status: shipping

Fractional AI-native engineering leadership, not slide decks.

Director-level engineering leadership with a track record of shipping production AI workflows, not just presenting on them. Advisory, implementation, and training for teams who want the same.

Start a conversation See what's shipped ↓
// what I do

Three ways to work together

Most engagements start with implementation, since it's the fastest way to prove value, then expand into advisory or training as trust builds.

01 · advisory

Strategy that ships

Fractional AI-strategy guidance for engineering leaders. Where to adopt AI-native workflows, where not to, and how to sequence it without disrupting a team that's already shipping.

  • An honest read on where AI-native practices actually fit your stack, and where they don't
  • A rollout sequence that doesn't stop in-flight work to retool
  • Direct access for the calls that come up mid-engagement, not a scheduled monthly check-in
02 · implementation

Hands-on build

Direct implementation of AI-augmented development workflows, internal tools, and automation, built inside your existing stack rather than bolted on top of it.

  • Working code shipped into your actual repo, not a proof-of-concept in a sandbox
  • Tools and automation scoped to the specific bottleneck your team names, not a generic template
  • Full test coverage, so what ships is maintainable once I'm not the one touching it
03 · training

Team enablement

Workshops and onboarding that get engineering teams fluent in AI-native tooling fast, grounded in practices that have already been run in production.

  • Sessions run against your codebase, not a generic slide deck
  • Paired work, so your team ships the next feature themselves before the engagement ends
  • Folded into the same retainer, not billed as a separate enablement line item
// how it works

How an engagement actually runs

Not a fixed pipeline. Every engagement looks different depending on what your team needs, but the shape below holds steady.

01
Tell me where you're stuck. A conversation about the actual bottleneck, not a sales call.
02
Implementation starts week one, and I'm the one writing the code. No handoff to a junior team; real code shipped early, in your actual stack, proves the fit faster than a scoping deck.
03
Advisory and training fold in as patterns emerge. Added where they're actually needed, not sold upfront as a bundle, and nothing ships until your team can run it without me.
04
Retainer continues month to month, one client at a time. No contract, it keeps going as long as it's compounding, and working with one client means the hours I commit are the hours you get.
// proof of work

Recently shipped

A running log of production systems built and launched solo, end to end: data pipelines, public APIs, and full-stack apps.

~/shipped/log
7d2a915 running Led a .NET/C# and Angular engineering team to 100% AI-native adoption, driving 5x-8x productivity gains, as Director of Engineering .NET · C# · Angular · Claude Code
a3f9c12 shipped Built a full data ingestion pipeline and public API for a live pricing reference platform, end to end, solo Laravel · MySQL · Redis · search indexing · Hetzner
e71b408 shipped Designed a volatility and anomaly detection engine on top of live pricing data, tested a manipulation-detection hypothesis, and retracted it when the data didn't support it Stats engine · condition modeling
c204ad9 shipped Shipped a subscription SaaS product in five defined phases with full test coverage, from spec to billing Laravel · Angular · Stripe · admin tooling
4bd77f1 shipped Built rapid internal tooling for a small resale operation to cut listing time and track inventory at the batch level Next.js · inventory tracking
// who's behind this

Dan Rovito

Dan Rovito, Director of Engineering
Dan Rovito
Director of Engineering · Cincinnati, OH

I'm a Director of Engineering at a SaaS platform, where I've spent the past two years pushing my team toward AI-native workflows well ahead of the industry curve, using tools like Claude Code as core infrastructure, not novelty.

Outside of that role, I design and ship complete production systems solo: data pipelines, public APIs, full-stack apps, and the infrastructure underneath them. That's not a side hobby I mention for color, it's the proof that what I recommend actually works, because I built it myself first.

Based in the Cincinnati metro area. I work with teams who want practical AI adoption they can point to, not another slide deck about the future of work.

5x-8x
Productivity gain across a 100% AI-native engineering team
5+
Production systems designed and shipped solo in the past year
1
Person doing the strategy, the build, and the training
// how this is different

Solo builder vs. agency

Comparison shopping is fair. Here's what actually changes when the person pitching the work is the same person doing it.

Dan (solo) Typical agency
Who writes the code Dan, every time Delegated to junior or offshore devs
Continuity One person, full context, start to finish Account managers rotate, context resets
Time to first shipped work Week one Discovery and scoping phases first
Cost structure Flat $4,500/mo, cancel anytime Day-rate or project-based, scope creep common
Team enablement Built into every engagement Often a separate line item or upsell
Capacity One client at a time Spread across many concurrent accounts
// pricing

One retainer, one client at a time

Retainer only, no one-off projects. It's the only structure that lets an engagement stay embedded enough to actually compound.

$4,500 / month
1 opening available
  • Advisory, implementation, and training under one retainer
  • Direct access, no account layers in between
  • Ongoing iteration as tools and practices evolve
  • No contracts, cancel anytime
// questions

Before you reach out

You have a full-time Director role. How does this actually work?

This runs alongside it, not instead of it, which is exactly why I only take one client. The hours are real, they're just bounded, and a single engagement gets all of them instead of being split thin.

What's actually different about "AI-native" versus just giving my team ChatGPT?

A subscription doesn't change how your team works, it just adds a tab. AI-native means the tools are wired into how code gets written, reviewed, and shipped, so the gains show up in velocity, not in a chat log nobody reads back.

Do you build custom software, or configure existing tools?

I write real code, in whatever stack your team already runs. If an off-the-shelf tool is genuinely the better answer for something, I'll say so, but the default is a system that's actually yours, not a workflow rented from a third party.

Why trust an engineering leader over an "AI consultant"?

Most AI consultants have never had to keep a team shipping through the change they're pitching. I run one. Every practice I bring to your team, I've already run against a real production codebase and a real deadline, not a slide.

I don't know exactly what needs to change yet. Is that a problem?

No, that's most of the first conversation. Tell me where your team is stuck, and I'll tell you honestly whether AI-native practices are actually the right fix or not.

What happens if it isn't working out?

No contract, cancel anytime. If a month in it's not delivering, that's a conversation, not a lock-in.

dan@ ~ %

Tell me where your engineering team is stuck.

If AI-native workflows can actually move that number, I'll tell you honestly. If they won't, I'll tell you that too.

Get in touch