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.
Built for every enterprise persona
WaveflowDB is designed around the needs of every key enterprise stakeholder.
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.