Machine Learning & GenAI
That Ships to Production
From classical ML models to cutting-edge Generative AI — we cover the full spectrum. Predictive models trained on your data, RAG pipelines grounded in your knowledge base, and LLM-powered applications deployed to production with monitoring, evaluation, and CI/CD — engineered to perform reliably at enterprise scale.
Our Service Capabilities
End-to-end capability — from strategy and build to integration, monitoring, and ongoing support.
Supervised & Unsupervised ML
Classification, regression, clustering, and dimensionality reduction models — trained and validated on your domain data for churn, fraud, demand forecasting, and segmentation.
Deep Learning Systems
CNN, LSTM, Transformer, and GNN architectures for vision, NLP, time-series, and graph tasks — GPU-scale training with production-optimised inference.
Generative AI Applications
LLM-powered applications — RAG pipelines, AI copilots, content engines, and document intelligence — built on GPT-4o, Claude, Llama, and Mistral.
RAG Pipeline Development
Production-grade Retrieval-Augmented Generation pipelines with citation enforcement, hallucination controls, hybrid search, and evaluation frameworks.
MLOps & Pipeline Automation
CI/CD for ML, feature stores, model registries, automated retraining, and monitoring — turning notebooks into reliable, self-improving production systems.
Model Evaluation & Monitoring
Rigorous evaluation frameworks, drift detection, performance dashboards, and automated alerting — keeping models accurate as data evolves.
Our Engagement Process
A disciplined, outcome-focused approach from first call to go-live.
- 1
Discovery & Data Audit
Assess your business goal, data maturity, and determine the right ML/GenAI approach — classical ML, LLM, RAG, or hybrid.
- 2
Architecture Design
Design the system — model selection, data pipeline, feature engineering, serving infrastructure, and evaluation strategy.
- 3
Build & Train
Develop, fine-tune, and rigorously evaluate models against business-relevant benchmarks using your domain data.
- 4
Deploy & Integrate
Production deployment with APIs, CI/CD pipelines, monitoring dashboards, and integration into your existing stack.
- 5
Monitor & Improve
Continuous performance monitoring, drift detection, automated retraining, and monthly health reviews.
Tools & Frameworks We Master
A production-tested, vendor-agnostic stack built for enterprise security and compliance requirements.
Foundation Models
ML Frameworks
RAG & Orchestration
MLOps
Use Cases by Industry
Production AI systems we have built across regulated, data-heavy industries.
Credit Scoring + GenAI Explainability
XGBoost credit scoring model with GPT-4o explainability layer — regulators get plain-English risk summaries, approval accuracy improved 18%.
Clinical NLP + EHR Intelligence
BERT NLP pipeline + RAG Q&A over 2M clinical notes — HIPAA-compliant, 93.7% entity F1, deployed in 3 hospital networks.
Demand Forecasting + AI Analyst
LSTM demand model + RAG analyst chatbot — merchandising team queries forecasts in plain English, 4.2% MAPE on 30-day horizon.
Predictive Maintenance + Vision
LSTM anomaly detection + YOLOv9 visual inspection — reduces unplanned downtime 40%, defect detection 99.1% precision.
AI Copilot + Recommendation Engine
Embedded GenAI copilot + collaborative filtering recommendation engine — 15,000 daily users, 27% increase in feature adoption.
Research AI + Sentiment Engine
RAG research analyst + BERT sentiment pipeline over 50K daily news articles — feeds live trading signals with cited sources.
What Teams Say After Shipping with Us
Real results from teams who needed ML and GenAI systems to work in production, not just in a demo.
AndolaSoft has been a valued partner providing excellent customer service. Issues with clients or troubleshooting are handled in a timely manner and positive resolution is always the outcome.
I got a recommendation on AndolaSoft. They are more than half the cost, they have a can-do attitude, and they are responsive, timely, and easy to work with.
Andolasoft team is very hardworking, dedicated and professional that follows through with their goals. The technical leadership is also a superior value to any other developers.
Frequently Asked Questions
Machine learning trains models on historical data to make predictions or classifications. Generative AI uses large language models or diffusion models to generate new content — text, code, images, or audio. Many production AI systems combine both: ML models for prediction, GenAI for language understanding and generation.
Ready to Build Your ML or GenAI System?
Tell us your use case and data environment. We will recommend the right approach — ML, GenAI, or hybrid — and scope a concrete plan.