01 / Multiple channels
Emails, PDFs, forms
Requests come from forms, emails, and PDFs. Eva brings them together into a structured format.
Digital employees Operations
AI process automation for back office and operations.
Eva shows what AI-supported process automation can look like in the back office: a digital operations employee reads requests from forms, PDFs, and emails, checks master data, and prepares CRM records.
The focus is the many small checks, handovers, and entries that tie up time in daily operations.
Process view Eva in the approval process Example view: Eva structures an exhibitor registration, marks open points and submits the case for human approval.
Screenshot of a Teams view in which Eva structures an exhibitor registration and prepares it for approval. 01 Why Operations
Back-office processes are rarely cleanly linear.
Automating workflows sounds easy until the work consists of forms, emails, PDFs, spreadsheets, and CRM fields. This is exactly where friction occurs.
02 / Check data
Master data and CRM
The same checks and handovers appear every day: comparing master data, finding existing customers, preparing CRM records.
03 / Clear boundaries
Exceptions instead of flying blind
AI process automation works better when exceptions remain visible: receipt, checking, preparation, handover.
02 Example flow
Eva prepares the case before it reaches the team.
A request comes via form, email, or PDF. Eva extracts the relevant information, compares it with the CRM, and prepares the record. This turns back office automation into a verifiable workflow.
- 01 · Input
An inquiry comes via form, email, or PDF.
Channel, format, and responsibility are defined in advance.
- 02 · Reading
Eva extracts the relevant information.
Company, contact person, desired service, period, location, internal references.
- 03 · Reconciliation
It checks whether the customer already exists.
Appropriate master data is linked and missing or contradictory fields are marked.
- 04 · Prepare
Eva prepares a CRM record or project request.
Structured, linked to sources, and ready for the team.
- 05 · Handover
Unclear cases go to people with context.
With a note as to why the case does not continue automatically.
Eva does not take over the whole process. It handles the part that recurs, can be described, and can be clearly defined.
03 Inputs & Outputs
Which back office workflows can be automated.
The specific connection depends on the system. Eva can also work in environments where the data is on-premise. What matters is which access is truly necessary for the task and where a person has to approve it.
- In 01
Contact forms
- Out 01
Structured request
- In 02
PDF applications
- Out 02
Recognized existing customer
- In 03
Emails with requests or attachments
- Out 03
Prepared CRM process
- In 04
CRM and master data
- Out 04
Marked data gap
- In 05
Internal databases
- Out 05
Summary for the team
- In 06
Document filing
- Out 06
Handover to a responsible person
Eva should not make open decisions without a framework. Good operations automation works with limitations.
04 Control & Security
Eva does not work freely across the company. It gets a clear assignment.
With AI process automation, architecture matters more than a polished chatbot surface. A digital employee needs control; otherwise it will not be viable in daily operations.
01
Clear mission
One task, one channel, one target system, not everything at once.
02
Roles and rights
Per system, per data type and task, limited to what is necessary.
03
Audit logs
Every relevant step remains traceable and verifiable.
04
Human-in-the-Loop
Unclear cases, edge cases, and decisions remain with people.
05
No provider training
Customer data is not used to train third-party LLMs.
06
Hosting & data flows
Planned for the specific setup. On-premise contexts can also be checked.
05 How thirdmind builds Eva
Process automation with AI starts with a specific case.
We check together: which workflow takes time, where the work comes in, what data Eva needs, which systems it may read or prepare, when it may pass on a case, and when a human has to decide.
After that, a limited MVP is created. Not as a demo, but as the first system for AI-supported process automation with task, channel, access, logs, and clear responsibility.
When this MVP works in daily operations, it expands: more request types, more data sources, more preparation steps. Step by step.
06 Frequently asked questions
Frequently asked questions.
What does AI process automation mean in the back office?
AI process automation in the back office means preparing recurring work with documents, emails, forms, master data, and CRM records. A digital operations employee reads information, checks data, prepares work steps, and passes exceptions to humans.
Can Eva work directly in the CRM?
This depends on the CRM, the rights, and the desired process. A sensible start is often to prepare CRM records and make them visible before a human approves them.
Which back office workflows can be automated?
Frequent workflows with clear inputs and verifiable intermediate steps are suitable: structuring inquiries, reading PDFs, comparing master data, recognizing existing customers, preparing CRM records, and marking open points.
Which documents can Eva read?
Typical inputs are forms, PDFs, and emails. The project uses real examples to check which fields can be read reliably.
Does this also work with on-premise databases?
Yes, such setups are possible. The specific architecture depends on how the data can be accessed and what security requirements apply.
Does Eva replace an operations team?
No. Eva takes on recurring preparatory work. Decisions, edge cases, and responsibility remain with people.
How does a first project start?
It starts with a clear process: an input channel, a data type, a target system, and a task that occurs frequently enough. This creates a limited MVP.
Start a conversation
Which back office workflows should you no longer run manually?
Talk about process automationYuno asks four quick questions about input channels, data sources, target system, and approval. After that, it is clearer whether an AI process automation pilot makes sense.