Core Resources https://gradien.ai/resources/ Guides, perspectives, research, and product updates from Gradien. Core brings your proprietary knowledge into the AI surfaces you already use. https://gradien.ai/updates/core-brings-your-proprietary-knowledge-into-the-ai-surfaces-you-already-use/ https://gradien.ai/updates/core-brings-your-proprietary-knowledge-into-the-ai-surfaces-you-already-use/ Mon, 20 Jul 2026 00:00:00 GMT Gradien is launching Core, a layer that connects to the AI surfaces and work tools your team already uses, captures the context behind completed work with your approval, and makes that knowledge available inside whichever AI tool you open next. Core is available now with a 14-day trial and no card required, followed by Pro and Max plans priced per user. Compounding AI work requires a system that captures, understands, and learns. https://gradien.ai/resources/compounding-ai-work-requires-capture-understand-learn/ https://gradien.ai/resources/compounding-ai-work-requires-capture-understand-learn/ Mon, 20 Jul 2026 00:00:00 GMT This piece argues that AI work only compounds when a system does more than retrieve stored information. It lays out the capture, understand, learn loop behind Core and explains why human approval, not automatic updating, is what turns AI output into trustworthy company knowledge. Proprietary knowledge emerges when context and workpaths remain connected. https://gradien.ai/resources/proprietary-knowledge-emerges-from-context-and-workpaths/ https://gradien.ai/resources/proprietary-knowledge-emerges-from-context-and-workpaths/ Mon, 20 Jul 2026 00:00:00 GMT Companies keep feeding AI tools their explicit knowledge, documents and playbooks, while the tacit knowledge that actually drives decisions stays scattered in people's heads. This piece argues an AI system only becomes genuinely useful to one specific company when a knowledge graph and the workpaths that generate it stay connected, with humans approving what gets kept. Build workflows that improve each time they run. https://gradien.ai/resources/build-workflows-that-improve-each-time-they-run/ https://gradien.ai/resources/build-workflows-that-improve-each-time-they-run/ Mon, 20 Jul 2026 00:00:00 GMT This guide explains how to preserve the corrections, decisions, approved outputs, and outcomes from a recurring workflow so each run needs less reconstruction than the last. It uses a weekly report's first, tenth, and hundredth run to show what actually shrinks over time and why the improvement compounds. Decide which context should remain private and which context should become shared. https://gradien.ai/resources/decide-what-context-should-be-shared/ https://gradien.ai/resources/decide-what-context-should-be-shared/ Mon, 20 Jul 2026 00:00:00 GMT A practical method for separating what should stay in your own working notes from what should move into your team's shared knowledge, built around a simple rule: nothing becomes shared knowledge until someone with the authority to do so has approved it. Give every AI task the company context it actually needs. https://gradien.ai/resources/give-every-ai-task-the-context-it-actually-needs/ https://gradien.ai/resources/give-every-ai-task-the-context-it-actually-needs/ Mon, 20 Jul 2026 00:00:00 GMT A practical framework for deciding what an AI task actually needs, from sources and decisions to permissions, illustrated with a weekly client update at a fictional company. The method works with or without Core, which automates the same assembly across ChatGPT, Claude, Cursor, Codex, and Claude Code once a team adopts it. AI should continue the work instead of restarting the conversation. https://gradien.ai/resources/ai-should-continue-the-work-instead-of-restarting/ https://gradien.ai/resources/ai-should-continue-the-work-instead-of-restarting/ Mon, 20 Jul 2026 00:00:00 GMT Every new AI conversation starts from zero unless someone repeats the background by hand. Gradien argues that continuing the work, not restarting the chat, is what a durable context layer should deliver, and explains how Core keeps decisions, preferences, and sources attached across tools instead of letting each surface forget. Your working record remains yours as models change. https://gradien.ai/resources/your-working-record-remains-yours-as-models-change/ https://gradien.ai/resources/your-working-record-remains-yours-as-models-change/ Mon, 20 Jul 2026 00:00:00 GMT Models come and go, but a company's working record, its sources, decisions, preferences, and workpaths, should not have to migrate or disappear when they do. Gradien argues that this record belongs in a layer of its own, one that any AI surface can read from and that no single vendor controls. Your company's most valuable AI asset is the knowledge between the prompts. https://gradien.ai/resources/your-companys-most-valuable-ai-asset/ https://gradien.ai/resources/your-companys-most-valuable-ai-asset/ Mon, 20 Jul 2026 00:00:00 GMT The finished document from an AI session is not what makes it valuable. The decisions, corrections, rejected alternatives, and approvals that produced it are the proprietary knowledge worth keeping, and that knowledge should outlast whichever model or tool generated the output.