AI & Machine Learning

Machine Learning for Businesses with DIVINT

Precise forecasts, for example, for demand

Compliance and audit-ready

Productive in just 6-10 weeks

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These companies trust DIVINT

How can AI and Machine Learning be used in your company?

With AI and data, your daily life becomes easier: routines run automatically, decisions are based on facts, and customers receive more suitable offers. We start small with a specific topic (e.g., early detection of churn or better demand planning), set up a simple data connection, and launch an initial solution live within 6-10 weeks, which we demonstrate its benefits with a comparison test.

Services

Our machine learning focus areas at a glance

Data Strategy & Use Case Prioritization

We identify value-creating use cases, assess data availability and ROI, and develop an actionable roadmap from MVP to rollout.

Data Strategy & Use Case Prioritization

We identify value-creating use cases, assess data availability and ROI, and develop an actionable roadmap from MVP to rollout.

Data Strategy & Use Case Prioritization

We identify value-creating use cases, assess data availability and ROI, and develop an actionable roadmap from MVP to rollout.

Data Preparation & Feature Engineering

From raw data collection through cleaning, labeling, and feature engineering to the training/validation pipeline – the foundation for robust models.

Data Preparation & Feature Engineering

From raw data collection through cleaning, labeling, and feature engineering to the training/validation pipeline – the foundation for robust models.

Data Preparation & Feature Engineering

From raw data collection through cleaning, labeling, and feature engineering to the training/validation pipeline – the foundation for robust models.

Model Development (Classical & Deep Learning)

Selection and training of suitable algorithms (e.g., Gradient Boosting, time series, NLP, computer vision, deep learning), including hyperparameter tuning and explainability.

Model Development (Classical & Deep Learning)

Selection and training of suitable algorithms (e.g., Gradient Boosting, time series, NLP, computer vision, deep learning), including hyperparameter tuning and explainability.

Model Development (Classical & Deep Learning)

Selection and training of suitable algorithms (e.g., Gradient Boosting, time series, NLP, computer vision, deep learning), including hyperparameter tuning and explainability.

MLOps & Integration

CI/CD for models, reproducible training runs, model registries, API/batch serving, and integration into existing applications and processes.

MLOps & Integration

CI/CD for models, reproducible training runs, model registries, API/batch serving, and integration into existing applications and processes.

MLOps & Integration

CI/CD for models, reproducible training runs, model registries, API/batch serving, and integration into existing applications and processes.

Monitoring, A/B Testing & Drift Detection

Continuous performance monitoring, fairness/bias checks, data and concept drift detection, as well as controlled A/B rollouts instead of a big bang approach.

Monitoring, A/B Testing & Drift Detection

Continuous performance monitoring, fairness/bias checks, data and concept drift detection, as well as controlled A/B rollouts instead of a big bang approach.

Monitoring, A/B Testing & Drift Detection

Continuous performance monitoring, fairness/bias checks, data and concept drift detection, as well as controlled A/B rollouts instead of a big bang approach.

Governance, Data Protection & Ethics

GDPR-compliant data processing, role/rights concepts, audit trails, model transparency, and responsible AI policies.

Governance, Data Protection & Ethics

GDPR-compliant data processing, role/rights concepts, audit trails, model transparency, and responsible AI policies.

Governance, Data Protection & Ethics

GDPR-compliant data processing, role/rights concepts, audit trails, model transparency, and responsible AI policies.

Your advantages

Why Machine Learning – and what happens without it?

With DIVINT as your partner, you benefit from clear advantages. Without machine learning, however, potential remains untapped and risks persist:

With DIVINT Machine Learning

Benefits

Automated processes and noticeable efficiency gains

More accurate predictions (demand, churn, risk) and better decisions

Personalized experiences & recommendations for higher conversion/NRR

Early detection of fraud/anomalies and reduced risk of damage

Scalable insights from structured & unstructured data

Measurable business impact through MVPs, A/B tests, and continuous optimization

Without Machine Learning

Disadvantages

Decisions Based on Intuition Rather Than Data

Manual, error-prone processes and lengthy lead times

Unused data potentials in logs, texts, images, sensors

Increased risks due to lack of anomaly and fraud detection

Reduced relevance for customers due to lack of personalization

Difficult to scale insights and competitive disadvantages

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FAQ

Frequently Asked Questions

What is Machine Learning?

An approach where algorithms learn patterns from data and make predictions/decisions without being explicitly programmed for each rule – from classics like Gradient Boosting to deep learning with neural networks.

What is Machine Learning?

For which company sizes is IT support beneficial?

For everyone – data maturity and clear business benefits are crucial. From around 50 employees/multiple systems, ML becomes particularly effective when data is centrally accessible.

For which company sizes is IT support beneficial?

For everyone – data maturity and clear business benefits are crucial. From around 50 employees/multiple systems, ML becomes particularly effective when data is centrally accessible.

How does an ML project start?

With a Data/Use Case Assessment: Examine the data situation, define success criteria (KPIs), and plan an MVP. Then quickly move to bring a first model into production and validate it using A/B testing.

How does an ML project start?

Are internal specialist departments involved?

Yes. Domain knowledge is crucial – departments define labels/KPIs, validate results, and take responsibility for the operational work with the outputs (e.g., next-best-action).

Are internal specialist departments involved?

Yes. Domain knowledge is crucial – departments define labels/KPIs, validate results, and take responsibility for the operational work with the outputs (e.g., next-best-action).

How long does the implementation take?

A focused MVP (use case, data pipeline, model, serving) is often realistically achievable in 6-10 weeks. Scaling and additional use cases follow iteratively.

How long does the implementation take?

A focused MVP (use case, data pipeline, model, serving) is often realistically achievable in 6-10 weeks. Scaling and additional use cases follow iteratively.

Are projects possible remotely as well?

Yes. Data access is performed securely, collaboration works in a hybrid/remote manner; workshops, reviews, and go-lives are designed flexibly.

Are projects possible remotely as well?

Yes. Data access is performed securely, collaboration works in a hybrid/remote manner; workshops, reviews, and go-lives are designed flexibly.

How is the service billed?

Transparent: Fixed price for assessment/MVP, time-and-material for expansion or retainer for operation, monitoring, and continuous optimization (MLOps).

How is the service billed?

Transparent: Fixed price for assessment/MVP, time-and-material for expansion or retainer for operation, monitoring, and continuous optimization (MLOps).

Which tools and technologies are supported?

Vendor-neutral: Data Warehouses/Lakehouses, ETL/ELT orchestration, feature stores, ML frameworks (e.g., for NLP/computer vision), model registries, as well as API/batch serving.

Which tools and technologies are supported?

Vendor-neutral: Data Warehouses/Lakehouses, ETL/ELT orchestration, feature stores, ML frameworks (e.g., for NLP/computer vision), model registries, as well as API/batch serving.

How does ML address data protection, bias, and compliance?

Through data minimization, pseudonymization, access controls, audit trails, fairness and bias checks, and explainable models - documented in a GDPR-compliant and auditable manner.

How does ML address data protection, bias, and compliance?

Through data minimization, pseudonymization, access controls, audit trails, fairness and bias checks, and explainable models - documented in a GDPR-compliant and auditable manner.

What results are realistic?

Quick wins through MVPs, followed by sustainable efficiency/revenue levers: less manual work, better predictions, higher conversion/retention, and reduced risks – measurable through clearly defined KPIs.