Document Versioning
Document versioning provides comprehensive version control for your AI knowledge base, ensuring production stability while enabling continuous improvement. Teams can now iterate on content safely, with clear workflows that separate work-in-progress from production-ready documentation.Key Capabilities
- Four-State Version Lifecycle: Documents support Approved (production), Draft (work-in-progress), Review (pending approval), and Archived (historical) states
- Production Stability by Default: APIs and AI responses use only approved versions unless explicitly requested otherwise
- Collaborative Workflows: Multiple team members can work on drafts simultaneously with version notes and clear approval processes
- Complete Audit Trail: Track who made changes, when, and whycritical for compliance requirements
Why We Built This
Document Versioning and Knowledge Base Testing work together to create a robust content management system that balances stability with continuous improvement. Teams in regulated industries now have the tools to maintain high-quality, evolving documentation that powers AI systemswith the confidence that changes won’t compromise production stability or compliance requirements.For Content Teams: Safely iterate on documentation without affecting production systems. Create drafts from any document version, collaborate with teammates, and deploy updates only when ready. For Engineering Teams: Maintain API stability while content evolves. Production systems automatically use only approved content, with optional access to draft versions for testing. For Compliance Teams: Full version history with user attribution meets regulatory requirements. Track every change with clear audit trails and approval workflows.Knowledge Base Testing
Systematic testing ensures your knowledge base remains comprehensive and consistent. Two new test types help identify gaps and conflicts before they impact your AI applications.Document Coverage Test
Automatically identifies gaps in your knowledge base by testing how well your documents cover defined tasks. This ensures AI agents have the information needed to handle all scenarios effectively.Overlap and Contradictions Analysis
Tests your knowledge base using the Overlap and Contradictions test to identify:- Overlapping Information: Find redundant content across documents
- Contradictory Instructions: Detect conflicting guidance that could confuse AI agents
Key Features
- Automatic Change Detection: Identifies when model parameters (temperature, max tokens, top-k, etc.) change between deployments
- Missing Parameter Alerts: Flags when critical parameters required for evaluation or compliance are absent
- Intelligent Journal Entries: Generates human-readable descriptions of what changed, when, and by how much
- Complete Audit Trail: Maintains a tamper-proof history for regulatory compliance
- Zero Manual Overhead: Operates completely automatically in the background
Why We Built This
For Engineering Teams: Prevent configuration drift, accelerate incident response, and maintain evaluation integrity. Know immediately when model parameters change unexpectedly.For Compliance Teams: Achieve regulatory readiness with comprehensive audit trails, track risk-relevant parameter changes, and reduce compliance reporting time from weeks to minutes.
Improvements and Fixes
- Manage multiple evaluations per task simultaneously
- Schedule evaluations with custom criticality levels and intervals
- System journal entries are now visible in dashboard graphs
- Performance optimizations for faster response times
- Enhanced security for production deployments
📖 Recall (RAG Evaluation)
The Recall feature in Avido provides a comprehensive way to assess how well your AI application’s Retrieval-Augmented Generation (RAG) system is performing.- Measure key aspects of quality, correctness, and relevancy within your RAG workflow.
- No-code interface empowers both technical and non-technical stakeholders to interpret metrics.
- Ensure systems meet required quality standards before production.
🛠️ SDK & Trace Improvements
- Micro-second precision when ingesting data.
- Group traces to visualise workflow structure at a glance.
☁️ OpenAI on Azure Support
- EU customers can now run all inference on models hosted in Europe.
- Regardless of geography, we, or any of our providers, never train on any data.
🐞 Bug Fixes & Polishing
Lots of improvements and paper cuts to make your experience with Avido even smoother, faster, and enjoyable.
🔍 Enhanced Test View
- Easily dive into each evaluation to pinpoint exactly what’s working—and what needs improvement.
- Clearly understand AI performance to rapidly iterate and optimize.
📌 System Journal
- Track application changes seamlessly and visualize how these updates impact individual eval performance.
- Stay informed and make confident deployment decisions with clear version tracking.
🔐 Single Sign-On (SSO)
- Support for all major identity providers, making it even easier to roll out Avido in enterprises.
⚙️ Custom Evaluations
- Create custom evals directly from our UI or via API.
- Test specific business logic, compliance requirements, brand-specific wording, and other critical aspects of your application, ensuring unmatched accuracy and reliability.
🚀 Quickstart Workflow
- Upload existing outputs via CSV to automatically generate evaluation cases
- Smart AI-powered categorization of topics and tasks
- Interactive review interface for selecting benchmark outputs
- Automated evaluation criteria generation based on selected examples
📊 Improved Scoring System
- Simplified scoring scale (1-5) for more intuitive evaluation
- Updated benchmarking system for better quality assessment
- Refined evaluation criteria for clearer quality metrics
🤖 Smart Analysis
- Automatic topic detection from output patterns
- Task identification based on user intentions
- Intelligent grouping of similar outputs
- Automated quality scoring of historical outputs
💡 Enhanced Review Experience
- Visual topic distribution analysis
- Side-by-side conversation comparison
- Guided selection of benchmark outputs
- Contextual feedback collection for evaluation criteria