Built for the companies the AI industry skipped.

Roughly 200,000 American mid-market companies produce a third of private-sector GDP. Global integrators won't touch them below $150K minimums. Freelancers can't hold context across a multi-quarter deployment. Self-serve tools produce demos that die before production. We were built for exactly this gap.

For plants that can't hire their way out.

Manufacturing needs up to 3.8 million new workers by 2033 — and 1.9 million of those positions may go unfilled. Meanwhile only about 13% of U.S. manufacturers have integrated AI into operations. Those aren't two problems. A plant that can't hire needs every existing worker producing more.

What that looks like on your floor:

  • Predictive maintenance over the sensors you already have — unplanned downtime costs U.S. manufacturers an estimated $50 billion a year; documented AI deployments have cut downtime by up to half.
  • ERP and floor-system integration — so the data you already collect starts answering questions.
  • Automated extraction of unstructured data — work orders, inspection notes, supplier documents.

Nobody gets replaced. Your scarce workers get more productive, and unfilled positions get less damaging.

For operators losing margin to empty miles.

Transportation and warehousing employ 6.6 million Americans, and the mid-market layer — regional carriers, 3PLs, distributors — still runs on manual carrier portals, disconnected inventory systems, and dispatchers doing data entry.

What that looks like on your dispatch board:

  • Routing and load-matching layers that give a mid-sized operator the optimization only national carriers could previously afford.
  • Carrier-portal automation that returns your best dispatchers to work worth their time.
  • Connected inventory visibilityacross systems that don't talk to each other.

How an engagement actually lands.

A mid-market machine shop — about 180 employees, three lines that stop without warning, an ERP nobody trusts. Scan and Identify locate the highest-leverage problem in two weeks: not a new system, but a predictive-maintenance layer over sensors the plant already has. Ground takes a baseline — unplanned stoppages per month, and their dollar cost — before a line of code is written. Navigate builds the layer. Activate trains the floor supervisors who receive the alerts. Lock watches for drift.

The plant doesn't shed workers. It keeps the line running and books the saved hours as capacity it can finally sell.

Illustrative engagement — this describes our pattern of work, not a named client.

We don't sell to everyone who calls.

Every engagement passes four gates:

  1. 1

    Your company has 50–500 employees.

  2. 2

    The person at the table is an owner, CEO, COO, or operations director with budget authority.

  3. 3

    There's a concrete operational problem with an estimated cost — not curiosity about AI.

  4. 4

    You're ready to invest in solving it, not explore it.

If that's you, the first conversation is free and takes fifteen minutes.

We work first with manufacturing (predictive maintenance, ERP/floor integration, unstructured-data extraction) and logistics (routing, carrier-portal automation, dispatch productivity) — the two sectors where the labor math is most unforgiving. The method itself is sector-agnostic.

Talk to Natalia — free 15 min