AI & ML Engineering
Giving consciousness to code, purpose to data
Giving consciousness to code, purpose to data
Turning models into reliable products requires retrieval, evaluation, and safety hardening
Key Capabilities
Comprehensive blockchain infrastructure services designed for security, compliance, and operational excellence.
RAG Pipelines
Build sophisticated retrieval-augmented generation systems with vector databases and semantic search.
Safety Hardening
Implement robust guardrails, evaluation frameworks, and safety protocols for AI systems.
Inference Optimization
Optimize model inference with serverless architectures and performance monitoring.
Model Monitoring
Comprehensive monitoring and evaluation of model performance, drift, and business impact.
Our Approach
Deliverables
Tangible outputs that ensure your AI/ML systems are production-ready and maintainable.
Retrieval index and vector database setup
Optimized semantic search infrastructure for efficient information retrieval and context enhancement
Evaluation reports and safety assessments
Comprehensive model performance analysis with safety and bias evaluation frameworks
Prompt library and optimization guidelines
Curated prompt collections and best practices for consistent, high-quality AI interactions
Performance metrics and monitoring dashboards
Real-time model performance tracking with automated drift detection and alerting
Deployment guides and operational procedures
Production-ready deployment strategies with scaling and maintenance protocols
Technology Stack
Modern tools and frameworks we use to build robust AI/ML systems.
Vector databases (Pinecone, Weaviate, Qdrant)
High-performance vector storage for semantic search and similarity matching
Evaluation frameworks (Weights & Biases, MLflow)
Comprehensive model evaluation and experiment tracking platforms
Serverless inference (AWS Lambda, Vercel)
Scalable, cost-effective model deployment with automatic scaling
Monitoring tools (Grafana, Prometheus)
Real-time model performance monitoring and alerting systems
LLM frameworks (LangChain, LlamaIndex)
Modern development frameworks for building AI applications and workflows
Case Study: Retrieval-Augmented Q&A
Retrieval-augmented Q&A for enterprise docs → first-answer accuracy +32%
Implementation Highlights:
Built semantic search pipeline with vector embeddings
Implemented context-aware prompt engineering
Deployed with serverless inference architecture
Achieved 32% improvement in first-answer accuracy
Ready to build intelligent systems?
Let's discuss how AI/ML engineering can transform your business processes and unlock new capabilities.