AI That Replaces the Work
You Hate Doing.

The trap in AI automation is the tooling-first mindset. Someone reads about n8n, Make, or LangChain, picks a platform, and starts building. Six weeks later they have a workflow that does not solve the original problem because the process itself was the wrong shape. I work the other way around: process mapping first, tooling second. If the workflow can be solved with a $20/month tool and no AI, I will tell you. AI gets added only when it does something a rule-based system cannot.

My discovery sessions produce a one-page document for every workflow: the trigger event, the input data shape, the transformation steps, the output destination, the error states, the success criteria. Without that document, the implementation is guesswork. With it, building becomes mechanical. The fastest projects are the ones where the client can sign off on the process map in a single meeting.

n8n is my default orchestration platform for most projects. It runs self-hosted on the client infrastructure (data stays in-house), has first-class HTTP request and Code nodes for calling LLM APIs with custom prompts, and supports the kind of error handling Zapier cannot. For projects where the client already has Make or Zapier in production, I integrate with those platforms instead of forcing a migration. OpenAI and Anthropic Claude are my default LLMs, chosen per use case based on cost, latency, and quality. For data-sensitive deployments I run Llama or Mistral locally via Ollama.

Production reliability matters more than feature count. Every workflow I build has retry logic with exponential backoff, Slack or email alerts on failure, fallback paths for partial outages, and idempotency keys to prevent duplicate writes when a step retries. I have seen too many automations in production that silently swallow failures until a human notices three days later. The cost of fixing that is higher than building it right the first time.

The business context shapes the technical choices. Having built and scaled two SaaS products (Dropship.ai tracking 2M+ Shopify stores, SameAPI as a website traffic intelligence platform), I know where automation breaks at scale and design for those failure modes from the start. I do not pitch AI for the sake of AI. I build the smallest system that solves the actual operational problem, then iterate based on what the data shows after 30 days in production.

My AI automation service is for ecommerce operators, SaaS teams, and marketing agencies spending significant team hours on repetitive processes: lead routing, CRM updates, content production, support triage, reporting, data reconciliation. The ideal client has a clear manual process they want to automate, the technical infrastructure to support it (APIs, webhooks, or willingness to set them up), and an understanding that the first month is investment before the system delivers compounding returns.

I am not the right fit if you want to bolt AI onto a process you cannot clearly describe, or if you are looking for someone to set up ChatGPT and call it automation. The clients who get the most from my work are the ones who can describe what triggers a process, what happens in the middle, and what the output looks like. If you cannot do that yet, the $500 audit is the right starting point. We map the operational bottlenecks together before committing to a build.