Glossary · Clio

What is an extraction confidence score?

What is an extraction confidence score? A clear explanation for Azerbaijani business — and how Clio applies it.

Understanding Extraction Confidence Scores

An extraction confidence score is a precise numerical value assigned by an AI model to indicate the level of certainty regarding the accuracy of specific data extracted from a document. In high-stakes automated processing, these scores serve as the primary mechanism for distinguishing between high-certainty data and fields that require human intervention. By quantifying uncertainty, businesses can maintain strict data integrity and ensure that only the most reliable information moves forward in the pipeline. Within the Allmaz ecosystem, these scores are paired with source-page provenance, allowing users to see exactly where a piece of data originated. This transparency transforms the AI from a 'black box' into a verifiable tool, ensuring that invoices and contracts are converted into validated CRM records with total visibility. By leveraging these scores alongside deterministic validation, the system eliminates the risk of silent failures and ensures that data quality remains consistent across all processed documents.

Capabilities

The Business Value of Confidence Scores

Eliminates manual data entry errors by automating the extraction of invoices and contracts

Ensures extreme precision in CRM record creation through a combination of AI and human oversight

Provides full auditability via source-page provenance for every extracted field

Prevents corrupted or incorrect data from entering core systems via hard validation gates

Accelerates the processing of multilingual documents in Azerbaijani, Russian, and English

Blocks duplicate documents automatically to maintain a clean and efficient database

Advanced Data Extraction Capabilities

Multilingual Support

The system processes documents in Azerbaijani, Russian, and English, including specialized identifiers like the VÖEN tax ID.

Vision LLM Integration

A vision-based large language model extracts every field while providing a confidence score and source-page provenance.

Deterministic Validation

Strict validation rules act as a hard gate, ensuring that bad data can never auto-clear the system.

Schema-Based Flexibility

New document types are integrated via a schema rather than requiring new code, allowing for rapid scaling.

The Extraction and Validation Process

1The vision LLM scans the document and extracts required fields.
2The system assigns a confidence score and maps the data to its source page.
3Deterministic validation checks the data against hard rules to filter out errors.
4Duplicate documents are identified and blocked from processing.
5A human reviewer approves the extraction before any data is written to the CRM.

Frequently Asked Questions

Can data be written to the CRM automatically?

No, every CRM write requires human approval to ensure absolute accuracy and prevent incorrect entries.

What happens if the AI is unsure about a field?

The confidence score reflects this uncertainty, and the deterministic validation gate prevents any low-confidence or incorrect data from auto-clearing.

What is the target precision for field extraction?

The system targets straight-through processing at 99.5% field precision with a strict mandate of zero incorrect auto-writes.

How are new document types added to the system?

New document types are added as a schema rather than through custom code, allowing for fast and flexible integration.

Which languages and identifiers are supported?

The system fully supports Azerbaijani, Russian, and English, and is specifically capable of reading the VÖEN tax ID.

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