Machine Learning Development

ML Models That Learn From Your Data.

Gensoft builds production machine learning systems — custom models trained on your domain data, integrated into your existing stack, and continuously monitored — that predict, classify, and optimize at the speed your business demands.

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180+ML Models Deployed

45+ML Engineers

12ML Specialties

11+Years ML Experience

Free ML Consultation

Let's Scope Your ML Project

We'll assess feasibility and recommend an approach within 24 hours

ML systems trusted by leading global organizations

What We Build

Our Machine Learning Services

Specialized ML engineering across every major problem type — from supervised learning and deep neural networks to reinforcement learning and large-scale recommendation systems.

Predictive Analytics & Forecasting

Demand forecasting, sales prediction, churn scoring, lead scoring, and financial forecasting — supervised learning models trained on historical data to give your teams reliable foresight.

XGBoost Prophet LSTM

Computer Vision

Object detection, image classification, OCR, video analysis, defect inspection, and facial recognition — deep learning vision models for manufacturing, retail, healthcare, and security.

YOLOv8 OpenCV ResNet

Natural Language Processing

Text classification, sentiment analysis, named entity recognition, document summarization, semantic search, and intent detection — fine-tuned transformer models trained on your domain text.

BERT Transformers spaCy

Recommendation Systems

Collaborative filtering, content-based, and hybrid recommendation engines — for eCommerce product recommendations, content platforms, and personalized user experiences at scale.

Matrix Factorization Deep FM Two-Tower

Anomaly Detection & Fraud Prevention

Real-time fraud detection, network intrusion detection, equipment failure prediction, and quality control anomaly flagging — unsupervised and semi-supervised ML models for risk reduction.

Isolation Forest Autoencoder LSTM-AE

MLOps & Model Infrastructure

CI/CD pipelines for ML, model versioning, automated retraining triggers, A/B testing frameworks, drift detection, and production monitoring — the infrastructure to run ML reliably at scale.

MLflow Kubeflow Seldon

Technology

Our ML Technology Stack

End-to-end ML toolchain — from data ingestion and feature engineering to model training, serving, and monitoring in production.

ML Frameworks

TensorFlow 2.x PyTorch Keras Scikit-learn JAX

Data & Feature Engineering

Pandas Polars Apache Spark dbt Feast

Cloud ML Platforms

AWS SageMaker Azure ML Vertex AI Databricks

Experiment Tracking & MLOps

MLflow Weights & Biases DVC Kubeflow Airflow

Serving & Deployment

FastAPI TorchServe TF Serving BentoML Triton

Monitoring & Observability

Evidently Arize AI Prometheus Grafana

Industries

ML Solutions Across Every Industry

Machine learning delivers outsized value in data-rich industries where prediction, classification, and pattern recognition create direct business outcomes.

Fintech & Insurance

Healthcare & Pharma

Retail & eCommerce

Manufacturing & QC

Logistics & Supply Chain

Marketing & AdTech

Energy & Utilities

Media & Streaming

How We Work

Our ML Development Process

A rigorous, data-driven process from business problem to production model — with defined accuracy benchmarks and transparent milestones at every stage.

Problem Framing

We translate your business objective into a precise ML problem statement — choosing the right problem type (classification, regression, clustering, ranking) and defining measurable success criteria.

Data Audit & Preparation

We assess your data sources, quality, and volume — then build pipelines to clean, transform, label, and store training-ready datasets. Good data is the foundation of every reliable model.

Feature Engineering

We extract, transform, and select features that carry the highest predictive signal — often the single highest-impact activity in an ML project, determining how well any model can perform.

Model Training & Selection

We train and evaluate multiple model architectures, run hyperparameter optimization, and select the best performer — documented with experiment tracking so every decision is auditable.

Production Deployment

Models are deployed via optimized inference APIs, batch scoring pipelines, or embedded edge deployments — with load testing, latency profiling, and rollback capability built in from day one.

Monitoring & Retraining

We monitor for data drift, concept drift, and performance degradation — with automated retraining pipelines triggered when model metrics fall below agreed thresholds, keeping your ML reliable over time.

FAQ

ML Development FAQs

Common questions about ML feasibility, data requirements, and what to expect from a machine learning engagement.

Ask Us Anything

It depends on the problem type. Simple tabular classification or regression often works well with 1,000–10,000 labeled examples. Deep learning for image or text tasks typically needs 10,000+ samples for a robust model — though transfer learning and fine-tuning can reduce this significantly. We assess your data situation in discovery and recommend whether to collect more data, use data augmentation, or leverage pre-trained models.

AI is the broad field — ML is a specific technique within it. Machine Learning focuses on statistical models that learn patterns from data (classification, regression, clustering, etc.). AI development often includes ML plus generative AI, rule-based systems, computer vision pipelines, and intelligent automation. Our ML service focuses specifically on building and deploying predictive and analytical models trained on your data — as opposed to integrating existing LLM APIs.

We define accuracy benchmarks with you before development begins — using the right metric for your use case (precision/recall, AUC-ROC, RMSE, etc.). Models go through cross-validation, held-out test sets, and real-world shadow testing before going live. If a model doesn't reach the agreed threshold during the engagement, we continue iterating — we don't ship a model that doesn't meet the defined standard.

Yes — integration is typically how ML delivers business value. We expose models as REST APIs that your existing applications call in real-time, or schedule batch scoring jobs that write predictions back to your database or data warehouse. We work with your engineering team to ensure integration is smooth, with latency benchmarks agreed upfront.

Deployed models need ongoing care — data distributions shift, business conditions change, and model performance degrades over time. We set up monitoring dashboards, drift detection alerts, and automated retraining pipelines so your model stays accurate. We offer ongoing MLOps retainer agreements for clients who need continuous model health management without hiring a full-time ML team.

Let's Build Something That Learns

Your ML Project Starts With Your Data.

Tell us what you want to predict, detect, or optimize — and we'll assess whether ML is the right approach and what it would take to build it. Free, no-obligation technical consultation.

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