Breaking: DocScan Cloud Launches Batch AI Processing and On-Prem Connector

Quick take: DocScan Cloud today rolled out two features many enterprise customers have been requesting: a high-throughput batch AI processing engine and an on-prem connector for hybrid deployments. These releases target regulated industries and organizations with large archival digitization projects.

Batch AI processing

The new batch engine allows customers to submit multi-million-document jobs with orchestration controls, progress reporting, and granular retry semantics. Key capabilities include:

• Job chunking to control parallelism and resource consumption.
• Progressive validation options that let teams prioritize high-value documents first.
• Cost-estimation tools and per-job dry-run to predict processing time and costs before execution.

DocScan Cloud claims the batch engine achieves up to 40% better throughput for archival projects through optimizations like shared model loading, intelligent caching, and batch pre-processing.

On-prem connector

The on-prem connector provides a lightweight agent that sits within a customer's VPC or datacenter and performs sensitive inference locally while still integrating with the cloud control plane for management, updates, and monitoring. Notable features:

• Configurable sync policies to control what metadata or anonymized results are uploaded to the cloud.
• Support for customer-managed keys (BYOK) for encryption across agent and cloud communications.
• Automatic failover to cloud processing if authorized and network connectivity is available, enabling a hybrid operating mode.

Customer impact

These features expand DocScan Cloud's addressable market and make it easier for large enterprises and government agencies to adopt the platform without sacrificing residency or compliance requirements. Early adopters in finance and health systems reported shorter project timelines and a clear path to move legacy archives into searchable digital repositories.

Analyst perspective

From a market standpoint, these upgrades indicate a maturing product that recognizes the hybrid reality of enterprise workloads. As vendors converge on similar features, the differentiators will be execution quality: how well the connector handles patching, monitoring, and consistent model performance across edge and cloud.

"Supporting both on-prem inference and high-throughput batch jobs addresses two of the biggest adoption blockers we've seen in the field."

Availability & pricing

Both features are rolling out in Q1 2026. The on-prem connector will be part of an enterprise plan and require a one-time setup fee plus subscription for management. Batch processing is billed by processed pages with tiered discounts for committed volumes. DocScan Cloud promises migration assistance for customers moving archival content into the batch pipeline.

What to ask before you adopt

• How does the connector handle security patching and upgrades?
• What are the data transfer audit logs between the on-prem agent and the cloud?
• Is there SLA parity between cloud and on-prem processing?
• Can you run a cost simulation for your archive volume before locking in a commitment?

Conclusion

DocScan Cloud's new features lower the barriers for regulated and archive-heavy organizations to operationalize document intelligence. While exact performance and pricing will decide enterprise uptake, the product direction aligns with the hybrid, compliance-sensitive reality of large-scale digitization programs.