How Taito.ai Built Financial Infrastructure for Scale from Day One

The Challenge: Avoiding the Scaling Trap

Many early-stage companies fall into a predictable pattern: they build artisanal financial processes that work initially but become technical debt as they scale. Manual invoice operations, spreadsheet-based MRR calculations, and fragmented data systems might seem easier at first, but they don't scale with the business.

Juho Eräste, co-founder at Taito, recognized this trap early: "We're small now, and we could patch data manually. But to scale successfully, our company data needs to be solid from the start. It's far easier to set things up correctly from day one than to fix them later."

For a company focused on helping fast-growing organizations manage performance, this philosophy extended to their own operations. With their technology platform in R&D phase and investor reporting requirements still modest, they had a critical window to build the right foundation.

The Taito Approach: Automate Everything Non-Core

Taito's strategy is clear: anything outside their core product development should be automated. "Growing the company shouldn't directly mean growing headcount," Juho explains. "Admin and finance support can be automated as much as possible. Everything that's not core to our product, we want to automate."

This philosophy extends to their financial operations. With Stripe for payments and Procountor for accounting, they initially built MRR calculations manually by breaking down Stripe data and processing invoices by hand. While functional, this approach wouldn't survive scale.

What Matters Most: LLM Costs and Unit Economics

As an AI-powered product, Taito faces unique financial complexity. Their most critical operational metrics, tracked daily or weekly, center on product usage, as is essential for any early stage company. But equally important are their cost dynamics, particularly LLM expenses and COGS.

"Costs can easily get out of hand," Juho notes. "Is bringing in customers cash flow positive or negative? With LLM usage, sales costs, and go-to-market approach, we need clarity on whether growth investment makes sense. These metrics can't be a black box."

The team needs clear visibility into whether customer LTV is positive, and they want to avoid surprises. This requires connecting Stripe transactions & LLM API costs directly with financial reporting in real-time.

The Control Solution: Infrastructure for Growth

Control automated Taito's financial modeling. The platform handles:

• Readiness for multi-entity financial consolidation

• Integrated cost analysis 

• Real-time financial data

• Automated MRR tracking 

"What's interesting about Control is bringing automation to traditionally very manual tasks," says Juho. "In larger companies, you need a whole team for this. Having readiness for group structures is valuable for our future."

But Juho sees even broader potential: "I believe the future goes beyond data connectors to accounting systems. AI-assisted anomaly detection and insights: identifying cost changes faster and more effectively than manual review, that's the exciting direction. Taking raw data straight to C-suite and boardroom reporting makes perfect sense."

The Result: R&D Focus Without Financial Blind Spots

With Control handling financial consolidation and reporting, Taito's lean team stays focused on product development while maintaining complete visibility into their unit economics. They have:

• Clean, automated financial operations from day one, avoiding technical debt

• Real-time visibility into LLM costs and customer profitability

• Scalable infrastructure ready for growth without proportional headcount increases

• Data foundation supporting both operational and board-level reporting

Most importantly, they've eliminated the risk of financial surprises. As they scale toward product-market-fit, their financial infrastructure scales with them automatically.