Building a Closed-Loop RAG Flywheel: Operationalizing Expert Feedback to Reduce Hallucinations
Standard Retrieval-Augmented Generation (RAG) pipelines often encounter quality ceilings in production. While synthetic benchmarks and evaluation frameworks like... Read more.
Temporal Data Lineage for Auditing
Destructive updates in traditional relational databases overwrite history, causing immediate spoliation of evidence during regulatory discovery. When an auditor... Read more.
Beyond Pure Vectors: Implementing High-Precision Hybrid Search Natively inside Postgres
We observed a 500ms synchronization lag when separating our storage engine from an external search cluster, which causes broken user experiences when resolving exact... Read more.
From Notebook to Production: Building End-to-End ML Pipelines with Kubeflow, KServe, and Fractional GPU Sharing
Transitioning a machine learning model from an experimental Jupyter Notebook to a highly available, auto-scaling production endpoint is rarely a linear path. In... Read more.
Start Fresh, Don’t Lift and Shift: Scaling Analytics Platforms with dbt-core and PostgreSQL
We observed that executing a “lift and shift” of legacy, sprawling SQL scripts onto an enterprise cloud data warehouse fails to resolve core structural... Read more.
PostgreSQL Data Mesh: A Technical Guide to Schema Segmentation, Boundaries, and Governance
We deploy PostgreSQL natively to execute a decentralized data mesh architecture, proving that multi-million dollar cloud platforms and proprietary vendor ecosystems... Read more.
Deterministic RAG Auditing: Implementing Verifiable Grounding & Lineage on Unified PostgreSQL
The pervasive “lost in the middle” phenomenon is a failure of semantic retrieval, not just context window capacity. While increasing token limits is... Read more.
Beating “Lost in the Middle”: Unified Graph RAG on PostgreSQL
Our evaluation shows that by substituting naive chunk-based vector lookups with relationally injected context, the model’s $F_1$ verification score increased from... Read more.
RAG Context Pruning for Efficiency and Cost Optimization
After baseline production runs across our clients’ financial discovery pipelines, we observed an increase in Time-to-First-Token (TTFT) when retrieved context... Read more.
Production-Grade Compliance: Engineering the EU AI Act into Sovereign Agentic Pipelines
We measured a 42% increase in inference latency when we shifted from standard RAG to a cryptographically-verifiable audit chain. We accept this overhead. After 2,000... Read more.