ML / CV / NLP
Development Services
We build custom machine learning models that solve real business problems—from computer vision and NLP to predictive analytics and recommendation systems.
- Custom Model Development
- Production-Ready MLOps
- Enterprise Security
Machine Learning Expertise
We develop custom models across the ML spectrum—from classical algorithms to deep learning.
Computer Vision
Visual AI systems for detection, classification, segmentation, and tracking.
- Object Detection (YOLO, Faster R-CNN)
- Image Classification & Recognition
- Semantic & Instance Segmentation
- OCR & Document Processing
- Video Analytics & Tracking
Natural Language Processing
Text understanding and generation for documents, conversations, and knowledge extraction.
- Named Entity Recognition
- Sentiment & Intent Analysis
- Text Classification & Clustering
- Question Answering Systems
- Language Translation
Predictive Analytics
Forecasting and prediction models for business intelligence and automation.
- Time Series Forecasting
- Demand Prediction
- Churn & Risk Modeling
- Anomaly Detection
- Recommendation Systems
Search & Retrieval
Intelligent search systems using embeddings and semantic understanding.
- Vector Search & Embeddings
- Semantic Search
- Knowledge Graphs
- RAG Architectures
- Hybrid Search Systems
Real-World ML Applications
Examples of how we've applied machine learning to solve business problems.
Automated Quality Inspection
Problem
Manual visual inspection was slow, inconsistent, and missed subtle defects.
Solution Approach
Custom CNN model trained on defect images with real-time edge inference.
Outcome
99.5% defect detection with 10x faster inspection speed.
Intelligent Document Processing
Problem
Manual document review created bottlenecks and high error rates.
Solution Approach
Multi-modal model combining OCR, NER, and classification for automatic extraction.
Outcome
80% reduction in processing time with 95% accuracy.
Predictive Equipment Maintenance
Problem
Reactive maintenance led to costly unplanned downtime and safety risks.
Solution Approach
LSTM-based anomaly detection on sensor data with 7-day failure prediction.
Outcome
40% reduction in unplanned downtime, $2M annual savings.
Customer Intent Classification
Problem
Support tickets were manually routed, causing delays and misclassification.
Solution Approach
Fine-tuned transformer model for multi-label intent classification.
Outcome
85% auto-routing accuracy, 50% faster resolution time.
Our ML Development Process
Problem Definition & Data Assessment
We clarify the business problem, success metrics, and evaluate your data assets for ML readiness. Deliverables: Problem statement, Data audit report, Feasibility assessment
Data Engineering & Preparation
We build data pipelines, handle labeling, and prepare training datasets with proper splits. Deliverables: Data pipeline, Labeled dataset, Feature engineering
Model Development & Experimentation
Iterative model training with systematic experimentation, hyperparameter tuning, and validation. Deliverables: Trained models, Experiment logs, Performance benchmarks
Validation & Testing
Rigorous testing including edge cases, bias detection, and real-world performance validation. Deliverables: Test reports, Bias audit, Edge case analysis
Production Deployment
Deploy models with proper MLOps infrastructure—APIs, monitoring, and automated retraining. Deliverables: Production API, Monitoring dashboard, CI/CD pipeline
ML System Architecture
Production-ready ML pipelines from data ingestion to model serving.
Data Layer
Storage & Pipelines
Feature Store
Preprocessing
Model Training
Experimentation
Model Serving
API & Inference
Monitoring
Drift & Performance
Business-First Approach
We start with the business problem, not the technology. Models are designed to create measurable value.
Production Focus
Every model we build is designed for production—with proper testing, monitoring, and maintainability.
End-to-End MLOps
We deliver complete ML systems including data pipelines, training infrastructure, and deployment automation.
Responsible AI
We implement bias detection, explainability, and fairness testing as standard practice.
Our ML Technology Stack
Modern, battle-tested tools for building production ML systems.
Deep Learning
Computer Vision
NLP / LLM
ML Engineering
MLOps
Cloud ML
ML in Production
AI-Powered Solar Tracking Optimization
Focus: Edge ML, Computer Vision
Outcome: +12% energy generation through ML-optimized sun tracking.
TerraSmart Solar ML Platform
Focus: Predictive Models, Data Pipelines
Outcome: 30% faster field deployment with ML-driven site analysis.
Abode Smart Home Intelligence
Focus: Anomaly Detection, Device ML Models
Outcome: 99.99% uptime with ML-powered device health monitoring.
Common Questions
Ready to Build Custom ML Models?
Tell us about your use case and data. We'll assess feasibility and propose an approach.