Automating manual data transfer between systems

Consider a typical working situation. A company receives customer requests by email. Some information is in the email body, while the rest is in attached Excel, Word, or PDF files. A manager has to open the email, download the files, and find the required details: company name, contact person, phone number, products or services, quantity, price, delivery address, and other parameters.

The employee then manually enters this information into the CRM or ERP. If document layouts vary, they have to work out where each field is located every time. Names may be written differently, some details may be missing, and the same customer may already exist in the system under a slightly different name.

Next, the employee checks the entries, corrects phone number and address formats, matches items against reference data, creates a customer or deal record, and attaches the source documents. If information is missing, they return to the email or contact the sender.

Manually transferring data between systems takes time and scales poorly. As the number of emails, Excel files, PDF documents, and requests grows, more employees are needed to carry out the same repetitive operations. At the same time, the likelihood of errors increases: missing fields, duplicates, incorrect copying, or selecting the wrong customer or product.

In effect, expensive staff time is spent on a repetitive operation rather than decision-making:

Once the process is automated, employees no longer need to open every email and move information between applications manually. The system receives incoming data from email, an API, Excel, Word, PDF, or another source and passes it to the next processing stage.

Python-script handles technical preparation: retrieving attachments, identifying file formats, extracting text, and splitting documents into the required parts. Then text-LLM-model analyzes the content and converts unstructured information into a predefined structure: customer, company details, contact information, products, amounts, dates, comments, and other fields.

After extraction, the data is normalized. Phone numbers, dates, organization names, and other values are brought into a consistent format. Through PostgreSQL, the system can check existing records, reference data, and previously processed information. If the same customer is written as “Alpha LLC”, “LLC Alpha”, or Alfa, the pipeline determines whether the information refers to the same entity and uses semantic matching when needed for a more complex name.

The next python-script applies business rules: checking required fields, permitted values, duplicates, and consistency with reference data. Once validation succeeds, the information is automatically sent through an API to the CRM, ERP, or another system.

When the data is unambiguous, no human involvement is needed. If an exception occurs, such as being unable to identify the customer reliably or select the right reference item, an employee receives the task only to make the decision.

Automating the transfer of data from email, Excel, Word, and PDF to a CRM or ERP therefore replaces a sequence of manual operations with a single controlled pipeline. Employees stop mechanically copying information and participate only where human judgment is needed.

The result is fewer manual operations, shorter processing time per request, a lower likelihood of errors, and the ability to handle a much larger volume of data without a proportional increase in staff.