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.
Unified Graph-RAG in a Single Postgres Engine
Our production benchmarks confirm that consolidating Hybrid Graph-RAG into a single PostgreSQL instance via pgvector and Apache AGE reduced cross-service network... Read more.
Production Metric: 14.2% Semantic Decay
After processing 2.8 million unstructured retail fragments, we observed that 14.2% of records passing traditional NOT NULL and regex constraints contained semantic... Read more.
Cost-Aware Agentic Workflows with PydanticAI
Introduction: The Hidden Price of Autonomy The Architecture of a Cost Guardrail Implementing Usage Limits with PydanticAI PydanticAI provides the primary library-level... Read more.
Specialized Judges: Scaling RAG Evaluation with Prometheus-2 and PydanticAI
Our production benchmarks utilize the Feedback Collection and Preference Collection datasets to establish the performance delta between generalist and specialized... Read more.
The Future of Automation is Local: Why German Firms are Trading the Cloud for On-Premise AI
In early 2026, the AI landscape reached a crossroads. On one side, we have the “reasoning giants”: GPT-5.4 and Gemini 3.1 Pro. These models offer unprecedented... Read more.
From Generalist to Specialist: Benchmarking the 25x Speedup of Fine-Tuned “Tiny Compilers”
We measured a 96.7% reduction in inference latency by migrating our EDI logic from Llama 4 (70B) to a fine-tuned Llama 3.2 (1B) “Tiny Compiler.” In high-volume... Read more.