Agentic AI Training Environments

Synthetic Procure-to-Pay Dataset

Customer Story 01 · Agentic AI Training Environments

The Challenge:

A global enterprise automating its finance operations was training an AI agent to run its entire procure-to-pay cycle inside its ERP. Real company data was off limits because it was confidential, and it couldn't show exactly where errors occurred, so the agent's performance could be claimed but never proven.

Effective Solution:

Volga built a complete synthetic company, with suppliers, contracts, and transactions that reconciled to the cent from order through payment, so no confidential data was ever needed. Every document was mirrored one-to-one in a matching SAP S/4HANA layer. Volga planted errors on purpose and recorded each one, giving the client the answer key its agent had been missing. The dataset could be loaded straight into the client's own ERP.

Results:

For the first time, the client could prove how well its system works, not just claim it.

1,000+ documents across 215 complete procure-to-pay cycles, every figure matching to the cent

No real company data used, with personal details fully removed

35 planted errors, each recorded, so every catch, miss, and false alarm could be scored

30 SAP S/4HANA tables, ready to load into the client's own system

Every document traceable from start to finish

Emotion-Aware Dialogue Datasets

Customer Story 02 · Agentic AI Training Environments

The Challenge:

A conversational AI developer wanted a chatbot that talks like a real person, with the right emotion, tone, and cultural feel, not just correct grammar. That meant fresh, native-level conversations in many languages, labelled well enough to train on.

Effective Solution:

Volga paired native-speaking contributors by age, profession, and communication style, gave them a scenario, and had them chat in real time. Every message was tagged for emotion, tone, and intent, then checked by automated tools and human reviewers before delivery as clean JSON.

Results:

A chatbot that once sounded scripted now sounds like a real person, whether it's a boardroom meeting or friends joking around.

50,000+ messages written from scratch by native speakers

10+ languages, each with its own slang, humor, and local feel

Every message labelled for emotion, tone, and intent, so the data shows how and why things were said, not just what

Real back-and-forth conversations, with hesitation, sarcasm, and empathy

Automating Intercompany Netting Forecasts for Treasury

Customer Story 03 · Agentic AI Training Environments

Volga Partners · Use caseFilm coming soonIntercompany netting, forecast daily.Automated forecasting for treasury

The Challenge:

A global e-commerce leader’s treasury team built its monthly intercompany netting forecast by hand in spreadsheets. It was slow and error-prone, and mistakes meant either over-hedging or leaving money exposed. Everything also had to stay inside the company’s own systems.

Effective Solution:

In 14 weeks, Volga replaced the spreadsheets with an automated forecast that first predicts which entity pairs will move money, then how much. Every forecast shows what drove the number, so the team can question it. It all ran inside the client’s environment, with no outside systems involved.

Results:

Treasury now has a forecast it can rerun every day and check line by line.

About 90% accuracy in predicting which entity pairs will be active

Monthly error of just low to mid single digits on the highest-value corridor, tested on months the forecast had never seen

Worked on a second currency corridor with no special tuning

14 weeks from start to a working proof of concept

No company data left the client’s environment

Agentic AI Training Environments

Synthetic Procure-to-Pay Dataset

The Challenge:

Effective Solution:

Results:

Emotion-Aware Dialogue Datasets

The Challenge:

Effective Solution:

Results:

Automating Intercompany Netting Forecasts for Treasury

The Challenge:

Effective Solution:

Results:

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