Applied AI Engineering
Building intelligent systems that solve real problems
Our AI engineering practice focuses on production systems—not research papers or proof-of-concepts. We build LLM-powered applications, intelligent document systems, and AI orchestration layers that operate reliably at scale.
Focus Areas
LLM-based Systems
- →Custom AI orchestration and prompt engineering
- →Context management and retrieval systems
- →Model selection and fine-tuning strategies
- →Cost optimization and latency management
Intelligent Document Processing
- →OCR and document understanding pipelines
- →Structured data extraction from unstructured sources
- →Document classification and routing
- →Multi-modal document analysis
NLP & Understanding
- →Semantic search and information retrieval
- →Entity extraction and relationship mapping
- →Sentiment analysis and text classification
- →Multi-language support and translation
Speech & Interaction
- →Speech-to-text and text-to-speech systems
- →Conversational AI and dialogue management
- →Voice interface design and optimization
- →Real-time transcription and analysis
AI Engineering Principles
- ✓Observability and explainability over black-box solutions
- ✓Graceful degradation and fallback strategies
- ✓Human-in-the-loop where judgment matters
- ✓Cost-aware architecture from day one
- ✓Security and data privacy by design