Applied AI in complex environments
Engineering intelligent systems where data, models, and operational constraints meet. Specializing in end to end solutions that are dependable, maintainable, and practical.
Focus areas
Emphasis is placed on solutions that can be deployed, operated, and evolved within real technical and organizational constraints.
Applied machine learning
Model training, evaluation, and iteration with an emphasis on real data quality and failure modes.
End to end AI systems
From data ingestion and pipelines to deployment, monitoring, and human validation workflows.
Generative AI with control
RAG systems, fine tuned models, and retrieval design with an emphasis on traceability and trust.
Agentic AI and Orchestrated Systems
Design and integration of task-oriented AI agents that interact with tools, data sources, and enterprise systems under defined constraints. Focus on orchestration, control, and transparency rather than full autonomy.
Rapid Prototyping and Experimental AI
Prototyping as a structured step between use case ideation and production-level AI systems. Prototypes are used to validate data availability, model behavior, and system assumptions before committing to full-scale implementation.
AI Governance and Responsible Design
Design of AI systems with attention to governance, transparency, and operational responsibility. Regulatory and organizational constraints are treated as design inputs that shape architecture and workflows.
Interpreting the work
The projects shown here are selected for the decisions they required and the constraints they exposed. Most evolved through trade-offs around data, systems, and operational realities rather than following a predefined path.
More on perspectiveScope
A focused selection of applied AI projects and system-level work. Not a complete catalogue.
Selected work
A curated set of projects focused on applied AI systems and the constraints they operate within.
Computer vision in manufacturing
Chip detection system designed for real production conditions, including deployment constraints and operator feedback.
RAG chatbot with fine tuned models
Retrieval architecture combining embeddings, graph data, and controlled generation for traceable answers.
Time series anomaly detection on edge
Deep learning model development and deployment on microcontroller hardware for real time inference.
AI meter reading system
SaaS style pipeline combining detection, OCR, and manual validation workflows for utilities.