Your team has data but no system turning it into predictions or decisions?
Tried off-the-shelf ML tools that don't fit your actual data or workflow?
Machine Learning Development Services
Most machine learning projects fail not because the models are wrong, but because the surrounding system is not built to use them.
We build end-to-end ML systems -- from data pipeline to model training to production deployment -- that connect to your existing operations. Models that run in real systems, on your data, and deliver output your team can act on.
Custom ML models trained on your operational data
End-to-end: data pipeline, training, validation, and production deployment
Integration with your existing apps, CRM, ERP, or dashboards
100+ products shipped including AI and ML-powered systems
RaftLabs builds custom machine learning systems trained on your operational data and integrated into your production environment. We handle the full stack -- data pipelines, model training, validation, and deployment -- for use cases including demand forecasting, churn prediction, fraud detection, document classification, and computer vision. Every ML project starts with a scoped discovery phase to define data requirements, model approach, and success metrics before development begins.
ML value comes from production, not from notebooks
A model that lives in a Jupyter notebook is a prototype. A model that runs in your CRM, flags risks in your operations dashboard, or routes decisions in your application is a system.
The gap between prototype and production is where most ML projects fail. Data scientists build accurate models that never reach the people who need the predictions. We build the full system -- data pipeline, model, integration, and monitoring -- so predictions reach your team automatically.
What we build
Demand and inventory forecasting
Forecasting models for inventory, staffing, capacity, and sales. Trained on your historical order data with seasonal patterns, promotional calendars, and external signals included. Output delivered to your ERP, planning tool, or operations dashboard on a defined schedule. Confidence intervals included so your team knows when to apply judgment.
Customer churn prediction
Churn risk scoring for every customer account, updated weekly. Models trained on your transaction history, product usage, support interactions, and contract data. Risk scores delivered to your CRM so retention teams focus on accounts with actual risk signals -- not gut feel. Segment analysis to identify which customer profiles churn first.
Classification and categorisation
Multi-class classification for documents, support tickets, transactions, leads, and any other structured or semi-structured data that needs automatic categorisation. Trained on your historical labelled data. Output routed to your existing workflow -- queue, CRM field, or automated action -- without manual review of low-confidence cases.
Fraud and anomaly detection
Anomaly detection for transactions, user behaviour, sensor readings, and operational metrics. Pattern-based models that learn your baseline and flag deviations before they become costly. Tunable sensitivity to balance false positive rates against detection coverage. Alert delivery to your operations or fraud team with supporting evidence.
NLP and text intelligence
Document classification, named entity extraction, sentiment analysis, and text summarisation on your unstructured data. Built on fine-tuned language models calibrated to your domain vocabulary and document types. Applied to support tickets, contracts, emails, clinical notes, financial reports, or any high-volume text workflow.
Computer vision
Image classification, object detection, and defect identification for manufacturing, logistics, retail, and healthcare applications. Models trained on your labelled image data. Integrated into your inspection workflow, camera systems, or document processing pipeline. See our computer vision development page for specific use cases.
What do you want the model to predict?
Tell us the use case, the data you have, and the decision it needs to inform. We'll assess feasibility and give you a fixed-cost proposal.
How we run ML projects
Related services
Predictive Analytics Services -- business-focused forecasting and risk scoring
Machine Learning Consulting -- strategy and architecture without a full build commitment
Computer Vision Development -- image and video ML systems
NLP Development Services -- language model fine-tuning and text intelligence
AI Development Services -- custom AI products from scratch
Frequently asked questions
Custom machine learning development means building a model trained on your specific data to solve your specific problem -- not using a generic pre-trained model with limited customisation. Custom models outperform generic solutions when your data has patterns specific to your business, your domain, or your customer base. The development process includes data assessment, feature engineering, model selection and training, validation against held-out data, and integration into your production system.
The minimum data requirement depends on the problem. Classification models for tabular data (churn prediction, fraud detection, lead scoring) typically need 10,000--50,000 labelled examples. Time-series forecasting needs 12--24 months of historical data at the required granularity. Computer vision models need 1,000--10,000 labelled images per class. NLP models fine-tuned on a base model (BERT, GPT) need fewer examples -- 100--1,000 is often sufficient for classification tasks. We assess your data during scoping and tell you exactly what we need before committing to a build.
Supervised learning: classification (yes/no, multi-class) and regression (continuous output) for prediction problems. Unsupervised learning: clustering and anomaly detection for pattern discovery without labels. Time-series forecasting: demand, capacity, and trend prediction. Natural language processing: document classification, entity extraction, sentiment analysis, and text summarisation. Computer vision: image classification, object detection, and OCR. We match the approach to the problem -- not the other way around.
A focused ML project -- one use case, one data source, training, validation, and deployment to one target system -- typically takes 8--16 weeks. More complex projects with multiple models, custom data pipelines, and integrations with multiple systems take 4--9 months. Every project starts with a 2--3 week discovery phase to assess data quality, define success metrics, and scope the build before committing to a timeline.
We deploy ML models as REST APIs (FastAPI or Flask), containerised with Docker, and hosted on AWS or GCP. Your existing application calls the model API to get predictions. For real-time use cases, predictions are returned in milliseconds. For batch use cases, the model runs on a schedule and writes predictions to your database or data warehouse. We handle model versioning, monitoring (drift detection, performance tracking), and retraining pipelines so the model stays accurate as your data evolves.
A focused ML project -- discovery, model training, validation, and API deployment -- typically runs $25,000--$60,000. Larger projects with custom data pipelines, multiple models, BI dashboard integration, and automated retraining pipelines run $60,000--$150,000. We scope every project before pricing. The scoping process includes a data audit, model feasibility assessment, and a fixed-price proposal.