiness report, success KPI map, implementation roadmap.

02 Data foundation and architecture

We assess your data sources, quality, accessibility, infrastructure, and governance needs. Then we design the data pipelines and architecture required to support reliable model development.

Deliverables: Data audit, architecture blueprint, pipeline plan, data quality recommendations.

03 Model development & deployment

We build, train, test, and validate ML models against defined business objectives. Once validated, we deploy them into the right environment and integrate them with your applications, workflows, or reporting systems.

Deliverables: Trained model, validation report, deployment plan, integration documentation.

04 MLOps, monitoring, and governance

We set up the operational layer needed to manage models after deployment, including monitoring, drift detection, version control, retraining workflows, explainability, and governance.

Deliverables: Monitoring dashboard, model registry, governance framework, retraining plan.

05 Scale, optimize, and evolve

We help improve model performance, expand successful use cases, automate recurring workflows, and support long-term ML maturity as your business grows.

Deliverables: Optimization roadmap, performance reports, scaling plan, team enablement support.

How we deliver ML solutions

step 1

Discovery and planning

Deliverables: Use case shortlist, ML readiness report, success KPI map, implementation roadmap.

step 2

Data foundation and architecture

We assess your data sources, quality, accessibility, infrastructure, and governance needs. Then we design the data pipelines and architecture required to support reliable model development.

Deliverables: Data audit, architecture blueprint, pipeline plan, data quality recommendations.

step 3

Model development & deployment

Deliverables: Trained model, validation report, deployment plan, integration documentation.

step 4

MLOps, monitoring, and governance

We set up the operational layer needed to manage models after deployment, including monitoring, drift detection, version control, retraining workflows, explainability, and governance.

Deliverables: Monitoring dashboard, model registry, governance framework, retraining plan.

step 5

Scale, optimize, and evolve

We help improve model performance, expand successful use cases, automate recurring workflows, and support long-term ML maturity as your business grows.

Deliverables: Optimization roadmap, performance reports, scaling plan, team enablement support.

Success stories in spotlight

A field-service marketplace that turns technician matching into one secure mobile journey

Field Services & Marketplaces Marketplace & Mobile Mobile Marketplace

Laptop and compact tools representing a technician marketplace

View case study

Deployed understood the marketplace vision from day one and built a product that connects technicians and customers in one seamless mobile experience.

Andreas

A calmer first step into dental care through guided assessment and reassuring education

Digital Health Healthcare & Mobile Healthcare Mobile Application

Patient receiving a calm, guided clinical treatment

View case study

Deployed created an experience that feels supportive from the very first screen — exactly the right tone for patients dealing with dental anxiety.

Hans-Jörg Wolff

Dental scoring and treatment information brought into one secure, transparent journey

Dental Technology Healthcare & Data Dental Scoring Platform

Clear dental aligners in a protective case

View case study

The planning and backend work gave us a clear, secure foundation that we can confidently build the scoring features on top of.

Detlef Schultz

Safer dog walks through live video, GPS routes, and incident-ready recording

Pet Safety & Mobility Connected Safety Connected Camera & Tracking Application

Dog wearing a safety harness while exploring a mountain trail

View case study

Deployed understood the urgency behind pet safety and planned a product that connects live video, location, and incident recording into one reliable app.

Tom Norberg

Personalized self-hypnosis sessions designed to fit each listener’s goals and routine

Digital Wellness AI & Wellness AI-Personalized Audio Application

Person relaxing with wireless headphones during a guided audio session

View case study

They mapped out a clear path from concept to launch, and the personalization approach makes every session feel genuinely tailored.

Harald Tröstl

Build with ML experts who understand your business.

Talk to our ML experts today. Let’s explore your data, define high-impact use-cases,
and build a roadmap for intelligent, scalable machine learning.

96_%

Client retention rate

100₊

Machine learning projects delivered

Build an ML strategy that delivers

Prioritize the right use cases, assess your data readiness, and define a clear path from machine learning idea to production-ready solution.

Tools & technologies

Tracking & Model Registry
Workflow Orchestration
Data Versioning
Model Deployment
Feature Stores

Mlflow

LangSmith

Kubeflow

Apache Airflows

Dagster

DVC (Data Version Control)

Pachyderm

Lakefs

Seldon Core

AWS Sagemaker

Hopsworks

Quadrants

Start Your Machine Learning Journey With a Clear, Actionable Strategy

“Deployed understood the marketplace vision from day one and built a product that connects technicians and customers in one seamless mobile experience.”

Andreas

XPERTS Marketplace · Mobile Marketplace

“Deployed created an experience that feels supportive from the very first screen — exactly the right tone for patients dealing with dental anxiety.”

Hans-Jörg Wolff

Dental Anxiety Adieu App · Healthcare Mobile Application

“The planning and backend work gave us a clear, secure foundation that we can confidently build the scoring features on top of.”

Detlef Schultz

Dental Score App · Dental Scoring Platform

“Deployed understood the urgency behind pet safety and planned a product that connects live video, location, and incident recording into one reliable app.”

Tom Norberg

DogCam Safety App · Connected Camera & Tracking Application

“They mapped out a clear path from concept to launch, and the personalization approach makes every session feel genuinely tailored.”

