Prediction, classification and NLP models trained on your data.
When an LLM is the wrong tool: forecasting, churn, fraud, pricing, anomaly detection and custom NLP built by ML engineers.
What is machine learning development?
Machine learning development covers the models that predict, classify and detect rather than generate text: demand forecasting, churn and lifetime value, fraud and anomaly detection, pricing and custom NLP. Eazyware's ML engineers train these on your data, validate them properly and deploy them with serving, monitoring and retraining loops.
| Service line | AI-Powered Product Engineering |
|---|---|
| Engagement | Scoped build with milestones |
| Duration | Quoted after scoping; typically 8–16 weeks |
| Starting price | $17,500 |
| Typical range | $17,500 – $70,000 |
| Deliverables | 5 listed below |
| Delivered from | Bengaluru, India (IST, UK and US East hours) |
| Code ownership | Client owns code, infrastructure, prompts and documentation |
What problem does it solve?
Not every problem is a language problem. Demand forecasting, credit risk, churn prediction and anomaly detection need real ML: trained, validated and monitored.
How do we approach it?
Machine learning work starts with the decision the prediction will change, because a model nobody acts on is a science project. We audit the data for that decision, establish a baseline, usually the heuristic the business already uses, and then build features and models until we beat it by a margin that matters. Validation is done on hold-out periods, not just random splits, because operational data drifts with seasons and campaigns. The model ships behind an API with monitoring for input drift and prediction quality, and a retraining loop with a human review of any change before it goes live.
What do clients use it for?
- Demand and inventory forecasting
- Churn and lifetime-value prediction
- Fraud and anomaly detection
- Pricing and risk scoring
- Document and image classification
Is it the right fit?
Good fit when
- Businesses with two or more years of operational data
- Teams with a decision that a prediction would change
- Products needing real-time scoring
Probably not when
- Problems with too little labelled data
- Text generation tasks (use LLM Applications)
What do we build?
- Predictive models: demand, churn, LTV, pricing, risk
- Classification and anomaly detection: fraud, defects, alerts
- NLP: classification, entity extraction, sentiment, topic modelling
- Computer vision for documents and images where needed
- Feature pipelines and training infrastructure
- Model serving, monitoring and retraining loops
What you get
- Trained models
- Serving API
- Feature pipeline
- Monitoring dashboard
- Model cards
How does the engagement work?
- 01
Problem framing and data audit
- 02
Baseline
- 03
Feature engineering and modelling
- 04
Validation
- 05
Deploy
- 06
Monitor
What does good look like?
A model in production that measurably beats the previous way of deciding, with the lift shown on a dashboard your team reads. Forecast error, precision and recall, or whatever metric the decision needs, tracked over time. A feature pipeline that the data team owns, model cards that explain what the model uses and where it is weak, and alerts when the world changes enough to warrant retraining.
How does it compare?
| Eazyware | Typical agency | In-house hire | |
|---|---|---|---|
| Time to first result | Sprint Zero in 10 days, then a fixed-scope build | 6–12 weeks of discovery before a proposal | 3–6 months to hire, then ramp |
| Pricing model | Fixed scope, milestone billing, INR or USD | Time and materials, open-ended | Salaries, tooling, management overhead |
| AI depth | Multi-model, evals, cost routing, observability as standard | Often a single vendor API and a prompt | Depends entirely on who you can hire |
| Ownership | Client owns code, infra, prompts and docs | Sometimes retained or licensed back | Owned, but concentrated in one or two people |
| After launch | Care Plans with SLA and AI add-on | Change requests at hourly rates | Ongoing headcount whether or not there is work |
Which pitfalls do we design around?
ML projects fail on leakage, on random splits that hide drift, on optimising a metric the business does not use, and on shipping a model with no monitoring so it degrades silently. We check for leakage explicitly, validate on time, agree the business metric first and monitor from launch.
What do we measure?
Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.
- Forecast error or precision and recall
- Lift over the current baseline
- Drift and retraining cadence
Which technologies do we use?
- Python
- scikit-learn / XGBoost / PyTorch
- MLflow
- FastAPI
- Airflow / Dagster
- BigQuery / Snowflake / Postgres
Who does the work?
An ML engineer, a data engineer for pipelines and features, and an architect for serving and monitoring, with the principal reviewing validation before anything goes live.
What do you need to bring?
Two or more years of the operational data behind the decision, access to the current process or heuristic for the baseline, someone who owns the decision the prediction will change, and a data engineer or contact who knows where the data lives.
Frequently asked questions
LLM or ML?
We pick per problem, often both in one system.
How much data do we need?
Depends. We assess in Sprint Zero and say so honestly.
Who maintains models?
A Care Plan with the AI add-on covers retraining and drift.
Where does this fit?
AI/ML Development is part of our AI-Powered Product Engineering line. Not sure yet? Start with Sprint Zero, a ten-day discovery whose fee is credited to this build. See all pricing or talk to an engineer.