Database Agents: Executive Guide to Self-Healing in 2025
By IntelliDB Enterprise In fact, enterprises nowadays function in unbelievably low millisecond customer experiences, while downtime readily translates to actual financial loss. Well, while traditional and powerful, PostgreSQL still relies heavily on manual tuning, on reactive monitoring, and constant human oversight. But in 2025, this cannot continue. With the amount of AI workloads and exploding […]
SLA-Grade High Availability for AI Applications: Replication, Failover, and Uptime Engineering
AI cannot be programmed to work “within acceptable downtime windows” since it is, in fact, working 24/7 online. The user experience, quality of prediction, sustained automation, and revenue-generating viability demand the most critical of milliseconds. High availability (HA) is no longer regarded as a luxury in these situations; it has become a predetermined SLA requirement. […]
AI secured by design: Encryption, Redaction & Audit controls for vector databases
Vector databases have become fundamental backbones for RAG systems, semantic search, and AI agents. They store: embeddings, reasoning traces, context windows, and real-time memories—data much more sensitive than most organizations understand and realize. However, compared to OLTP and other analytical systems, such vector stores frequently comprise the least governed, least encrypted, and least trustworthy part […]
Postgres AI Observability: The Automatic Transformation of Logs into Insights, and Insights into Action
PostgreSQL can be described as the workhorse of all transactional systems, analytics, and mission-critical enterprise workloads. But with the rise of AI-driven applications, agent workflows, and vector-accelerated pipelines, the operational implications have risen dramatically. Now, systems are generating ever-increasing amounts of logs, presenting diversity in the nature of queries never seen before, and unpredictable workloads. […]
Production pgvector at Scale: ANN Decisions, Filtering, and Latency Optimization
The gradual transition from experimental semantic search prototypes to full-blown AI systems by enterprises has transformed pgvector on PostgreSQL into the de facto choice for embedding storage defined for search and memory for AI agents. The most striking differentiator is ironically quite simple: the vector search runs inside the same governed, transactional database where all […]
Governed Multimodal Pipelines: Connecting Text, Image, and Metadata to Create One Intelligent Core
Today, enterprises face a new frontier: moving beyond understanding unstructured text or transactional records. Instead, an AI application seamlessly hosts text, images, audio, logs, embeddings, and rich metadata at once-gathered signals coming from customer interactions, fraud signals, catalogs of products, or hours of video surveillance, medical diagnostics, and intelligent agents. In fact, that data is […]
Designing with portability in mind: On-premises, cloud, and Kubernetes strategies for AI-ready Postgres
AI-driven architectures rigorously reshape the ways enterprises store, process, and reason upon data. Established as the spine of transactional systems, PostgreSQL rapidly evolves into the solid base for vector search, AI agent memory, and real-time decision pipelines. However, the ultimate challenge becomes critical as organizations spread across on-prem data centers, create multi-cloud deployments, and establish […]
Efficient Similarity Search: Unlocking Smarter Recommendations with AI Databases
Consumers today demand a personal and instantaneous experience, and certainly, such a thing cannot be issued as a generic search with hard filtering as consumer trends. Hence, similarity search came into play, which is the ultimate secret weapon of AI databases and drives everything-from managing product recommendations on an e-retail platform for e-commerce sites or […]
Contextual Understanding with AI Databases: Fueling Next-Gen AI Applications
The AI realm continues to present a dynamic interface, with the resurgence of another old problem of AI: absence or lack of context. If a model does not have context, it cannot render output in a way that meets the need or is appropriate. The RAG system generally makes a good example of how AI-based […]
Enhanced Performance and Scalability: How AI Databases Power Real-Time Applications
The pressing necessity imposed upon businesses in the digital-first economy is apparently to absorb huge molecules of data in real-time, which defines speed as the measure of success. Be it online shopping carts handling millions and millions of transactions or health care platforms processing patient data on the fly; both performance and scalability have become […]