AI & Automation
Computer vision and LLM pipelines that take a repetitive human task and make it a background job with a number attached.
What this looks like
A person reads supplier emails and retypes line items. A QC operator looks at trays of vials under a lamp. An accounts team matches invoices to purchase orders by eye. Those are the tasks worth automating, because they are high-volume, rule-shaped, and a machine makes fewer mistakes at 3 a.m.
How we build it
- Start with the measurable error. We do not ship an AI feature without a baseline and a target — accuracy, time saved, or exceptions per hundred.
- Human in the loop, always. The model produces a draft with a confidence; a person confirms the exceptions. Nothing silently changes a ledger.
- Local models where privacy matters. On-premise vision models for production-line imagery; hosted LLMs only where the data is not sensitive.
- Deployed as a desktop tool when that is simpler. A
.exeon the QC machine beats a web app nobody can reach from the factory floor.
Platforms
Python (OpenCV, NumPy, PyQt/PyInstaller), Node.js orchestration, n8n workflows, OpenRouter/OpenAI for language tasks.
Next step
Have a system that needs to exist?
Send the workflow that hurts most and we will tell you honestly what it takes to fix it.