Are you still manually checking various documents for logical consistency and accurate information transfer?

Do the quantities in a plan match the order? Has a change also been taken into account in subsequent documents? Does the number of accessories match the intended main components? Do the details in contracts, expert reports, or minutes correspond to what is documented in drawings, tables, or photos?

Such checks are often time-consuming, especially when relevant information is spread across many different files and document versions.

Vimmera AI Agents handle exactly this task.

They understand text, files, presentations, contracts, expert reports, tables, photos, and drawings. Handwritten notes, additions, and annotations within documents can also be included in the review.

Information is not merely extracted. The agent relates it to other information, compares numbers and quantities, tracks changes across different documents, and identifies conspicuous or implausible connections.

This turns the manual search through many individual documents into a systematic, intelligent review of your entire information base.

When information needs to be not only read but also cross-checked

Contracts, invoices, orders, presentations, drawings, photos, expert reports, plans, minutes, and tables often contain interdependent information.

A quantity from a plan later appears in an order. A change affects further items. A handwritten note supplements a drawing. A statement from an expert report must match what is shown in a photo. Accessories must match a main product. Prices must align with quantities and credits.

This is precisely where specialized Vimmera AI Agents can be used for verification and control tasks.

They do not merely read individual documents. They understand different information sources, bring their contents together, identify connections, and specifically point out anomalies.

Understanding texts, files, images, and drawings together

Vimmera AI Agents can capture and compare information from a wide variety of sources.

These include, for example:

• Texts and documents
• PDF files
• Tables
• Presentations
• Contracts
• Expert reports
• Minutes
• Invoices
• Technical documentation
• Photos
• Plans and drawings
• Handwritten notes
• Annotations and notes within documents
• Scanned documents

The different types of information are not considered in isolation.

For example, an agent can compare a detail from a contract with a drawing, take a handwritten addition in a document into account, or relate information from an expert report to a photo and other project documents.

This also allows information that is not cleanly stored in a database or table to become part of a structured verification process.

Understanding information where it actually originates

In many companies, relevant knowledge is not contained in a single structured data source.

It is distributed across files, emails, tables, contracts, presentations, drawings, photos, handwritten additions, and meeting minutes.

For a complete review, it is therefore often not enough to extract text from documents alone.

Vimmera AI Agents can consider different content together and build a coherent understanding of the process from it.

For example, the following can be checked:

Contract ↔ Drawing

Expert report ↔ Photo

Plan ↔ Handwritten addition

Presentation ↔ Calculation

Minutes ↔ Subsequent document version

Order ↔ Invoice

Main product ↔ Accessories

Quantity ↔ Price

Change ↔ Follow-on items

This makes it possible to identify connections that are easily overlooked when individual files are considered separately.

Understand documents. Compare figures. Check connections.

A Vimmera AI Agent developed accordingly can first structure information and then compare it with other information.

For example:

• Quantities and unit counts
• Products and accessories
• Orders and invoices
• Plans and service items
• Changes and their follow-on items
• Prices and credits
• Main components and dependent parts
• Different document and plan versions
• Details in texts and their representation in drawings
• Documented conditions and visible information in photos

This is not only about identical values.

The agent can also take into account which information can be compared at all and how it relates to other information.

Intelligent quantity verification instead of simple number matching

Suppose a plan contains 24 components.

Of these, 18 require certain additional equipment.

A simple system could compare the total quantity of 24 with the number of accessory items and would already identify a discrepancy.

An intelligent verification agent, on the other hand, recognizes:

24 components in total

18 of them with additional equipment

18 accessory items

It therefore uses the actually relevant reference quantity.

Even more complex relationships can be taken into account.

For example, if there are 18 electrical components but only 6 control modules documented, this is not automatically an error.

Perhaps each control module can operate multiple components.

The agent therefore does not only check:

Is the number the same?

But rather:

Does the quantity fit the function and the documented relationship?

Automatically tracking changes

Changes are a frequent cause of inconsistencies.

For example, if an item is reduced from 12 to 10 units, numerous other details may be affected.

A Vimmera AI Agent can then specifically check:

• Has the accessory quantity also been adjusted?
• Do the connections still match?
• Have the control components been corrected?
• Does the order match the new version?
• Has the change been incorporated into subsequent documents?
• Do quantities and prices still match?
• Has the change also been taken into account in drawings and plans?
• Are there still notes or annotations referring to the previous version?

