AI Process Automation

Replace manual, repetitive work with LLM-powered pipelines that integrate with your existing stack.

Operations teams in mid-size businesses spend a significant portion of their working hours on tasks that are repetitive, rule-bound, and highly predictable — exactly the kind of work that large language models handle well. Document classification, data extraction from unstructured inputs, email triage, report generation, and structured data entry into ERP or CRM systems are all strong candidates for AI process automation. The challenge is not getting an LLM to perform these tasks in a demo. The challenge is building automation that performs reliably in production, handles edge cases gracefully, integrates with existing software without disrupting workflows, and gives teams visibility into what the system is doing — and why. That is the kind of AI process automation I build for businesses. I start every engagement with a process audit: which workflows are genuinely repetitive, which have clearly defined inputs and outputs, and which carry enough volume to justify the investment. Not everything should be automated. But for the processes that qualify, the return is almost always immediate and measurable — typically in hours of manual work recovered per week. Document automation is one of the highest-value areas. I build pipelines that ingest documents from email, shared drives, or upload interfaces — extract structured data using LLMs, validate against business rules, flag uncertain cases for human review, and write confirmed results into downstream systems. This covers invoices, contracts, delivery notes, support tickets, and regulatory filings. Triage and routing automation is another area where LLM-powered pipelines deliver fast results. Incoming emails and support tickets are classified, summarized, enriched with context from internal knowledge bases, and routed to the right team — with draft responses prepared for high-confidence cases. Human agents focus on the edge cases, not on sorting. Every pipeline I build includes observability from day one: logging of inputs, model outputs, classification confidence scores, and downstream actions. This is what separates a reliable business tool from a fragile prototype. When something behaves unexpectedly, you need to see exactly what happened and correct it — not guess. For businesses that handle sensitive data, I build automations on self-hosted infrastructure, keeping data entirely within your own environment. GDPR compliance is a first-class concern, not an afterthought.