AI/ML & GenAI

Build intelligent systems with machine learning, computer vision, and generative AI that deliver measurable business value.

Business Outcomes

1

Smarter Decisions

Leverage predictive analytics and ML models to automate decisions and uncover insights from your data.

2

Enhanced Experiences

Build conversational AI, recommendation engines, and personalization that delights users and drives engagement.

3

Operational Efficiency

Automate repetitive tasks with computer vision, NLP, and intelligent document processing.

Core Capabilities

Custom ML model development and training
Large language model (LLM) fine-tuning and deployment
Retrieval-augmented generation (RAG) systems
Computer vision for quality inspection and automation
Natural language processing and sentiment analysis
MLOps pipelines with versioning and monitoring
Model evaluation, testing, and bias mitigation
Edge AI and on-device inference optimization

Reference Architecture

End-to-end ML pipeline with automated training, versioning, deployment, and continuous monitoring for production-grade AI systems.

Reference Architecture Diagram

Delivery Approach

1
Problem Definition & Data Assessment

  • • Business objective mapping to ML capabilities
  • • Data quality audit and labeling strategy
  • • Success metrics and evaluation criteria
  • • Feasibility study and risk analysis

2
Model Development

  • • Data preprocessing and feature engineering
  • • Model architecture selection and experimentation
  • • Training with hyperparameter optimization
  • • Evaluation against test datasets and benchmarks

3
Deployment & Integration

  • • Model serving infrastructure (API or edge)
  • • Monitoring dashboards for accuracy and latency
  • • A/B testing framework for iterative improvements
  • • Integration with existing applications and workflows

4
Operations & Continuous Learning

  • • Feedback loops for model retraining
  • • Drift detection and alerting
  • • Cost optimization for inference
  • • Documentation and team training

Real-World Use Cases

Enterprise RAG Assistant

Built a retrieval-augmented generation system for internal knowledge base search, reducing time-to-answer for support teams.

Success Metrics:
85% reduction in search time
92% answer accuracy rating
50K+ documents indexed
Sub-2-second response time

Computer Vision Quality Control

Deployed an edge-based vision model for manufacturing defect detection, reducing false rejects and improving throughput.

Success Metrics:
98.5% precision and recall
60ms inference latency
30% increase in throughput
85% reduction in manual inspection

Predictive Maintenance System

Trained time-series models on sensor data to predict equipment failures, enabling proactive maintenance and avoiding downtime.

Success Metrics:
40% reduction in unplanned downtime
70% fewer emergency repairs
15-day failure prediction window
$2M+ annual cost savings

Intelligent Document Processing

Automated invoice and contract extraction with NLP and OCR, eliminating manual data entry for financial operations.

Success Metrics:
95% extraction accuracy
20K+ documents processed monthly
80% time savings
99.2% STP rate achieved

Technologies & Platforms

PyTorch
TensorFlow
OpenAI
Anthropic
LangChain
Hugging Face
MLflow
Kubeflow
Ray
ONNX
TensorRT
Pinecone

Frequently Asked Questions

Ready to Get Started?

Schedule a consultation to discuss your requirements and receive a detailed proposal.