The Thesis
SliceCFO is an intent engineering firm for growth-stage companies. We embed a senior finance practitioner alongside a founder, then systematically encode that founder's organizational knowledge into AI infrastructure that compounds over time. The visible output is a Head of Finance. The invisible infrastructure is an organizational intelligence layer.
You start with a Head of Finance. You end with a second brain for your enterprise. Not a roadmap - what happens automatically when intelligence accumulates institutional memory starting from the function that sees everything.
Why Finance First
Finance is the natural entry point for building intelligence infrastructure across an entire company. Four reasons.
Revenue, margin, burn, runway - how founders make every decision. Speaking dollars from Day 1 means immediately useful for any strategic question, not just one department's workflow.
Finance touches payroll (people), revenue (sales), COGS (ops), R&D spend (product). Starting here gives a cross-functional view of the entire enterprise without needing separate integrations for each department.
Founders can tolerate messy CRM or disorganized project board. They cannot tolerate not knowing cash position, missing payroll, botching a close. Finance pain is existential. The urgency is built in.
Once the system understands the business through financial data, expanding into ops, strategy, and workforce decisions is natural. "Your COGS are up 12% - want me to look at what's driving it?" is the intelligence layer growing from Head of Finance into an enterprise brain.
The Market Gap
87% of CFOs say AI is critical. Over $500M has flowed into AI-native finance plumbing. Yet nobody systematically extracts organizational knowledge, encodes it into machine-actionable infrastructure, and builds a compounding intelligence layer. Two camps exist: "AI replaces the task" (point solutions) and "humans do strategy, AI does grunt work" (tribal knowledge walks out the door). Neither builds anything that lasts. That's the gap we fill.
What We Build
Every engagement follows the same arc. Three months of hands-on build, then the founder runs it. The system gets smarter every month because it's learning from real decisions and real context.
Ty rebuilds the finance function: invoicing, close, payroll, dashboard, data infrastructure. Bookkeepers trained. Open-source infrastructure the client owns regardless of what happens next. $45K for a finance rebuild is market rate - except this one comes with AI infrastructure built in.
The founder gets a dedicated AI instance that answers finance questions from real data. Connected to the full tool stack, trained on the business context, ready to operate.
Scope broadens beyond routine finance. Scenario modeling, transition economics, strategic analysis. Ty shifts from building to oversight and strategy. The system starts connecting dots across departments.
Twelve months of institutional memory. The system operates across finance, ops, and strategy. It knows the business well enough to flag risks, surface opportunities, and draft analysis the founder actually trusts.
The solution library keeps growing. The founder barely thinks about infrastructure - they just ask questions and get answers grounded in their own data, their own context, their own history. The system compounds because it never forgets and never drops a thread.
What We Are Not
We don't match companies with finance contractors. There's one practitioner (Ty), one AI co-founder (Cai), and the infrastructure they build together. When we leave, the system stays and keeps running. Fractional CFOs leave and take the knowledge with them.
We don't sell dashboards or charge monthly SaaS fees. The infrastructure is built in code, version-controlled, and owned by the founder. No vendor lock-in, no data trapped in someone else's product, no annual renewal where the price goes up and the features stay the same.
We don't advise companies on how to adopt AI. We build the infrastructure and hand over the keys. The system doesn't depend on us after the engagement - it depends on the founder's own data, context, and decisions accumulating over time.
$5-50M ARR, finance complexity outgrowing the spreadsheets, a founder who thinks in systems? Start a conversation with Cai. If it's a match, Cai will set up a call with Ty.