Custom AI Automation Developer — Build, Not Wrap
A custom AI automation developer engineers end-to-end workflows that use AI as a component — not buying a vendor wrapper, not stitching together a Zapier integration, but writing the model, the pipeline, and the operator tooling. I work as a senior custom AI automation developer with 25 years in computer science, U.S. Army veteran, single-principal delivery. The buyer owns the model, the pipeline, the source documents, the deployment, and the operating runbook outright. No per-token tax, no vendor lock-in.
What "custom AI automation" covers
- LLM-driven internal workflows. Customer-service triage, claim review, contract summarization, document classification — all running on a model the buyer owns.
- Document-extraction pipelines. Structured data out of PDFs, scanned forms, email attachments, and other unstructured inputs.
- RAG-backed customer-service bots. Auditable answers with citations, versioned source documents, audit trails.
- Agent loops with eval harnesses. Tool-use workflows where the agent makes decisions and the eval harness keeps them honest.
- Self-hosted deployment. The buyer's hardware, the buyer's cloud, the buyer's compliance posture.
When NOT to build custom
The honest brand-voice answer: most buyers should not. If you are validating a hypothesis, a wrapper is correct. If you are running at low volume, a wrapper is correct. If your per-month AI spend is under $5K, a wrapper is correct. The economics that justify custom AI automation start when the buyer has a known workflow, predictable volume, $20K+/month in per-token fees, or a compliance posture that rules out third-party APIs. I will say this honestly in the scoping call. I would rather lose the engagement than ship the wrong shape.
The build / buy / wrap honest matrix
| Shape | When it is correct | What it costs |
|---|---|---|
| Wrap (third-party API) | Hypothesis validation, low volume, fast iteration | Per-token, high; capex zero |
| Buy (vendor product) | Common workflow, vendor differentiation does not matter | Per-seat or per-month, medium; capex low |
| Build (custom self-model) | Known workflow, predictable volume, compliance constraints, $20K+/month wrapper bill | One-time capex high; per-request zero after deploy |
Who hires me for custom AI automation
Operations VPs whose per-month wrapper bill has crossed $20K and who want to know the build alternative. Regulated-industry CTOs (healthcare, energy, financial services) whose compliance posture rules out third-party APIs. Founders building AI-native SaaS who need a self-hosted model in the offering. Microsoft-shop enterprises whose Azure ML stack covers training but not the workflow-automation surface.
Are you available freelance for custom AI automation?
Yes — one engagement at a time, fully present. Custom AI engagements typically run 3-6 months because the eval / build / deploy / monitoring loop is real work.
What is your hourly rate?
Bespoke, scoped per engagement, written into SOW before contract. Custom AI engagements are typically priced per-phase rather than hourly: eval harness; first working model; production deploy; monitoring.
How is this different from buying an AI workflow product?
A vendor workflow product is a wrapper with a UI on top. The buyer pays per-month or per-seat forever, the workflow is whatever the vendor supports, and the data leaves the buyer's premises. Custom AI automation produces a workflow the buyer owns outright — the model, the pipeline, the source documents, the deployment, the operating runbook. The right answer depends on the buyer's specific economics; this page exists for the buyers for whom the custom answer is right.
Can the system run on our own hardware?
Yes — self-hosted deployment is one of the more common engagement shapes. Healthcare, energy, and financial-services buyers whose compliance posture rules out third-party APIs lean on this regularly.
Do you ship monitoring + operating tools with the automation?
Yes — that is the difference between a demo and a production workflow. Drift detection, golden-set regression suites, cost guardrails, deploy / rollback, and operating dashboards are part of every engagement that ships to production.
Scope This Engagement
Single principal, plan first, working code on every checkpoint.
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