This does not only identify the original change.

Its effects on connected information and documents are also checked.

Handwritten additions can also be relevant

Not every important piece of information is found in neatly completed fields.

A handwritten number on a drawing, a mark in a document, or a brief addition in the margin can be crucial for a process.

Vimmera AI Agents can also include such information in the review.

This makes it possible, for example, to compare machine-generated details with handwritten additions or identify changes recorded only as a note in a document.

The file format is not what matters.

What matters is the content and its relevance to the respective verification process.

Photos as an additional source of information

Photos can also be part of an intelligent review.

Depending on the use case, Vimmera AI Agents can capture visible information in images and relate it to other documents.

For example, documented properties, labels, designs, components, or recognizable conditions can be compared with details from contracts, expert reports, drawings, or minutes.

This turns a purely document-based review into a more comprehensive consideration of different information sources.

Identifying conspicuous numbers and orders of magnitude

Not every error is a direct contradiction.

Sometimes a detail stands out mainly because it appears unusual in the specific context.

For example:

8 becomes 88

12.5 becomes 125

an old unit count remains after a change

a credit is carried over with the wrong sign

meters and square meters are confused

a quantity appears unusually high in relation to the overall process

An AI agent can identify such patterns and issue them as a targeted verification prompt.

It does not have to claim that it already knows the correct value.

It can transparently explain why a detail is conspicuous and what should be checked.

Fewer false alarms through context

A good automated review should not generate as many warnings as possible.

It should identify the relevant anomalies.

That is why possible explanations can also be taken into account during the review.

For example:

• Is the item part of a set?
• Does the quantity refer to only part of the total quantity?
• Can one component operate several other components?
• Is there a shared control system?
• Has a standard item been credited and replaced?
• Does the detail come from an older version?
• Has a service intentionally been awarded elsewhere?
• Has a change already been taken into account elsewhere?
• Is there a handwritten addition that explains the discrepancy?
• Does a drawing show a newer version than another document?

Only if a relevant discrepancy remains afterward does a specific verification prompt arise.

A discrepancy becomes a specific verification question

A verification agent should not simply report:

“The numbers do not match.”

It can work much more precisely:

Identified:
According to the plan, 18 components require certain equipment. The current overview contains 20 corresponding accessory items.

Why conspicuous:
The documented accessory quantity is two units higher than the identifiable reference quantity.

Possible explanation:
Older plan version, additional reserve items, or a quantity change that was not updated.

To check:
Are the two additional items intended, or was the quantity not adjusted after a change?

This turns an automated check into an actionable basis for further work.

Applicable across numerous business areas

Intelligent verification agents can be used wherever information from different sources is interconnected.

For example, in:

• Contract review
• Invoice review
• Procurement
• Order review
• Cost estimation
• Quality management
• Project management
• Change management
• Technical documentation
• Bills of materials
• Product configurations
• Tenders
• Service descriptions
• Production documents
• Supplier documents
• Expert report review
• Plan review
• Documentation control

The specific verification logic is adapted to the respective process.

Company knowledge becomes part of the review

Vimmera AI Agents become especially powerful when they not only understand documents and files, but also take a company's individual rules into account.

This is because many important verification rules emerge from experience.

For example, employees know:

• which items belong together
• which quantities depend on each other
• which combinations are normal
• which exceptions occur regularly
• which changes trigger further changes
• which documents need to be compared with each other
• which discrepancies are actually relevant

This knowledge can be systematically integrated into how an AI agent works.

This creates not a general chatbot, but a specialized Vimmera AI Agent for a specific verification process.

AI Agents that take a closer look

Finding information is only the first step.

The greater benefit comes when AI understands information, connects it, compares figures, considers drawings and images, identifies dependencies, and explains anomalies in a comprehensible way.

Vimmera develops AI Agents that process texts, files, presentations, contracts, expert reports, photos, drawings, and handwritten information together and can be tailored to individual processes and verification rules.

This makes it possible to structure extensive verification tasks, automate recurring checks, and specifically make relevant anomalies visible to employees.

Which verification processes can be automated in your company?

Many checks today rely on experience, manual comparisons, and the knowledge of individual employees.

Vimmera AI Agents can connect this knowledge with existing documents, files, and information to develop specialized verification processes.

Talk to us about your use case.