A machine-learning model becomes much more useful when it can be called securely and reliably by any authorized software system. Moving a model from a notebook to a service is a separate engineering problem, and it is the one we solve.
Examples
14- Classification APIs
- Probability APIs
- Forecasting APIs
- Recommendation APIs
- Anomaly-detection APIs
- Ranking APIs
- Demand forecasting
- Risk scoring
- Fraud scoring
- Churn prediction
- Lead scoring
- Pricing models
- Forecasting systems
- Optimization engines
Example request and response
Input
{
"company_id": "12345",
"country": "US",
"industry": "manufacturing"
}Output
{
"risk_score": 72,
"distress_probability": 0.14,
"growth_probability": 0.68,
"confidence": 0.86
}What production model serving involves
Serving a model well means treating the model as one component in a system that has to keep working:
- Model packaging and versioning
- Feature pipelines at request time
- Batch and real-time inference paths
- Input validation and schema contracts
- Confidence and fallback behaviour
- Latency budgets and caching
- Model health and drift monitoring
- Retraining and safe rollout
- Audit logging of predictions
We can deploy a model you already have, or build the model as part of the same engagement.
Related services
Prediction and Scoring API Development
APIs that convert raw information into scores, forecasts, probabilities, rankings and decision signals your customer's software can act on.
AI API Development
Integrate LLMs, NLP and AI workflows into controlled, production-grade APIs with validation, deterministic rules and predictable costs.
Deploy Your ML Model as an API?
Tell us what the API should receive, what it should return and who will be calling it. We will come back with an approach, a scope and a price.