Most businesses don't need artificial general intelligence. They need a process that currently takes 4 hours to take 15 minutes. That's what AI automation actually looks like in practice — targeted solutions that solve specific bottlenecks.
I help businesses implement AI where it creates measurable ROI: automating repetitive tasks, building intelligent customer interactions, and connecting AI capabilities to existing business workflows.
Customer-Facing AI Chatbots
What this solves: customers waiting hours for answers, support teams spending 60% of time on repetitive queries, after-hours inquiries going unanswered. How it works: I connect an LLM to your existing knowledge base through a RAG pipeline. The chatbot answers from your actual data, not generic internet knowledge. Escalation to human agents is built in for complex cases.
Workflow Automation
What this solves: staff spending hours on data entry, copy-pasting between tools, and manually generating reports. How it works: I build custom automation pipelines using Python, n8n, or direct API integrations. AI handles the intelligent parts — understanding unstructured data and making classification decisions — while traditional automation handles the mechanical parts.
AI-Powered Document Processing
Extracting structured data from invoices, contracts, receipts, and forms. Classifying documents. Summarizing long reports. Translating technical content across languages. What this solves: manual data entry from paper or PDF documents, hours spent reading and summarizing lengthy reports, inconsistent data extraction.
AI-Enhanced Product Features
Adding AI capabilities to your existing web application or SaaS product. Smart search, content recommendations, automated categorization, predictive analytics. What this solves: generic search that doesn't understand user intent, manual content tagging, missing opportunities that predictive analytics would catch.
See OmniShop →Related Work
AI Readiness Assessment
Not every process benefits from AI. I start by mapping your current workflows and identifying where AI creates genuine value vs. where simpler automation or process redesign would work better. The biggest waste in AI adoption isn't failed projects — it's successful implementations that automate a process nobody needed in the first place.
Workflow analysis, opportunity map, prioritized roadmapProof of Concept
Before committing to a full build, I create a working prototype that demonstrates the AI solution on your actual data. You see real results with real inputs before investing in production development.
Working prototype, 1-2 week turnaroundProduction Implementation
Full engineering of the AI solution — error handling, monitoring, fallback mechanisms, and integration with your existing systems. AI in production is fundamentally different from a demo: it needs to handle edge cases, scale under load, and degrade gracefully when the model returns unexpected results.
Production-ready system, CI/CD pipeline, documentationMonitoring & Optimization
AI systems need ongoing monitoring. Model outputs can drift, new edge cases emerge, and your business needs evolve. I set up monitoring dashboards and provide optimization support to keep the system performing.
Monitoring dashboard, performance reports, ongoing supportComprehensive review of workflows, AI opportunities, prioritized roadmap.
Custom AI chatbot connected to your knowledge base, deployed on your website or internal tools.
End-to-end automation of a specific business process, including AI components and integrations.
Adding AI capabilities to your existing web application — smart search, recommendations, document processing.
Why Work With an Independent AI Engineer
The AI space is full of agencies that resell API calls with a markup. I build actual AI systems — custom pipelines, fine-tuned prompts, RAG architectures — because I've been doing AI engineering since before ChatGPT made it mainstream.
What that means for you: you get an AI solution designed for your specific use case, not a generic chatbot widget with your logo on it. Every implementation is connected to your existing web platform and workflows, not a standalone tool that creates yet another silo.