Mastascusa Holdings · Build

AI automation, shipped in days. Fixed scope. Fixed quote.

AI work built to hold up in production, not just in the demo. Tests, monitoring, and a deployment doc come with every engagement, and the scope, deliverables, and price are agreed in writing before any work starts.

Scope my project Not sure yet? Start with a Quick Check

Start here · 1 hour + written plan

AI Quick Check

Not sure what to build yet? We spend an hour on how your business runs today: the tools, the data, and the repetitive work. Within three days you get a two-page written plan.

The three places AI would actually help, and the ones where it wouldn't
What has to be cleaned up or written down first
Rough effort for each, so you know what a first project looks like

Credited in full toward an Automation Sprint if you start one within 30 days.

Book a Quick Check

Then, when you know what to build

1 week

Automation Sprint

One specific thing shipped

Quoted after scoping · fixed price, in writing

You have one AI integration in mind — a Claude-powered support triage, a smart classifier, an LLM-backed search. We scope it on the call, build it the same week, and ship it with a written handoff.

What you walk away with

Working integration deployed to your environment
Prompt + tool schemas checked into your repo
Smoke-test eval harness covering the golden-path cases
Written deployment doc + monitoring notes

You know what you want built, you just need an experienced pair of hands to ship it without disrupting your roadmap.

Talk to me about this

2 weeks

AI Foundation

Production-grade AI stack stood up

Quoted after scoping · fixed price, in writing

You're going to ship multiple AI features over the next quarter. The Foundation establishes the production discipline up front — the eval harness, the monitoring, the deployment story — so you don't have to retrofit it under pressure.

What you walk away with

LLM API integration with structured tool-use scaffolding
Eval harness with regression tests for every prompt change
Cost + latency + token monitoring with alerting
Deployment doc + rollback runbook
One reference feature shipped on top of the stack

You're past the prototype and want production discipline before you scale AI usage across the product.

Talk to me about this

4 weeks part-time

Embedded Month

One AI feature shipped end-to-end

Quoted after scoping · fixed price, in writing

I shadow your founders for a month, scope to ship, and build a meaningful AI feature in collaboration with your team — code review, eval design, monitoring, the full path. You walk away with the feature and the playbook.

What you walk away with

One end-to-end AI feature shipped to production
Eval suite + monitoring instrumented and handed off
Code reviewed alongside your team in your stack
Architectural notes for the next 2–3 features in the roadmap
On-call coverage for the first 2 weeks post-ship

You want an ML engineer working alongside the team for a month, not a contractor on the outside.

Talk to me about this

What "ML-engineer-grade" actually means

Eval before deployment

Every Foundation and Embedded engagement ships with a regression eval harness. Prompt changes never go to production without a green run. Most low-priced LLM-integration offers skip this entirely — and that's why their work doesn't hold up under real production load.

Monitoring as a default

Cost, latency, token usage, error rate, and content-flag rate are instrumented and alerting before the first user hits the feature. You learn about a degradation from your dashboard, not from a customer.

Built against the rubric we publish

The same maturity rubric we use to audit other companies' AI deployments is the standard the build is held to.Read it

One engineer, no handoffs

The person scoping the work is the person doing the work. I run Mastascusa Holdings full-time, and every engagement is mine end to end.Background

Not what you need?

Not sure what to build yet? A two-week Readiness Review tells you what's worth doing first.

Common questions

What if the scope grows mid-engagement?: The scope is fixed. If the work changes, we have a 5-minute conversation, agree to a change order in writing (priced against the original quote, or as a clearly bounded add-on), and I keep building. No surprise invoices.
What stacks do you work in?: TypeScript / Node, Python, Go. Anthropic, OpenAI, and open models. Vercel, Cloudflare, AWS, GCP. If your stack isn't here, ask — I'll be honest about whether I'm a fit.
Do I need to sign anything before the call?: No. The first call is free. We sign a mutual NDA + a one-page SOW only when we agree on the scope.
How is it priced?: Every engagement is quoted at a fixed price once we've scoped it — usually within a day of the first call. You get one number for one written scope, not an hourly meter. If the quote doesn't work for you, you've spent nothing but the call.
What happens after the engagement ends?: Optional monthly retainer: office hours, eval review, on-call advisory. Most customers start with a Sprint, upgrade to a Foundation a month later, then settle into a retainer. No pressure — the engagement stands on its own.

Scope your project

Tell me what you want built.

A few sentences is enough. I read every brief myself and reply with a proposed SKU, a price, and the soonest start window. No discovery-deck dance, no qualifying call to qualify the qualifying call.

Or email directly: kevin@mastascusa.com