Harald Tröstl

AI-Powered Personalized Hypnosis App · AI-Personalized Audio Application

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“Deployed understood the marketplace vision from day one and built a product that connects technicians and customers in one seamless mobile experience.”

Andreas

XPERTS Marketplace · Mobile Marketplace

“Deployed created an experience that feels supportive from the very first screen — exactly the right tone for patients dealing with dental anxiety.”

Hans-Jörg Wolff

Dental Anxiety Adieu App · Healthcare Mobile Application

“The planning and backend work gave us a clear, secure foundation that we can confidently build the scoring features on top of.”

Detlef Schultz

Dental Score App · Dental Scoring Platform

“Deployed understood the urgency behind pet safety and planned a product that connects live video, location, and incident recording into one reliable app.”

Tom Norberg

DogCam Safety App · Connected Camera & Tracking Application

“They mapped out a clear path from concept to launch, and the personalization approach makes every session feel genuinely tailored.”

Harald Tröstl

AI-Powered Personalized Hypnosis App · AI-Personalized Audio Application

Frequently asked questions

What are machine learning solutions?

Machine learning solutions use data, algorithms, and automated learning systems to identify patterns, make predictions, recommend actions, and improve business decisions.

How do I know if my business is ready for machine learning?

Readiness depends on your data quality, business goals, infrastructure, internal capabilities, and use-case clarity. A readiness assessment helps identify what is feasible and what needs to be improved first.

What types of ML models can Deployed build?

Deployed can build models for forecasting, classification, regression, recommendations, anomaly detection, churn prediction, fraud detection, NLP, computer vision, and optimization.

How long does a machine learning project take?

Timelines depend on data readiness, model complexity, integrations, and deployment requirements. A simple prototype may take weeks, while production-ready ML systems can take several months.

What data do we need to start?

You need relevant historical or real-time data connected to the business problem you want to solve. This may include customer data, transaction data, operational data, product data, text, images, logs, or sensor data.

How do you deploy ML models into production?

We deploy models through APIs, batch pipelines, cloud services, applications, dashboards, or embedded workflows, depending on how the model will be used.

How do you monitor model performance after deployment?

We monitor accuracy, drift, latency, data quality, usage, errors, and business KPIs to ensure the model continues performing as expected.

How do you manage model drift and retraining?

We set up drift detection, retraining triggers, model versioning, validation workflows, and monitoring dashboards to keep models accurate over time.

How do you ensure ML governance, security, and explainability?

We apply access controls, documentation, model lineage, versioning, explainability methods, audit trails, and governance practices based on your compliance needs.

Can Deployed support us after the model is deployed?

Yes. Deployed can provide managed ML support, performance monitoring, retraining, optimization, issue resolution, and roadmap improvements after deployment.

What KPIs should we track for ML success?

Common KPIs include prediction accuracy, automation rate, cost savings, revenue lift, churn reduction, fraud reduction, operational efficiency, model latency, uptime, and business adoption.

Machine Learning Solutions Built for Scalable Business Impact

Do these machine learning challenges sound familiar?

Data is scattered, inconsistent, or incomplete

Use cases are not tied to measurable business value

Models perform well in testing but fail in real workflows

Model performance declines after deployment

Business-first ML planning

Data foundation before model development

Production-ready delivery

Governance built in

Machine Learning Solutions Built for Real-World Use

Machine Learning

ML Strategy and Consulting

Machine Learning

Data Engineering and Feature Engineering

Machine Learning

Custom ML Model Development

Machine Learning

Predictive Analytics Solutions

Machine Learning

NLP and Computer Vision Solutions

Machine Learning

Model Deployment and Integration

Machine Learning

MLOps and Governance

Machine Learning

Managed ML Services and Continuous Optimization

How we deliver ML solutions

01

How we deliver ML solutions

Discovery and planning

Data foundation and architecture

Model development & deployment

MLOps, monitoring, and governance

Scale, optimize, and evolve

Success stories in spotlight

A field-service marketplace that turns technician matching into one secure mobile journey

Andreas

A calmer first step into dental care through guided assessment and reassuring education

Hans-Jörg Wolff

Dental scoring and treatment information brought into one secure, transparent journey

Detlef Schultz

Safer dog walks through live video, GPS routes, and incident-ready recording

Tom Norberg

Personalized self-hypnosis sessions designed to fit each listener’s goals and routine

Harald Tröstl

Build with ML experts who understand your business.

Talk to our ML experts today. Let’s explore your data, define high-impact use-cases, and build a roadmap for intelligent, scalable machine learning.

96%

100+

Build an ML strategy that delivers

Tools & technologies

Start Your Machine Learning Journey With a Clear, Actionable Strategy

Andreas

Hans-Jörg Wolff

Detlef Schultz

Tom Norberg

Harald Tröstl

Andreas

Hans-Jörg Wolff

Detlef Schultz

Tom Norberg

Harald Tröstl

Frequently asked questions

Latest insights & resources

Designing for Anxious Users: UX Lessons From Digital Health

Building Trustworthy AI Verification

How AI Agents Are Reshaping Software Engineering

Data Engineering Comes Before AI

Architecting a Two-Sided Mobile Marketplace

A CI/CD Pipeline That Fits a Two-Person Team

Talk to our ML experts today. Let’s explore your data, define high-impact use-cases,
and build a roadmap for intelligent, scalable machine learning.

96_%

100₊