Why We Built Inherent: The Production Memory Problem
The founder story behind Inherent: a 2am session where the same query returned different context — and why that became a context engine for reliable AI memory.
Plain-English definitions of the words in every AI pitch deck.
11 posts
The founder story behind Inherent: a 2am session where the same query returned different context — and why that became a context engine for reliable AI memory.
Why strong AI models still produce wrong answers in production — and how the context layer, not the model, is the thing that's missing.
Enterprise AI fails at the discipline layer, not the model. A decade of jet-engine software on the People, Process, Tools sequence leaders get backwards.
Knowledge base AI turns company knowledge into AI answers. CEOs should care because stale, unowned, or overshared docs create expensive wrong answers.
Context engineering is the discipline of giving AI the right context, permissions, memory, and proof so business agents can work reliably.
Agentic AI is AI that runs a business loop: observe context, decide, act, check, and escalate. CEOs should define where that loop stops.
AI agents are useful only when the workflow has clear goals, tools, boundaries, context, and review points. Start with the work.
Enterprise AI is not a model purchase. Leaders are buying faster workflows, better decisions, and reliable context for real operations.
Artificial intelligence in business means software that helps predict, classify, generate, or act. Learn the basics before buying AI tools.
A context engine is the governed memory layer that gives AI agents source truth, permissions, retrieval context, and audit receipts before production work.
A knowledge base used to help humans find answers. In AI systems, it becomes the governed truth layer agents depend on for reliable context.
Inherent on Substack
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