Accounting, document management, reporting, liquidity planning and audit processes — automated, transparent and with clear early-warning signals.
Use case 01
Accounting, cash flow, receivables, payables, tax and reporting KPIs — condensed in one interface, always up to date.
The AI finance dashboard condenses operational and strategic KPIs from accounting, controlling, treasury and tax into one interface. Trends, deviations and early-warning indicators are surfaced automatically — without manual reporting effort.
Management, CFO and accounting can see the company's financial position at any time — and intervene exactly where needed.
How it works
We connect accounting, ERP, banking and other financial sources — securely, locally and without data leaving your company.
KPIs, reports and early-warning rules are tailored to your accounting logic and reporting obligations.
We support and optimise continuously — the dashboard grows with your processes and requirements.
Use case 02
A direct contact point for accounting, controlling and tax — based on your own data, receipts and transactions.
The AI assistant answers concrete questions about postings, open items, KPIs or tax topics — context-aware and traceable, based on your internal data and receipts.
Accounting and controlling save time on research, inquiries and preparing analyses. Complex questions are answered in natural language, with references to the underlying documents.
Use case 03
Tax files, contracts, policies, balance sheets and experience — accessible through a single question.
The knowledge management platform provides an intelligent, central access point to all finance-relevant documents: balance sheets, tax documentation, contracts, policies, internal guidelines and experience from past closings.
Research times drop to minutes, new employees become productive faster, and senior know-how is preserved when people leave. Knowledge becomes a shared tool for the entire finance team.
Use case 04
The AI agents shown serve as examples and inspiration. Which agents actually make sense depends on the industry, company size, processes and individual requirements – the possible applications are virtually unlimited.
For Austrian SMEs this is probably the most common use case of all. The biggest effort lies not in the bookkeeping itself but in coordinating: following up, reminding, collecting receipts, clarifying. These AI agents take over exactly those tasks that today still often run by hand.
Chasing by email/phone, manual reminders, document tracking.
Detects missing receipts for bank, credit card and expenses, requests them from employees automatically and makes them available to accounting.
~10 h savings / month
Pays for itself in 8 to 11 months
Dunning, follow-up calls, documenting payment agreements.
Monitors open receivables, sends personalised payment reminders, documents agreements and follows up on critical cases.
~15 h savings / month
Pays for itself in 7 to 10 months
Invoice checking, approval tracking, supplier queries, exception cases.
Checks incoming invoices for completeness and purchase-order reference, detects discrepancies and guides the internal approval process end to end.
~14 h savings / month
Pays for itself in 9 to 12 months
Manual reconciliations, email reminders, checklists in Excel.
Coordinates month-end and quarter-end closes, requests missing information from departments and monitors the processing status.
~7 h savings / month
Pays for itself in 11 to 14 months
Manual checking, looking things up in the VAT Act, queries, correction postings.
Checks receipts for correct tax codes (reverse charge, intra-Community acquisition), validates VAT IDs in real time and provides correction suggestions.
~4 h savings / month
Pays for itself in 19 to 22 months
Each of these automations works both for companies with in-house accounting and for companies with an external tax advisor. The tax advisor is not replaced but seamlessly integrated into the processes where needed.
Potential time savings versus today's largely manual effort. Guideline values, depending on volume, data quality and process maturity.