Kaspars P.

I help companies identify, prove, and deploy AI solutions for operational problems where the business impact can be measured. If your team is spending too much time on manual workflows, document processing, visual inspection, knowledge retrieval, or repetitive operational decisions or you already have an AI prototype that is unreliable, expensive, or not production-ready? I can help. My approach is business problem → proof → production. First, I understand the workflow, data, constraints, and what improvement would actually justify the investment. Then I determine the right technical approach, validate it on real data, measure the result, and build the production system if the business case holds. With 10+ years in production software and AI, I’ve worked on systems for manufacturing, e-commerce, and operational environments where reliability, cost, and measurable performance matter. Recent results include: * Reduced factory processing time by 20% through AI-driven automation * Built computer vision systems for automated quality-control workflows * Developed semantic search and RAG systems over large datasets * Deployed production ML systems on AWS SageMaker and cloud infrastructure * Optimized AI inference pipelines to reduce cost and improve reliability I typically work on problems involving: * Manual workflows that could be automated with AI * Document processing, extraction, classification, and knowledge retrieval * Internal AI assistants and RAG over company data * Computer vision for inspection, quality control, detection, and classification * Existing AI prototypes that need to become reliable production systems * AI systems with accuracy, latency, reliability, or inference-cost problems Depending on the problem, I work with AI agents, LLMs, RAG, computer vision, traditional machine learning, automation, and cloud infrastructure. My strongest value is not simply building models. It is determining what should be built, how to prove that it works, and how to make it reliable enough to operate inside a real business. For new AI initiatives, I can start with a focused feasibility and architecture phase: 1. Understand the current workflow and business objective 2. Identify the highest-value AI opportunity 3. Assess the available data and technical feasibility 4. Define measurable success criteria 5. Design the architecture and implementation path 6. Build and validate a prototype on real data 7. Productionize and integrate the solution when the results justify it Typical engagements range from feasibility and architecture reviews to working prototypes and complete production implementations. Core technologies: Python, PyTorch, OpenCV, FastAPI, OpenAI, Claude, Gemini, Llama, LangChain, FAISS, OpenSearch, pgvector, AWS SageMaker, Lambda, EC2, Docker, Kubernetes, and CI/CD. If you have an operational problem that you think AI could improve, send me the workflow, current bottleneck, and what you would like to improve. I can help determine whether AI is actually the right solution, what should be tested first, and the fastest practical path from idea to production.