Document classification
Enterprises rely on document classification to enforce security policies, automate routing, support compliance, and apply business logic at scale. However, real-world documents vary widely in structure, intent, and clarity. Edge cases, ambiguous content, and new or unmodeled document types routinely break static classifiers.
Traditional classification models also struggle to keep up with policy changes or expanding namespace schemas. Teams often respond by creating new models for each category set, business unit, or jurisdiction. The result is model sprawl, slow update cycles, and inconsistent outcomes.
Orca replaces this with a single, adaptive classification architecture.
The problem with conventional document classification
Poor handling of ambiguity and new document types
Static deep learning classifiers degrade when they encounter unclear or never-before-seen patterns. Updating them requires retraining and redeployment
Policy and taxonomy complexity
Business and security policies evolve. New categories, subcategories, and compliance rules expand the namespace. This increases maintenance cost and slows iteration
Model sprawl
Different departments or regions often require slightly different classification rules. Teams end up maintaining many similar models, each expensive to train, validate, and supervise
Slow cycle times
Retraining pipelines introduce significant delays. This is especially painful when classification accuracy impacts compliance, routing, or automated actions
Orca's solution:
One classification model for all policies and namespaces
1. A single model that adapts instantly
Orca uses a memory controlled classifier that incorporates updated examples or policy changes in real time. You update the memoryset and the model behavior changes immediately. No retraining required.
2. Per document customization
Different document types or business contexts can load different memorysets at inference. This allows one model to handle multiple taxonomies, compliance rules, and security classifications without proliferation.
3. High accuracy under drift and edge conditions
Retrieval augmented classification maintains accuracy even when new or ambiguous document types appear. Orca has demonstrated strong performance under data drift in both text and image classification.
4. Explainable classifications
Each inference shows the specific memories that influenced the decision. This allows auditors and engineers to understand why a document was classified into a particular category and to correct errors precisely.
5. Lower cost and faster cycle times
Eliminating retraining reduces compute cost and drastically reduces iteration time. Model maintenance becomes memory management instead of model engineering.
Supported enterprise use cases
- Security classification and data loss prevention
- Compliance labeling and policy enforcement
- Workflow routing and queue assignment
- Document type and sub-type identification for downstream automation
- Multi-tenant or multi-region classification with different taxonomies
- Rapid adoption of new document categories or policy updates
Example workflow
1. A new policy introduces a new document class or a new sensitivity level
2. The classification team adds or modifies a few labeled examples in the relevant memoryset
3. Orca immediately incorporates the change with no training cycle
4. Downstream routing and policy enforcement update in real time
5. If a misclassification occurs, the inspector shows why and how to fix it
Where this is a fit
- Enterprises with large and evolving document taxonomies
- Teams that need predictable and auditable classification
- Organizations that manage many near-duplicate models
- Environments where accuracy directly impacts compliance or security
- Workflows that cannot wait for retraining cycles
Talk to Orca
Speak to our engineering team to learn how we can help you unlock high performance agentic AI / LLM evaluation, real-time adaptive ML, and accelerated AI operations.