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Getting Started

Welcome to the DocServant Platform User Guide. DocServant uses state-of-the-art artificial intelligence to convert unstructured documents (such as invoices, bank statements, contracts, receipts, and reports) into structured, clean tabular data.

How it Works

The typical extraction lifecycle on the DocServant platform consists of three main steps:

1. Create a Template: Define the blueprint or schema of the output data you wish to extract. You can configure individual objects (like sheets in an Excel document) and fields (with instructions, types, and descriptions).
2. Run an Extraction: Upload your document files (PDFs, Word documents, or images) and select the corresponding template. DocServant processes the files asynchronously to extract information exactly matching your schema.
3. Export Data: Review the extracted results in the dashboard. Check for accuracy and export the data directly as an Excel workbook (`.xlsx`) or a CSV file.

Templates

A Template is a user-defined blueprint that specifies the structure of the data you want DocServant to extract. By defining objects and fields, you instruct the AI on exactly what information to find and how to format it.

Core Template Configuration

When configuring a template, you can customize how extractions are handled:

• Output Mode: Choose between Combined (default) or Single. Combined merges all document outputs into a single consolidated sheet. Single generates separate outputs for each document.
• Store Data: Toggle this on to retain original source files for document previews. Turn it off to automatically purge files from the server after extraction completes (ideal for strict data privacy).
• Global Instructions: Provide overall context to the AI model, such as the document language, formatting preferences, or specific terminology rules.

Template Creation Process In-Depth

Manual Template Setup

Creating a template manually gives you maximum control. First, add an Object (e.g., `Invoices` or `Line Items`). Within each object, add Fields:

• Field Name: A short descriptor (e.g. `Invoice Number`, `Total Amount`).
• Field Type: Let the AI know the data type. E.g. a date type will enforce ISO-8601 formatting, and a number type will strip currency symbols.
• Field Description / Instruction: Direct prompt telling the AI where to find the data or how to interpret it. For example: "Extract the invoice total before taxes, usually labeled as Subtotal."

XLS Template Upload Setup

If you already have a reporting template in Excel, uploading it is the fastest way to get started. DocServant reads the workbook structure:

Once uploaded, you can refine the generated prompt instructions and adjust data types to achieve optimal extraction accuracy.

Run Extraction

Once you have created a template, you are ready to run extractions. DocServant processes your files asynchronously and formats the output data according to your template schema.

Submitting an Extraction Job

To submit a document for extraction using the web interface:

1. Navigate to the Runs or Extraction section in the dashboard sidebar.
2. Choose the template you want to use from the dropdown selection list.
3. Click the upload zone or drag and drop your document files. You can upload multiple files to process them in parallel.
4. Configure any overrides (like forcing a specific output_mode or disabling data storage).
5. Click Run Extraction to submit the job.

Supported File Formats & Guidelines

DocServant supports a wide variety of formats for extraction. For best results, follow these quality guidelines:

Monitoring and Exporting Results

After submission, your extraction job appears in the Run History table. You can watch progress in real time as the status shifts from pending or processing to completed or failed.

Once a run is complete:

• Clicking the run details displays an interactive table and spreadsheet side-by-side with your document. You can hover over cell values to see the extracted results aligned with the visual preview.
• Use the Export button at the top-right of the run page to download results.

Reference Data

Reference Data allows you to upload custom datasets (such as product catalogs, customer rosters, vendor lists, or internal code tables) and use them to validate, match, and enrich your AI extraction results.

What is Reference Data?

In many workflows, extracting text directly isn't enough. You may need to verify that an extracted vendor matches a database record, or map an informal product name on a receipt to an official SKU. Reference Data bridges the gap by letting you query your uploaded records directly within your extraction prompts.

Creating and Managing Datasets

To upload your lookup data:

1. Navigate to Reference Data in the dashboard sidebar.
2. Click New Dataset to open the creation wizard.
3. Define the basic schema fields (e.g. `SKU`, `Product Name`, `Unit Price`).
4. Upload your records via a CSV import, or manually add individual rows directly within the interface.

Querying Datasets with the Query Builder

Once a dataset is active, you can bind it to a field in a template. This allows you to write lookup queries using the Query Builder:

Lookup Resolution & Return Field

When a query matches one or more records in your dataset, you specify a Return Field (e.g., `SKU` or `Internal Code`). DocServant will replace the raw extracted value in the final spreadsheet or JSON output with the matched record's return field. This eliminates manual catalog lookup and post-processing steps.

Best Practices

Optimize your extraction success, increase accuracy, and manage workspace security by applying these guidelines when designing templates and running jobs.

1. Field Instruction Formatting (Prompt Engineering)

The AI reads your field instructions as direct orders. Clear, explicit prompts lead to significantly higher accuracy:

2. Handling Multi-Page & Multi-Sheet Templates

When dealing with complex documents containing nested lists or repeated tables:

• Separate Objects: Define a distinct template object for each unique collection. For instance, put general invoice metadata (invoice number, date) in one object, and the list of item rows in another.
• Consistent Row Definitions: For line-item tables, keep fields simple (e.g. `Description`, `Quantity`, `Price`). Let the AI do the heavy lifting of mapping row by row.

3. Data Privacy and Security

If your organization handles sensitive information (e.g., patient records, personally identifiable information (PII), or confidential financials):

4. Common Troubleshooting Tips

Contact / Support

If you couldn't find the answer you were looking for, or if you encounter any platform issues or billing discrepancies, please reach out to our dedicated support channels.