SFTP - Prodigy Integration and Automation

Common Integration Use Cases Between SFTP and Prodigy

1. Secure transfer of raw image and document datasets into Prodigy for labeling

Organizations often receive large volumes of sensitive source files through SFTP from manufacturers, retailers, field teams, or external partners. These files can be automatically moved into Prodigy as labeling tasks for computer vision or document classification projects. This supports use cases such as product image tagging, defect detection, invoice extraction, and document categorization while keeping the transfer encrypted and auditable.

Business value: Faster dataset onboarding, reduced manual file handling, and secure collaboration across internal teams and external data providers.

Data flow: SFTP to Prodigy

2. Controlled delivery of labeled training data back to enterprise systems

After annotation is completed in Prodigy, labeled datasets can be exported and delivered through SFTP to downstream systems such as data lakes, model training environments, or MLOps pipelines. This is useful when organizations require a secure, standardized handoff of approved training data to analytics teams or external model development partners.

Business value: Reliable dataset distribution, improved governance over training data, and easier handoff between data labeling and model training teams.

Data flow: Prodigy to SFTP

3. Secure annotation workflow for regulated content

Enterprises in regulated industries can use SFTP to move sensitive records such as customer documents, claims files, medical images, or financial forms into Prodigy for annotation. The labeled outputs can then be stored in controlled repositories for audit, compliance review, or model development. This approach helps maintain encryption and traceability throughout the labeling lifecycle.

Business value: Supports compliance requirements, reduces risk of data exposure, and enables AI initiatives in highly regulated environments.

Data flow: SFTP to Prodigy and Prodigy to SFTP

4. Active learning loop for continuously improving model quality

Prodigy can identify the most informative samples for labeling, and those selected files or records can be staged through SFTP from source systems or partner repositories. Once labeled, the updated dataset can be sent back via SFTP to retrain models on a scheduled basis. This creates a repeatable loop for improving computer vision or NLP models with minimal labeling effort.

Business value: Lower annotation costs, faster model improvement, and better use of subject matter expert time.

Data flow: Bi-directional

5. External partner labeling without exposing internal systems

When organizations work with third-party labeling vendors or offshore review teams, SFTP can be used to securely exchange source files and completed annotation packages with Prodigy as the central labeling workspace. This allows external contributors to work on approved datasets while the enterprise retains control over access, file movement, and transfer logs.

Business value: Safer outsourcing of labeling work, improved partner governance, and reduced operational overhead for secure file exchange.

Data flow: Bi-directional

6. Batch ingestion of archived content for retrospective labeling

Many enterprises have large archives of historical images, scanned documents, or text files stored in secure file repositories. These can be transferred via SFTP into Prodigy for retrospective labeling to build training sets for new AI initiatives such as search, classification, or quality inspection. This is especially valuable when organizations need to turn legacy content into structured training data quickly.

Business value: Unlocks value from archived content, accelerates AI project startup, and reduces the need for manual data preparation.

Data flow: SFTP to Prodigy

7. Secure export of reviewed labels for audit and model governance

In many enterprises, labeled datasets must be reviewed by compliance, quality, or model governance teams before they are used in production. Prodigy outputs can be exported and transferred through SFTP to audit archives or governance repositories, preserving a secure record of what was labeled, when it was labeled, and by whom.

Business value: Stronger auditability, better model governance, and easier support for internal controls and regulatory reviews.

Data flow: Prodigy to SFTP

8. Secure distribution of labeling tasks across distributed teams

Enterprises with multiple business units or global teams can use SFTP to distribute source datasets to Prodigy-based labeling workflows in different regions, then collect completed annotation files back into a central location. This supports standardized labeling operations across teams while maintaining secure transfer practices and consistent dataset handling.

Business value: Better cross-team coordination, consistent labeling standards, and efficient scaling of annotation operations across locations.

Data flow: Bi-directional