Navigate
Services
Solutions
Tools & Resources
Industries
Buyer Paths
Service
Machine Learning Development Services
Build predictive systems that turn operational data into measurable decisions — from forecasting and classification to recommendation engines and production ML pipelines.
What This Service Delivers
Machine learning is most valuable when it is applied to a specific operational decision: how much to produce next month, which customers are about to churn, which invoices are risky, which patients need follow-up. We design, train, and deploy custom ML models — classification, regression, forecasting, recommendation, NLP, and computer vision — then run them in production with monitoring, retraining, and drift detection so they stay accurate after launch.
View this service as...
Investment
$20,000 – $100,000
Timeline
8–20 weeks
Expected outcome
Demand, churn, risk, and revenue forecasts trained on your data — not generic benchmarks.
Key risks to plan for
Large-scale data labeling · Ongoing retraining (add AI Optimization) · Data acquisition
Forecast accuracy
Demand, churn, risk, and revenue forecasts trained on your data — not generic benchmarks.
Decision uplift
Scoring and recommendation systems that measurably improve the decisions your team makes every day.
Automated lifecycle
Retraining pipelines, monitoring, and drift detection keep models accurate as your data changes.
Explainable by design
Auditable features, evaluation reports, and explainability for regulated industries.
Investment & Timeline
Starting from
$20,000
Typical project range
$20,000 – $100,000
Typical timeline
8–20 weeks
What's included
- ✓ Data audit and feature engineering
- ✓ Model development and evaluation
- ✓ ML pipeline (training → serving)
- ✓ Monitoring and drift detection setup
What's not included
- ✕ Large-scale data labeling
- ✕ Ongoing retraining (add AI Optimization)
- ✕ Data acquisition
Factors affecting price
Data quality and volumeModel complexityInference latency and compliance needs
What You Get
Custom ML development — classification, regression, forecasting, recommendation
Predictive analytics on your operational data
NLP and computer vision where your problem requires them
Model training, fine-tuning, and evaluation
Model deployment and MLOps (serving, monitoring, retraining)
Edge ML and AI integration into existing software
Related Project
Sentinel SYX
Veterans faced long waits for disability-claim guidance. The claims process is complex, and applicants often did not know which evidence they needed or what their eligibility was.
Centcept built an AI-powered platform for Sentinel SYX that automates claims assistance, eligibility guidance, and case tracking. The AI walks applicants through their situation and points them to the right evidence and next steps.
Outcome
Veterans now receive personalized guidance in minutes instead of waiting months for answers. Claims support is available around the clock.
Technology
Pythonscikit-learnPyTorchXGBoostHugging FaceMLflowONNXKubernetes
Frequently Asked Questions
What problems is machine learning best suited for?
Prediction, classification, and recommendation on historical data: demand forecasting, churn prediction, fraud and risk scoring, predictive maintenance, pricing, and personalized recommendations. If you have patterns in your data, ML can operationalize them.
How is machine learning different from generative AI?
Generative AI creates new content (text, images, code). Machine learning predicts or classifies from structured patterns — forecasts, scores, and decisions. Many systems combine both: ML scores the risk, an LLM explains it.
How do you measure success for an ML project?
We define business metrics up front — forecast error reduction, churn uplift, precision/recall on your decision — and report model performance against them continuously after launch.
Do we need our own data science team?
No. We bring the data engineering and MLOps. We audit what you have, design collection strategies where needed, and hand over with documentation and training.
Can you deploy ML at the edge or on-premise?
Yes. Models can be served via API, on edge devices (ONNX/TFLite), or inside your own VPC — matched to your latency and data-residency requirements.
Your Service Lifecycle
Related Resources
Ready to get started with machine learning?
$20,000 – $100,000 typical range · 8–20 weeks typical timeline. Book a call and we'll scope the highest-value version of this work.