Our Work

Intelligent Document Search Platform

Built a semantic search engine for a legal tech company, enabling natural language queries across millions of documents.

Challenge

Traditional keyword search was insufficient for complex legal queries. Users needed to find relevant documents based on concepts and context, not just exact text matches.

Solution

• Implemented transformer-based embeddings for semantic understanding
• Built vector database with sub-second query times
• Developed hybrid search combining semantic and keyword approaches
• Created relevance ranking using fine-tuned models

Results

• 78% improvement in search relevance
• 45% reduction in time-to-find
• 200ms average query latency
• Handling 10M+ documents

AI-Powered Product Recommendations

Developed a multi-modal recommendation system combining visual similarity and user behavior for an e-commerce platform.

Challenge

The client needed to improve product discovery and increase conversion rates through personalized recommendations that go beyond simple collaborative filtering.

Solution

• Built visual search using pre-trained CNNs fine-tuned on product images
• Implemented hybrid recommender combining visual, textual, and behavioral signals
• Created real-time inference pipeline with <100ms latency
• Deployed A/B testing framework for continuous optimization

Results

• 32% increase in click-through rate
• 18% boost in conversion rate
• $2.4M additional annual revenue
• 99.95% uptime over 12 months

Enterprise MLOps Platform

Designed and implemented a complete MLOps infrastructure for a Fortune 500 company, enabling their data science team to deploy models 10x faster.

Challenge

Manual deployment processes were causing 2-3 month delays from model development to production. No standardization across teams led to inconsistent practices and difficult maintenance.

Solution

• Built automated CI/CD pipelines for ML models
• Implemented model registry and versioning system
• Created monitoring dashboard for model performance
• Established automated retraining workflows
• Set up A/B testing infrastructure

Results

• 10x faster model deployment
• 65% reduction in production incidents
• 40% cost savings on infrastructure
• Enabled 50+ models in production