InnovWave AS — AI Agent for Smart Capital Decisions

Inspiration

InnovWave AS was inspired by a simple but critical problem: small and medium businesses don’t fail because they lack profit — they fail because they lose control of their cash flow.

In many real-world cases, financial decisions are still made using spreadsheets, intuition, or delayed accounting reports. There is no real-time visibility into what is safe to spend, when to restock, or how future cash will evolve.

At the same time, AI agents are becoming more powerful — but they lack secure access to real user data.

We wanted to bridge that gap: build an intelligent financial agent that can act on behalf of users, securely.

What it does

InnovWave AS is an AI-powered financial command center for SMEs.

It provides:

Real-time cash position tracking
Forward cashflow projections
Monthly surplus estimation
Invoice approval and payment simulation
AI-generated financial insights and recommendations

The AI agent can interpret financial data and answer key questions like:

"Can I afford this purchase?"
"What happens to my cash after restocking?"
"Where am I losing money?"

All interactions are secured through Auth0, ensuring that the agent operates safely with user-authorized access.

How we built it

We built InnovWave AS using a modern full-stack architecture:

Frontend: Next.js, TypeScript, Tailwind CSS
Data visualization: Recharts
Animations: Framer Motion
Backend logic: API routes + financial computation modules
AI layer: LLM-powered insights engine
Authentication: Auth0 with Token Vault simulation for secure token handling

We integrated financial modeling logic inspired by working capital systems, including:

Surplus = Cash + Expected Inflows − Committed Outflows − Restock Forecast

The system dynamically computes financial metrics and feeds them into an AI agent for interpretation.

Challenges we ran into

One of the biggest challenges was balancing complexity and clarity.

Financial systems can easily become overly complicated. We had to carefully select only the most critical metrics that actually help decision-making.

Another challenge was integrating secure authentication while maintaining a smooth user experience. Ensuring that AI agents operate safely with delegated permissions required careful design using Auth0 concepts.

Finally, merging multiple systems (UI, AI, financial logic) into a coherent product within a limited timeframe required strict prioritization.

Accomplishments that we're proud of

Building a fully functional financial dashboard with real-time insights
Creating a clear and actionable AI agent experience
Successfully integrating secure authentication using Auth0
Delivering a product that feels like a real SaaS platform, not just a prototype

What we learned

We learned that clarity beats complexity.

Users don’t need more data — they need better decisions.

We also learned how critical security is when building AI systems that interact with sensitive data. Auth0 allowed us to design a system where agents can act on behalf of users safely.

What's next for InnovWave AS

Next steps include:

Real bank integrations (Plaid or Open Banking APIs)
Advanced forecasting with seasonality detection
Automated payment scheduling
Voice-based AI financial assistant
Smarter capital optimization strategies

Our long-term vision is to turn InnovWave into a full financial operating system for SMEs — not just showing what is happening, but actively managing how money flows.

Bonus Blog Post

Building InnovWave AS in a hackathon setting was a lesson in focus and execution.

One of the most interesting challenges was integrating security into an AI-driven system. AI agents are powerful, but without proper access control, they can become unsafe or unreliable.

This is where Auth0 Token Vault plays a key role. Instead of exposing credentials or managing tokens manually, we designed the system so that the AI agent operates with delegated permissions. This allows the agent to act on behalf of the user while maintaining strict security boundaries.

Another challenge was deciding what NOT to build. Financial systems can quickly become overwhelming. We focused only on actionable metrics like surplus, cashflow projection, and restock impact.

By combining deterministic financial logic with AI interpretation, we avoided hallucinated numbers while still providing smart insights.

The result is a system that feels both intelligent and reliable — a rare balance in AI-powered products.

This project showed us that the future of AI is not just about intelligence, but about trust.