Machine Learning Models That Answer a Business Question
A model is worth building when it improves a decision you already make. Most business uses fall into four groups.
Forecasting
Demand, revenue, staffing, or inventory predicted from history.
Classification
Tickets, transactions, or leads sorted and prioritized.
Recommendations
Products, content, or next actions suggested per customer.
Anomaly detection
Unusual transactions, readings, or behavior flagged early.
Machine Learning Services We Provide
Our machine learning services cover the whole path from a business question to a model your teams use every day. Most engagements combine several of them.
Use-case selection. We rank candidate uses by value, data readiness and risk, and agree how success will be measured before anything is built.
Data preparation and feature engineering. We check history, quality and ownership, then build the pipelines and features the model depends on.
Custom model development. Forecasting, classification, recommendation and anomaly detection models, tested on data they have not seen and compared with your current method.
Deployment and MLOps. Models ship as services your applications call, with versioning, monitoring and retraining instead of a one-off notebook.
Integration with your systems. Predictions arrive where decisions are made: your ERP, CRM, ecommerce platform or internal tools.
Explore data integration services
Your Data Decides What the Model Can Do
Before any modeling, the data is checked for volume, history, quality, and ownership. Cognativ has built data platforms that cut manual reconciliation 70% for a health insurer.
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The decision
Who acts on the prediction and how the result is measured.
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Model and evaluation
Candidate models compared against the current baseline.
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Features
The signals that plausibly drive the outcome.
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Clean, owned data
Enough history, consistent definitions, and a clear owner.
From Notebook to Production Machine Learning
A model that only runs on a laptop changes nothing. We ship it as a service your applications call, with monitoring for drift and a retraining schedule.
Data pipelines
Fresh, validated data delivered on schedule.
Model service
An API your applications and reports call.
Monitoring
Accuracy, drift, and cost tracked after launch.
Predictions your teams can rely on
Request a free machine learning consultation
Machine Learning or Generative AI?
Predicting a number or a category from structured data is classic machine learning. Reading, writing, or answering questions in language is generative AI. Many products use both, and the choice follows the task.
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Machine Learning Development Questions
What to expect before you start.
What do machine learning development services include?
Problem framing, data assessment, feature work, model building and evaluation, deployment as a service, and monitoring after launch.
How much data do we need?
It depends on the question. A data assessment in discovery tells you whether the history you have is enough before modeling starts.
How do we know the model works?
It is tested on data it has not seen and compared with your current method, using the business measure agreed at the start.
Can the model run in our cloud?
Yes. Models are deployed in your environment or a managed one, with access controls and logging.
What happens when the data changes?
Monitoring detects drift, and the model is retrained on a schedule or when accuracy falls below the agreed level.
How does a project start?
With a free first consultation about your machine learning use case, then a short discovery that sets scope, team, and price before work begins.
What is the difference between machine learning services and a managed ML platform?
A managed platform from a cloud provider gives you tools to build and host models. Someone still has to frame the problem, prepare the data, build, test and run the model. Machine learning development services provide that work, usually on the platform you already use.
Do you provide AI and machine learning consulting before a build?
Yes. When the use case is not yet clear, the work starts with consulting: a review of candidate uses, data readiness and expected value that ends with a recommendation to build, buy or wait.
Data Platform Case Studies
Data foundations built to support analytics, products, and better decisions.
Health Insurance Reconciliation Drops 70%
Data Integration
Claims Analytics
Business Reporting
Data Aggregation
Health Insurance Reconciliation Drops 70%
Cognativ integrated fragmented claims, enrollment, care management, finance, and reporting data into a unified insight engine for a health insurance organization.
The work reduced manual reconciliation by 70%, improved audit readiness, and gave leadership real-time visibility.
Review the Data Unification
Healthcare Data: 20x Exit Valuation
Data Platforms
API Development
Data Integration
Data Marts
Healthcare Data: 20x Exit Valuation
Cognativ helped a traditional third-party payer reposition from a claims-processing service provider into a strategic healthcare data broker.
The transformation reshaped the company’s go-to-market model and increased exit valuation by 20x.
See the 20x Exit Story
Data Platform Reaches 75% Category Market Share
Data Platforms
Data Aggregation
Data Management
Technology Strategy
Data Platform Reaches 75% Category Market Share
Cognativ helped reposition a data management offering around a clearer market category, stronger operational narrative, and more defensible growth strategy.
The repositioning helped the business establish 75% market share in an emerging category while clarifying its value for enterprise buyers.
Explore the Data Category Win
B2B Distribution Throughput Increases 35%
ERP Modernization
API Integration
Inventory Management
Warehouse Automation
B2B Distribution Throughput Increases 35%
Cognativ helped a B2B distributor modernize legacy ERP limitations, automate warehouse workflows, and improve operational visibility.
The transformation increased throughput by 35%, raised average order size by 20%, and lifted gross margins by 15%.
See the Throughput GainsRelated Software Development Insights
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