Agentic team members are now a reality for every governance team.

Werner and Wilma break down the barrier to SAP data analysis
and continuous monitoring, giving your team data science
and AI capabilities right now.

Meet the agents

Werner and Wilma are our most requested agentic team.

They bring decades of SAP data science know-how and governance toolset. They handle exhausting, repetitive work like SAP data crunching, automated monitoring,
and filtering out false positives.

This allows your team to focus on things humans do best: see the bigger picture, make the final judgment, and collaborate across the organization.

Talk to a human

Schedule a no-commitment call to learn if Agents are a good fit your team.

Group 40 (1) Group 1155

Wilma and Werner

When you add Werner to your team, you make SAP data analytics and continuous monitoring possible for your team. With Wilma on your team, you also put critical insights right at your team’s fingertips.
Here’s how the agents do it.

Werner

Your advanced SAP data expert

As part of your team, Werner exports, labels,
and analyzes SAP data. He also brings AI analytics,
continuous monitoring, and SAP attachment retrieval
to the table.
What does it mean in practice?
Sifting through thousands of data hits manually
probably sounds soul-crushing to your team.
But for Werner? He loves it, and he’s excellent at it.

werner
Watch our agent introduce himself

Werner’s superpowers

Vector

On-prem,

with no Internet access

machine-learning

Prioritizing analysis

results with Machine Learning

attachment

Pulling

SAP attachments

star-ai

Both AI and

non-AI options

run

Perfect to start your journey


with Continuous Monitoring

payment

Perfect for Duplicate

Payment analysis

Experience
for yourself

Access our Discovery Room
to watch a 6-minute video of Werner
and Wilma analyze a real dataset
for a specific use case.

Watch the video (6:40)

Wilma

Your wingwoman

Wilma is your team’s wingwoman whenever you work with analytics results produced by Werner. As part of your team, Wilma is at your fingertips to help your team understand, interpret, prioritize, or even follow-up on SAP data analytics.

Wilma speaks the SAP tech jargon just as well as business language and will help your team make sense of the results and find the right questions and answers.

Wilma
Watch our agent introduce himself

Wilma’s superpowers

audit

Pre-audits and contextualizes

data analysis results

chat

Easy and clear

chat interface

language

Understands SAP jargon

and business language

Vector (1)

On-prem, with your

approved LLM model

mcp

Compatible with your


MCP-environment

promts

Equipped with pre-developed

prompts

Built to respect
your security and compliance policies.

For accounting, compliance, and internal audit professionals, protecting sensitive financial data is non-negotiable. Our architecture ensures that Werner and Wilma act strictly within the bounds of your existing compliance policies.

On prem solution.

No external access 
to your data

Group 32 (1)
1

High security companies approved

Group 33
2

Data protection guarantee

Group 34
3

Both AI

and no-AI options

Group 35
4

Made in Germany

Group 36
5
1 (4) Frame-203

Real world use case:
Duplicate Payments

Finding duplicate payments across massive SAP data sounds
straightforward until you try it. Standard, rule-based analysis
produces thousands of hits, most of them false positives.

Werner changes this entirely. His AI-driven algorithms automatically
filter and prioritize the results for you, instantly surfacing the critical
insights that actually require human judgment.

Watch how Werner and Wilma solve this live

We recorded a 6-minute video showing the whole process live at our Discovery Room. Sign in and watch for free.

The agentic way

The manual way

Duplicate payment process

Focus on decisions. Automate manual work. Remove false positives automatically.

Manually check the results by going to SAP, opening the attachments and comparing them, with no way of sorting false positives out.

  1. Werner: Uses AI-algorithms to sort and prioritize results.

  2. Wilma: Compares the details and detects discrepancies, and contextualizes findings for your team.

  3. Your specialist: Works with Werner and Wilma and holds the final judgement on whether it’s a hit or a miss.

  1. Get the data, normalize and merge the data, run raw SQL queries.

  2. Use all of your experience to filter realistic samples.

  3. Open SAP manually twice for every single sample. Compare scanned attachments side-by-side to catch mostly false positives.

Status
Number of accounts payable (5 years) 2 400 000
Number of accounts payable (5 years) 2 400 000
Potential duplicates (candidates) 10 000
Potential duplicates (candidates) 300 000
Realistic duplicates (probability above 50%) 200
Realistic duplicates (based on experience) 10 000
Reviewed with Werner and Wilma 200
Reviewed manually 300–500
True duplicate payments confirmed 44
True duplicate payments confirmed 21
Zero manual hours
2–3 days
5–10 days
5–10 days

The agentic way

The manual way

Duplicate payment process

Focus on decisions. Automate manual work. Remove false positives automatically.

  1. Werner: Uses AI-algorithms to sort and prioritize results.

  2. Wilma: Compares the details and detects discrepancies, and contextualizes findings for your team.

  3. Your specialist: Works with Werner and Wilma and holds the final judgement on whether it’s a hit or a miss.

Manually check the results by going to SAP, opening the attachments and comparing them, with no way of sorting false positives out.

  1. Get the data, normalize and merge the data, run raw SQL queries.

  2. Use all of your experience to filter realistic samples.

  3. Open SAP manually twice for every single sample. Compare scanned attachments side-by-side to catch mostly false positives.

Status
Number of accounts payable (5 years) 2 400 000
Number of accounts payable (5 years) 2 400 000
Potential duplicates (candidates) 10 000
Potential duplicates (candidates) 300 000
Realistic duplicates (probability above 50%) 200
Zero manual hours
Realistic duplicates (based on experience) 10 000
5–10 days
Reviewed with Werner and Wilma 200
Reviewed manually 300–500
True duplicate payments confirmed 44
2–3 days
True duplicate payments confirmed 21
5–10 days