Public-Data Systems · AI Automation · Product Engineering

Products that survive production.

Software products — web, mobile, backend — with the automation and infrastructure to keep them running. Designed, built, and shipped fast, and made to hold up.

How it works

Every engagement takes one of three shapes. Each step earns the next — start small, see the work, then decide.

01

The Audit

$3,500 fixed · 1–2 weeks

A written, prioritized map of where AI and automation actually pay off in your business — effort and value per item, plus the honest list of what isn't worth automating.

Your risk: a fixed price for a document you own outright. No obligation to build anything.

02

The Build

3–8 weeks · fixed outcome · milestone-billed

One scoped thing shipped to production — an automation, a pipeline, an AI integration, or an MVP. Working end-to-end skeleton in week one, acceptance criteria agreed up front.

Usually scoped straight from the audit's roadmap, so nothing is built on a guess.

03

The Retainer

Monthly · about a day a week · month-to-month

Senior engineering on tap: the systems keep running, failures get handled, and a short written status each month tells you what shipped and what's next. Scale up for a push, down after.

30-day notice, no lock-in. The person who built it is the person running it.

Priced to the outcome, not the hour — a fixed price for the audit, milestones for the build, a flat monthly rate for the retainer. You’ll always get a straight number before anything starts.

Selected work

Business operating system

Next.jsFirebaseVercel

A full internal operations platform — analytics aggregated across seven services, credential and product registries, live cron-cached data. (You're looking at its public face right now.)

Production automation engines

TypeScriptPlaywrightLLM

Browser-automation and AI-in-the-loop pipelines running twenty-four hours a day on containerized infrastructure — discovery, decisioning, and reporting, unattended.

Public-data pipelines

PythonETL

A public-records pipeline across thirty-plus state jurisdictions — cross-registry joins on a normalized dual key, per-source validation, and a readiness gate that decides which sources are trustworthy enough to act on. Cloned to a second market in weeks.

Carrier network automation

GitLab CIAnsible

Automated provisioning across a multi-market cable network; CI/CD pipelines that eliminated manual configuration errors, plus core-network migrations run in production.

Private cloud

ProxmoxVaultOllama

A high-availability Proxmox cluster behind a Palo Alto firewall — secrets management, full monitoring, and local language-model inference.

Who you're working with

You work directly with Will Compton, start to finish — the person who builds it also runs it. No account managers, no handoffs, no offshore team.

FAQ

What kind of work do you take on?

Product and MVP builds, AI integration, automation engineering, data pipelines, DevOps and infrastructure, and monitoring — from a single automation to a full product shipped end to end.

Do you work remotely?

Yes. Fully remote, working with teams across the United States.

What does an engagement look like?

Every engagement takes one of three shapes: a fixed-scope audit (one to two weeks, a written prioritized roadmap), a build (three to eight weeks, one scoped thing shipped to production), or a monthly retainer (about a day a week of senior engineering). Most clients start with the audit — it's the lowest-risk way to find out what's worth doing.

What does an engagement cost?

Priced to the outcome rather than the hour. The audit is $3,500 fixed — half at signing, half on delivery — and the full fee credits toward a build signed within 30 days. Builds are fixed-outcome and milestone-billed by scope; retainers are a flat monthly rate. Ask and you'll get a straight number.

Can you add AI to an existing product?

Yes — LLMs, agents, and retrieval integrated into your existing stack and workflow, built for production with the reliability and monitoring to match.

Can AI run on our own infrastructure?

Technically yes — open models run on your own hardware. But I don't take on private-AI deployments at the moment, so I'd be the wrong person to build it. I've written up the honest version of the trade-offs, including why most small businesses shouldn't self-host: /guides/private-ai-for-business

Who do you work with?

Founders, small teams, and operators who need senior engineering without a full-time hire — someone who can build the product and run the infrastructure under it.

Contact

Let’s build something that runs itself.

Tell me what you want built, or what’s manual, brittle, or on fire. If it’s a fit, you’ll get a straight answer on how to approach it and what it takes.

Prefer email? wcompton@comptonconsulting.net

wcompton@comptonconsulting.net
(614) 626-5103