Why Traditional Databases Hallucinate?
Traditional Vector Databases Hallucinate because they optimise for mathematical similarity, not reliable business context
High Cost of AI Agents
Up to 60% of AI agent costs stem from vector DB infra, making deployments inefficient and expensive.
Low Quality Retrieval
Current vector DBs return low-context chunks, causing poor precision, hallucinations, and unreliable AI-generated outputs.
Poor Recall
ANN methods frequently miss relevant results, with recall degrading rapidly as datasets scale beyond top-k thresholds.
Rigid Schema & Indexing Challenges
Index-first rigid architectures create schema inflexibility, adaptation challenges, and significant operational overhead.
Weak Security & Compliance
Poor RBAC, weak tenant isolation, and compliance gaps prevent enterprises from trusting vector DBs for critical workloads.
Scalability & Performance Issues
Single-index architectures create bottlenecks, scaling challenges, and performance degradation under complex queries and large datasets.
Why WaveflowDB Stands Out
See how we compare against others in performance, growth
WaveflowDB- AI Assistant
- Auto Chunking
- Full Corpus
- RBAC
- Hybrid Search
- 40% Better Precision
- 30% F1 Score
- No Read & Write Costs
- Auto-Indexing, Auto-reranking
- No AI Assistant Support
- Manual Effort
- Limited Context
- Basic Authentication
- Traditional Search
- Standard Accuracy
- Lower Performance
- Usage-based Pricing
- Manual Configuration
Frequently Asked Questions
Answers to common questions about our products, processes, and what sets us apart.