Mastascusa Holdings · Build

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

Senior ML engineer, picking up where lightweight wrappers fall short. You get production-grade AI work — eval harness, monitoring, deployment doc all included — with the price and the deliverable on the page before the first call.

Scope my projectSee the three SKUs

Beta cohort · half off

The first three customers get 50% off in exchange for written permission to publish the engagement (anonymized fine) as a case study. Full price from customer #4 forward.

Three ways to work together

1 week

Automation Sprint

One specific thing shipped

$1,500

Beta: $750 · first 3 customers

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 a senior 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

$4,900

Beta: $2,450 · first 3 customers

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

$9,000

Beta: $4,500 · first 3 customers

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 a senior 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

Senior, not staffed-down

The person scoping the work is the person doing the work. Day-job: production ML engineer leading a 6-person AI imaging team. Zero handoff loss.Background

Not what you need?

Deploying where failure has consequences? Start with the readiness audit.

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 at the same SKU rate or 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.
Why is the price on the page?
Because hiding it wastes everyone's time. The fixed price is the differentiation against hourly freelancers and "talk to sales" consultancies. If the SKU is a fit, we move fast; if it isn't, you saved a discovery call.
What happens after the engagement ends?
Optional retainer ($4–6k/month): 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