Timia

Arkaive

ARKAIVE

Intelligent Unstructured Data Management at Scale

With Arkaive, TIMIA the processing and organization of information in projects with high volumes of data .

TIMIA analysis dashboard with document statistics and a storage panel showing distribution by status.

Advanced control over large document volumes

Arkaive is an enterprise platform for automated unstructured data processing, designed to manage complex document collections efficiently, scalably, and cost-effectively.

Large-scale automated classification

Processes thousands of documents in parallel, identifying their content and intelligently organizing them without manual intervention.

Proven cost optimization

Reduces AI processing costs by 85–90% through caching and similarity techniques that detect and eliminate duplicate documents before reprocessing them, avoiding redundant work and unnecessary analysis.

Integration with enterprise repositories

Connects multiple document sources and unifies them into a coherent and traceable processing flow.

Designed to solve document complexity

Arkaive implements parallel processing, intelligent workflows, and multimodal AI to manage large-scale document flows, prioritizing cloud resource optimization.

Ingests documents from multiple sources

Analyzes and classifies content automatically

Detects and eliminates duplicates with high precision

Orchestrates multi-stage flows efficiently

Scales dynamically based on volume and load

Maintains traceability and operational control

Connection with multiple Data Sources

Google Drive
Google Drive
Azure Blob Storage
Azure Blob Storage
AWS S3 Storage
AWS S3 Storage
Microsoft SharePoint
Microsoft SharePoint
Local Server Icon
Local Server
Architecture diagram: unstructured data is ingested into Azure cloud-native and processed with automated workflows for classification, storage, and sending to vector databases.

Designed for real efficiency and scalability

Arkaive enables organizations to process more information, in less time and at a lower cost, transforming unstructured data into a solid foundation for decisions and automation.

Ideal when processing documents with AI is slow, costly, or difficult to scale.