Why We Started Steele Nash

Andrew Schremp has spent his career inside B2B SaaS companies, not advising from the outside but embedded in the operations, making the decisions, and accountable for the results.

As an Operating Partner at Bow River Capital ($5B AUM), he led hands-on transformations across five portfolio companies: rebuilding GTM engines, overhauling pricing, restructuring teams, and installing the operational discipline that drives durable growth. The fund delivered top-decile returns (2.0x+ MOIC, ~35% IRR), and Andrew was in the room for all of it. Across those engagements he doubled ARR in under 18 months, drove 40%+ YoY bookings growth, removed $8M+ from cost structures, grew pipeline coverage from under 1x to over 4x, and lifted net revenue retention to 121%.

What he learned along the way: the biggest drag on performance isn't strategy. It's the manual work that quietly consumes your best people — the data entry, the copy-paste workflows, the reporting cycles that take days instead of minutes. That's where AI creates real leverage. Not in flashy demos, but in the unglamorous operational work that runs every business.

He built and deployed AI-powered workflows across multiple portfolio companies, automating outbound prospecting, accelerating document processing, and streamlining reporting. He engineered them as scalable, repeatable frameworks that new team members could run without technical support. These weren't one-off builds. They were systems designed to compound: the same AI-augmented outbound motion that drove 174% YoY SaaS growth at one company was rebuilt and redeployed across the portfolio. He measured the impact directly against the P&L every time.

Before private equity, Andrew founded and sold Health Sqyre, a health tech platform he raised $5M for, scaled to all 50 states with 1,000+ payer integrations and 40,000+ transactions, then exited in a strategic acquisition to VGM Group.

At Steele Nash, he brings that operator's lens to every engagement. The goal is the same one he's always had: find where the time and money are being lost, build something that fixes it, make it repeatable, and measure the difference.

Yannick Dawant has spent 10+ years building the kind of software that has to work: financial infrastructure, payroll systems, and data platforms where errors aren't acceptable and reliability isn't optional.

He's led engineering teams on mission-critical infrastructure — the systems that process wages, calculate taxes, and power compliance workflows for businesses at scale. These systems are held to an uncompromising standard: accurate, compliant, and always available. That's a different discipline from building features. It's the discipline of building things that run.

He's also led large-scale infrastructure modernization efforts — taking bloated, over-engineered systems and rebuilding them for simplicity and resilience without sacrificing capability. That instinct, finding where complexity has outgrown its usefulness and cutting it without breaking anything, is exactly how he approaches AI workflow design.

Earlier in his career, Yannick designed and built analytics platforms from scratch: data pipelines, real-time ingestion systems, and production reporting infrastructure. He's operated high-volume systems and presented technical work at industry conferences.

As a technical advisor to Steele Nash — while remaining a full-time engineer at Gusto — he brings that same standard to every workflow we build. The goal isn't to ship something that looks good in a demo. It's to build something that runs quietly, handles edge cases, and keeps working six months after launch — without requiring a technical team to maintain it.