When critical technology is stuck.

I take over high-stakes programs where legacy data, politics, regulation, and pressure have outgrown the plan. AI is one tool; delivery is the mandate.

Operator. YC alum. Turnaround and technology executive.

01

The Reality of Delivery

Large technology programs rarely fail because the strategy deck is weak. They fail when legacy data, vendor promises, internal politics, and regulatory exposure meet a deadline the organization can no longer move.

That is where I operate: after the easy answers are gone, before failure becomes the accepted outcome.

I bring Silicon Valley execution into traditional, high-consequence environments. Startup meets enterprise, without external consultants.

I take responsibility for the constraint, make the data usable, narrow the scope to the smallest live slice, and harden the system until executives, operators, and control functions can rely on it. AI matters when it helps the work ship.

02

How I Work

  1. Diagnose the constraint.

    Start with data quality, availability, permissions, and the rulebook before choosing a model.

  2. Ship the controlled slice.

    Put the smallest useful system into production fast enough for reality to test it.

  3. Harden for operation.

    Make it robust enough for users, audit, compliance, and the next organizational change.

03

Proof of Work

Production-grade AI where data is messy and compliance is non-negotiable — without consultants.

HSBC London (GB)

Banking: regulated cutovers in hours.

Context
Tier-1 banking. Partial S/4HANA cutovers usually mean weeks of systems-integrator work across legacy data, approvals, and change windows.
Execution
Built autonomous agents that compose and check migration logic end-to-end. Live in production. Cutovers in hours, not weeks, with no external consultants in the loop.
EMCO Hallein (AT)

Machine Tools: reliable answers from legacy data.

Context
€200M machine-tool manufacturer. Decades of maintenance records and machine telemetry were trapped in formats neither technicians nor downstream systems could reliably use.
Execution
Built the AI & Innovation function from zero. Converted fragmented machine data into a searchable operating surface for service teams. First-time-right answers in seconds; customer and employee satisfaction rose together.
Rheinmetall Düsseldorf (DE)

Defense: resilient communication under constraint.

Context
European defense. Highly-available, cryptographically resilient data communication under classified-grade constraints and non-negotiable integrity requirements.
Execution
Drove joint research mandates with TU München and ETH Zürich. Specifications translated operational, availability, and integrity thresholds into work engineers could build against.
YC Mountain View (US)

Deep-Tech: patented emotion-recognition IP.

Context
Co-founded Going Ninja, a deep-tech company built around patented emotion-recognition technology. From patent to global market standard.
Execution
Y Combinator S18 alum. Raised $80M in venture capital. Exit and integration to Volkswagen delivered without losing the product core.

04

Contact

I look forward to hearing from you. Reach me directly for a short personal exchange.

Felix Rascher

Palais Eschenbach
Eschenbachgasse 11
AT-1010 Vienna

Mobile+43 676 3309265

Mailcontact@rascher.online

LinkedInhttps://de.linkedin.com/in/felix-rascher

05

Operating Notes