Generative AI has cut the time it takes to write specifications and code. But can you say for sure that its answers are right? Engineers still re-check unsourced AI output from scratch, and responsibility for quality stays with people. Faster, perhaps, but not more certain. In automotive software, vital know-how, such as the reasons behind design decisions and lessons from past defects, still lives in a few experts' heads. Inconsistent terms, unstated assumptions and scattered evidence keep it from being reused. This talk presents three conditions for raising development efficiency and quality at the same time by combining AI with knowledge (the experience and know-how an organization has accumulated): (1) every answer can be traced back to its evidence; (2) answers a machine can determine with certainty are clearly separated from AI advice; (3) knowledge approved by people grows the more it is used. Our common platform, "AI × Knowledge," puts these conditions into practice. At its core are a knowledge graph, which links knowledge through relations such as "cause → effect" and "requirement → design," and an ontology, a shared dictionary of terms and their relations. We combine these with an LLM (large language model, an AI that understands and writes text) and a deterministic engine (rule-based checks that always give the same answer for the same input), and every answer carries its source. On this platform we offer purpose-built packages. ■ APK (AI-Powered Knowledge Framework): for development and design Its core, MBSE Assist, supports MBSE (Model-Based Systems Engineering), which links requirements, design and verification through models rather than documents. Across seven stages from requirements to testing, it reliably flags issues with the exact rule clause, traces change impact and finds gaps in test specifications. It also finds missing design specifications based on standards such as ISO/IEC 25010 (software quality) and fills them with design solutions from past projects, shown at the booth with an electric parking brake (EPB). Three more packages: Code Analysis AI Assist finds gaps between specifications and source code and proposes fixes; Safety Knowledge Assist supports functional safety (designing so failures do not cause hazards), from HARA (hazard analysis) and FMEA/FTA (failure analysis) to deriving safety requirements (SG, FSR, TSR); E2E Assist supports end-to-end automated driving, where one AI model goes from sensor input to vehicle control. ■ Prism Connect: for factory safety, equipment and maintenance Six packages put data from equipment, sensors, cameras and robots to work: equipment events, safety monitoring, conveyance safety, predictive scoring (early signs of failure as numbers), operator support and AI knowledge integration. A real six-axis robot shows symptoms leading to candidate causes, quoted from maintenance records, carrying design knowledge onto the shop floor. Don't leave everything to AI: return what a machine can determine with certainty, and turn human judgment into an organizational asset with its evidence attached. We will also show how to start with a single design change.