Machine learning for prediction problems ChatGPT can't solve.
We train custom models on your own data for fraud detection, churn, demand forecasting, and recommendations — prediction problems a language model was never built to handle.
What is machine learning development?
Machine learning development is training a model on your own historical data to predict, classify, or score an outcome, rather than connecting to a general-purpose AI model. A fraud-detection model learns your business's actual fraud patterns; a churn model learns what your specific customers do before they leave. That's a fundamentally different job from integrating ChatGPT, which draws on general language patterns, not your operational history.
Roughly 85% of machine learning projects fail, and poor data quality is the leading cause — not weak modeling technique. Basic ML solutions run under $100,000; advanced, large-scale initiatives can exceed $1,000,000, and the gap between those numbers is almost always data readiness and integration complexity, not the algorithm chosen.
- Data audit first
- Feasibility checked before a single model gets built.
- Right-sized models
- Gradient boosting where an LLM would be slower and costlier.
- Drift monitoring
- Retraining scheduled to how fast your data actually changes.
- Explainable output
- Predictions you can defend, not a black-box score.
Large language model, or custom machine learning?
These solve different problems. Picking the wrong one costs accuracy, money, or both.
| Approach | What it's built for | Best fit |
|---|---|---|
| LLM / ChatGPT | Language tasks: conversation, summarization, answering questions from documents. | Chatbots, support automation, content generation. |
| Custom Machine Learning | Structured prediction: a number or category learned from your own historical data. | Fraud risk, churn probability, demand forecasting, ranking. |
Machine learning systems we build most often.
A common and costly 2026 mistake is reaching for an LLM on a structured prediction problem a lightweight gradient-boosting model would solve more accurately, more explainably, and at a fraction of the inference cost.
Fraud & anomaly detection
Models that flag suspicious transactions or behavior in milliseconds, learned from your own historical patterns.
Churn prediction
Scoring models that flag at-risk customers before they leave, so retention teams act early, not after the fact.
Demand & revenue forecasting
Time-series models for inventory, staffing, and revenue planning, tuned to your business's actual seasonality.
Recommendation systems
Ranking and recommendation models that surface the right product or content to the right user, at scale.
Predictive maintenance
Models that flag equipment likely to fail, trained on sensor and operational data, not fixed maintenance schedules.
Computer vision & classification
Image-based defect detection, document classification, and visual quality control models.
Why we audit data before we model anything.
Model architecture rarely kills a machine learning project. The data underneath it does. We run a data audit and feasibility check before any model gets built — that step catches most of the projects that would otherwise fail four months in.
- 85% of ML projects fail
- The failure rate across the industry, not a worst case.
- Poor data quality is #1
- The leading cause of failure, ahead of weak modeling technique.
- 12–18 months to strong ROI
- The typical window before returns compound meaningfully.
- Models degrade over time
- Real-world data drifts from what the model was trained on.
- Feasibility checked first
- Whether your data can actually support the prediction you want.
- Retraining planned in
- A schedule matched to how fast your data changes, not a fixed cadence.
A clear path from raw data to production model.
Four stages, with feasibility proven before anything gets committed to a full build.
Data audit & feasibility
We assess whether your data can actually support the prediction, before committing to a build.
1–2 weeks · AuditFeature engineering
We clean, label, and engineer features from raw data so the model learns from real signal, not noise.
1–3 weeks · PreparationModel training & prototype
We train and evaluate candidate models, then hand you a working prototype proving the approach works.
2–6 weeks · PrototypeDeployment & monitoring
We deploy to production and monitor for data drift, retraining on a schedule that matches your data.
2–5 weeks · DeploymentThe machine learning technology we use.
Production-grade tools, chosen for accuracy, explainability, and cost efficiency.
Python
Pandas
PyTorch
TensorFlow
MLflow
AWS SageMaker
DockerProof, not promises.
Engineering built on real data, not a pitch deck.
Data · Full-Stack · Marketplace
A platform rebuilt on automated, data-driven decisions
BeesApp needed automated inventory decisions across a multi-vendor marketplace — the same data-pipeline discipline that any reliable machine learning system depends on. We rebuilt their platform on Next.js and FastAPI with 120+ API endpoints and real-time data flow, the operational foundation every ML deployment needs before a model can be trusted with a decision.
Read the case studyExplore more software services.
Machine learning is one of the more specific builds inside our broader AI development work.
AI Development
The full range of AI systems we build.
ExplorePredictive Analytics
Forecasting applied to business planning decisions.
ExploreChatGPT Integration
Conversational AI for your product.
ExploreData & Analytics
The pipelines every ML model runs on.
ExploreCloud SaaS Apps
Infrastructure that scales ML workloads.
ExploreAPI Integrations
Connect model outputs into the systems that act on them.
ExploreCustom Software
Bespoke builds, MVPs & enterprise systems.
ExploreCloud & DevOps
Deployment pipelines and monitoring for model serving.
ExploreMachine learning questions
The things clients ask us most before starting a machine learning build.
Machine learning development is training a model on your own historical data to predict, classify, or score an outcome — fraud, churn, demand, or ranking — rather than connecting to a general-purpose AI model. The model learns patterns specific to your business instead of general language patterns.
Use a large language model if the task involves language: answering questions, summarizing, or holding a conversation. Use custom machine learning if the task involves predicting a number or category from structured data, such as fraud risk, churn probability, or demand. A common and costly mistake in 2026 is reaching for an LLM on a structured prediction problem a lightweight model would solve more accurately and far more cheaply.
Roughly 85% of machine learning projects fail, and poor data quality is the leading cause, not weak modeling technique. We audit data quality and feasibility before any model gets built, so a project doesn't reach month four before someone discovers the data can't support it.
Basic ML solutions run under $100,000; advanced, large-scale, or highly custom initiatives can exceed $1,000,000. Cost depends on data readiness, model complexity, and infrastructure needs. We scope your specific use case before quoting a fixed price.
A working prototype takes 4 to 8 weeks, and a full production deployment typically takes 8 to 16 weeks, depending on data availability and integration complexity.
Fraud detection, churn prediction, demand forecasting, recommendation systems, and predictive maintenance are the strongest fits. Amazon attributes up to 35% of its revenue to recommendation systems alone, which reflects how much value well-built prediction models can return.
Yes. Models degrade over time as real-world data drifts from what they were trained on, so we monitor performance and retrain on a schedule that matches how fast your data actually changes.
Yes. You own 100% of the trained model, code, and training pipeline in documented repositories, with no dependency on us to retrain or operate it.