Updates on production AI systems, engineering practice, recent technology, and company work from the Vincere.dev team.
How confidence scoring, constrained generation, citations, and a refusal-driven feedback loop turn the questions an AI agent can't answer into a prioritized roadmap for improving its knowledge.
How we layered indexed lexical search into a Postgres-based AI agent retrieval system using ParadeDB and scoped BM25 queries.
A practical blueprint for ingesting, chunking, retrieving, evaluating, and operating RAG over large document collections.
A field guide to the moving parts that make RAG reliable beyond the demo: retrieval, reranking, guardrails, observability, and evals.
Enterprise RAG needs refusal behavior, confidence signals, and evidence thresholds so the system can avoid unsupported answers.
A diagnostic guide to why RAG systems still hallucinate in production, mapping each failure mode to its root cause and the fix that actually addresses it.
A cost-aware blueprint for fresh dashboards: where near real-time spend actually goes, and the architecture decisions that keep it predictable for finance and data leaders.
Practical lessons from building a healthcare data warehouse automation platform that supported 100+ pipelines with a 3-5 person data team.
We help teams design, deploy, and operate RAG systems, AI workflows, and data infrastructure in real business environments.
