I design and deploy self-running AI systems for businesses — automated workflows, AI assistants, RAG, and data training — so your team monitors the work instead of doing it by hand.
In McKinsey's 2025 survey, 88% of organizations were using AI in at least one business function — yet only about 6% reached real bottom-line impact, and just 1% considered their deployment "mature." The gap isn't the technology. It's process design, integration, and keeping the system running reliably over time. That's exactly what I do.
Email, orders, data, reports — connect the tools you already use with n8n and OpenClaw into a single automated process. Trigger it once; the system does the rest and flags you when something breaks.
Answers customers, searches internal knowledge, handles repetitive tasks — grounded in your own documents and voice. Not a generic chatbot that guesses.
The assistant retrieves directly from your knowledge base, processes, and products — answering accurately with citations, and sharply cutting down on made-up information.
Collect, clean, and structure the data so AI learns the right thing. The data foundation decides most of the output quality — and it's the part most people skip.
I hand over the system with a monitoring dashboard and documentation, and train your team to run and watch it themselves. The goal is for you to monitor the system, not to hire someone to operate it every day.
AI bills balloon when everything calls a frontier model. I tier the work so 70–80% of tasks run at near-zero cost, reserving expensive models for the decisions that genuinely need them.
Cron, API calls, rule-based data handling — no inference cost. The stable foundation.
High-volume language work on lightweight or local models — fast, cheap, private.
Used only for orchestration and genuinely complex decisions. Expensive, but rarely called.
Tools chosen for the problem, not the hype. Caching, batching, context compression, and tiered models keep the AI bill from climbing as you scale — built to keep running long-term, not a demo that looks great and then costs a fortune.
Scope and timeline are fixed before we start — you know exactly what you get and when, with no mid-project surprises.
Review your current process, find the 3 bottlenecks costing the most time and money, and decide what to automate first.
A system blueprint, tool and tiered-model choices, with estimated time saved and monthly running cost.
Build the workflows, assistant, and RAG; integrate with your existing tools; test on your real data until it runs reliably.
Deliver the self-running system with a monitoring dashboard, train your team, and watch the early period to ensure stability.
I build and run self-operating systems for my own projects — where every failure is a real cost to me. "Monitor not operate" isn't a demo slide; it's how I work every day.
Ideal for: businesses that want to test and prove the value before a bigger investment.
Ideal for: businesses that want AI to actually handle customers or internal lookup.
Ideal for: businesses that want AI as long-term operating infrastructure.
Every package starts with a free audit and ships with a monitoring dashboard — you monitor, you don't operate.
Yes. We start with the single process eating the most time, prove it works, then expand. No large upfront investment required.
We can run local or private models so your data never leaves your systems. For sensitive data, that's the default approach I recommend.
You don't need to be. I hand over a dashboard and documentation so all you do is monitor — the system handles execution. That's the whole "monitor not operate" idea.
The first workflow usually ships in 1–2 weeks. Assistants and RAG depend on document volume. A full system rolls out in phases so you see value early.
That's what I optimize from the design stage. A layered architecture keeps 70–80% of tasks near-free and reserves expensive models for the decisions that truly need them.
Send a short description of the process you want to automate. I'll reply with 3 concrete proposals — no strings attached.
onmeevn@gmail.com →