How Inven Moved from Manual Exports to Automated Financial Modeling in Hours

Inven is one of the fastest-growing B2B SaaS companies in the Nordics, building an AI-native deal sourcing platform that helps M&A professionals uncover opportunities across 21 million companies globally. Founded in 2022 by former McKinsey and BCG consultants, Inven has raised €11.2M in Series A funding led by Ventech and Vendep Capital, with participation from Risto Siilasmaa's fund First Fellow Partners. Despite being based in Helsinki, they serve 950+ companies, with majority of revenue coming from the US market.

Run with a lean, modern approach, Inven is known for combining exceptional product development with strong commercial execution. Their energetic team operates from a beautiful new headquarters at Bulevardi 21 in central Helsinki, where we sat down with Joona Puro and Ella Norja from Inven's finance team.

The Challenge: Building Finance Operations That Scale 10x

Inven's two-person finance team faces a unique challenge: they need to build a finance function that can scale from their current size to €10-20M ARR, and potentially to €100M ARR, without proportionally scaling headcount.

"Our core objective is that the function scales without the team size scaling in the same proportion," explains Joona. "This requires automating processes as much as possible so we can focus on what matters most."

Before Control, their workflow created constant friction. Manual data exports from Stripe and Accounting software to Google Sheets formed the foundation of every analysis, forecast, and report. "We were constantly pulling data from different systems into spreadsheets," says Joona. "It was repetitive work that took time away from actual analysis and made our data less reliable."

The manual approach created multiple pain points:

• Daily data transfer work consuming time that should go to analysis

• Error-prone copy-paste processes compromising data reliability

• Limited scalability as the company grew rapidly

• Fragmented data sources requiring separate extraction from multiple systems

What Actually Matters: ARR Growth and Unit Economics

For a fast-scaling SaaS company, Inven tracks metrics that directly impact investor confidence and operational health:

• ARR and growth rate as the primary health indicator

• Net Revenue Retention (NRR) and Gross Revenue Retention (GRR) measuring customer satisfaction and expansion

• Burn rate development tracking path to profitability

While LLM costs interest the team given Inven's AI-native product, they're not currently a priority. "AWS costs don't scale with the business if R&D stays disciplined," Joona notes. The focus stays on core SaaS economics.

But getting clean visibility into these metrics required pulling data from multiple sources. Each extraction was manual, each analysis started from scratch.

The Solution: Automated Financial Modeling Pipeline

Control automated Inven's entire data consolidation workflow, connecting their accounting system and billing data directly to their financial models in Google Sheets. "Control reduces manual data transfer from one place to another, to spreadsheets," says Joona. "We get the information into a smarter format, and from there to wherever is needed. This all streamlines our analysis."

The implementation required close collaboration to handle Inven's specific data structures, but once configured, the system runs automatically:

• Automated daily data sync from all financial sources

• Clean, structured data ready for immediate analysis

• Error-free financial models built on reliable, auto-updating data

• MRR tracking and SaaS metrics calculated from accurate source data

"The team provided excellent support and best practices, helping us set up even difficult data sources," Joona recalls. "They answered hundreds of questions with a consultative approach."

The Results: Continuous Time Savings on Every Analysis

The impact isn't measured in one-time savings but in continuous operational efficiency. "We get continuous value," says Joona. "It saves us time on every forecast we build."

Key outcomes:

• 100% automated daily data updates across all systems

• Zero manual exports from accounting to spreadsheets

• More accurate data with no copy-paste errors

• Reliable foundation for forecasting, cash flow analysis, and investor reporting

The team now spends their time on actual financial analysis, building forecasts, analyzing trends, understanding drivers, rather than preparing data for analysis.

Looking Forward: From Data Integration to AI-Powered Insights

Inven's finance team sees the next frontier in how finance teams interact with their data. Rather than manually investigating cost changes or building scenario analyses, they envision asking natural questions and getting instant answers traced back to source transactions.

"I'd love to ask questions like 'Why did this cost increase?' or 'What is the financial impact if we cut a specific cost by 30%?' and get answers directly from the data," says Joona. The goal is moving from manual investigation to AI-powered financial intelligence that can explain changes, model scenarios, and surface insights automatically.

International Expansion

As Inven considers establishing subsidiaries when the time comes, they're building financial infrastructure that's ready to handle multiple entities. "SaaS doesn't necessarily need global presence everywhere," Joona explains. "But as the company grows, at some point it becomes relevant."

With Control's multi-entity consolidation capabilities already in place, Inven won't need to rebuild their financial operations when that expansion happens. The infrastructure scales with them.

Key Metrics:

• 100% automated daily data updates

• Zero manual exports from Stripe or accounting system to spreadsheets

• Continuous time savings on every forecast and analysis

• More accurate data with no copy-paste errors

• No need for internal data engineers for pipeline and data model setup and maintenance

• 2-person finance team scaling to €10-20M+ ARR