Machine learning solutions for real business problems.
Forecasting, recommendations, churn, fraud detection, computer vision — we scope, build, deploy and operate ML systems that earn their keep.
Machine learning solutions are custom ML models and pipelines designed to solve a specific business problem — demand forecasting, recommendation, churn prediction, fraud detection, computer vision or NLP — engineered, deployed and monitored in production.
What we deliver with Machine Learning Solutions.
Demand, revenue, inventory, capacity — with proper backtesting.
Product, content and next-best-action systems.
Propensity models with clear uplift measurement.
Detection pipelines with review queues.
Detection, classification, OCR on your images or video.
Training, serving, monitoring, drift detection, retraining.
Honest fit check.
A plain answer up front. We'd rather not sell you something you don't need.
- You have structured data and labeled examples for a prediction task
- You need forecasting, recommendations, fraud detection or classification
- You want models deployed and monitored in production, not just notebooks
- You need a chatbot or text generation — use LLMs instead
- You don't have historical data yet — collect first, model later
- You want a pre-built analytics tool, not custom ML
Who this service is for.
- Product teams needing demand forecasting or recommendation systems
- Fintech and insurance companies needing fraud or risk models
- Ecommerce businesses wanting churn prediction and LTV scoring
- Healthcare and operations teams needing computer vision or NLP
Our stack, battle-tested.
Starting from $700, depending on project scope and requirements.
What makes us different.
The people you meet in discovery stay involved through architecture, delivery and launch.
Metadata, schema, page performance and semantic markup are part of delivery, not a post-launch add-on.
Tradeoffs, integrations and scope changes are documented so your team can audit decisions later.
Repos, infra, analytics and documentation live in your accounts from the beginning.
Frequently asked questions
Maybe. We audit data quality and volume before committing. Often the answer is 'yes for X, not yet for Y' — we start where the data supports it.
Use an LLM when the task is language-heavy and examples aren't labeled. Use ML when you have structured data and labels — it's faster, cheaper and more accurate.
Yes — full MLOps including monitoring, drift detection and retraining pipelines.
Cuibit machine learning projects start from $700, depending on project scope and requirements. A focused prediction model (churn or forecast), a full ML platform with MLOps and monitoring, and a computer-vision pipeline are each scoped differently — written proposal after a data audit.
A single prediction model takes 6–12 weeks including data audit, training, validation and deployment. Multi-model systems with MLOps take 3–6 months.
We set up automated monitoring for prediction drift, data distribution shifts and accuracy degradation. Retraining pipelines trigger automatically or on schedule depending on your needs.
Related services.
Ready to start?
Tell us about your project. A senior strategist replies within one business day — with a written first take.