Case study · crisis supply chain · delivered under pressure

Sanity Cares

From idea to public-health delivery under emergency constraints.

Sanity Cares was a compressed execution problem: design and deliver a compliant physical product into a healthcare system during a global supply shock. The hardest parts were not the idea; they were compliance, procurement, and manufacturing coordination across Irish and UK subcontractors.

01 / Context

Problem and constraints.

The problem

Urgent PPE shortage.

COVID-19 created a sudden need for protective equipment inside healthcare systems. The work required speed, but speed alone was not enough: the product had to be manufacturable, compliant, deliverable, and trusted.

Key constraints

Speed with no shortcuts.

  • Compliance and CE-marking.
  • Procurement into a healthcare system.
  • Subcontract manufacturing through Irish and UK partners.
  • Logistics, packaging, and delivery reliability.
  • Public-sector expectations during emergency conditions.
02 / Evidence

Role and evidence.

Sanity Cares face shield worn with PPE
fig.01 — CE-marked face shield in use.
Product proof

Real PPE product in use.

As co-founder, I coordinated product/design/compliance/manufacturing/logistics across a fast-moving supply chain. The public-safe proof is the delivered physical product and scale: €2.5m revenue and 1.625m CE-marked face shields delivered to Ireland’s HSE.

No private contracts, procurement documents, or compliance files are published here.

03 / Proof

What this proves.

Speed under pressure

Move from ambiguity to delivered product without waiting for perfect information.

Regulated supply-chain execution

Compliance, procurement, manufacturing, and delivery had to line up.

Partner coordination

External subcontractors were part of the operating system, not an afterthought.

Uncertainty tolerance

The context changed quickly; the execution rhythm had to adapt.

Compressed delivery under constraint is the same muscle I bring to AI builds: scope it, ship it, verify it.

Next proof.

Typo shows multi-year operating execution; the AI Operating System case shows how I use agents as practical leverage.