Tag: Data & AI

This feature in SD Times explores how Progress’ DataDirect Autonomous REST Connector (ARC) provides an easy way for companies to build and deploy data connectors to any REST API source without needing to wait for someone in IT to set it up.

This article explores the shift from retrieval-augmented generation (RAG) tutorials to production-ready architectures, focusing on latency, cost control, reliability and compliance in real-world deployments.

This feature in SD Times explores how companies today want to leverage their data to support business intelligence (BI) initiatives, but without the proper data connectivity processes and tools in place, that data could remain locked in silos. Enter the Progress DataDirect Hybrid Data Pipeline (HDP) connectivity solution, which allows companies to securely connect cloud and on-premises data to BI tools and vendors to connect third-party data and reporting tools so they can deliver more value to their customers.

Deep research is iterative, not transactional. AI must preserve context, reasoning and evidence across long-running investigations to be useful in R&D. Trust is the gating factor. When outputs can’t be traced, reviewed or defended, AI stalls at the pilot stage and never reaches production. Production-ready AI compounds research value. Deep research systems that are governed, explainable and reusable turn isolated insights into institutional advantage.

R&D doesn’t lack data—it lacks signal. AI-driven knowledge discovery only works when answers are grounded in trusted, contextual enterprise data, not probabilistic guesswork. Most AI tools break trust before they create value. Treating research data like generic internet content strips away context, provenance and scientific rigor. Boring, reliable AI wins in 2026. Knowledge discovery that is governed, explainable and embedded into real R&D workflows is what turns AI from pilots into lasting outcomes.