Preserving R&D rationale: the why behind every design decision
This use case preserves the most expensive knowledge R&D produces: the reasoning behind design decisions and the memory of everything tried and abandoned. Reports keep the results; the why — and the graveyard of alternatives — lives in the specialist's head. With a Knowledge Space, future projects query both, and stop paying for the same experiments twice. Used by R&D teams, product managers and the engineers who inherit legacy designs.
How companies handle this today
Today, an R&D archive is a library of successes: what shipped, documented; what failed, remembered — by one person, until he leaves. Teams starting a new development reflexively re-explore old dead ends because nothing marks them as dead, and legacy products become untouchable because nobody knows which constraints are physics and which were a supplier limitation from 2018. The cost is invisible precisely because it looks like normal R&D work — it is just R&D work that was already done once.
With a Knowledge Space
MentX is a personality-aware knowledge base: it captures the knowledge of a company's key person — from meetings and a guided intake track — into a living, temporal knowledge graph with full source attribution. A product team scopes a cost-reduction on a mature product. Before the kickoff, they query the base: what was tried on this product, what failed and why, which constraints are real. Twenty minutes returns the 2017 aluminum attempt (failed on UV aging, supplier has since reformulated — worth revisiting), the certification dependency that makes one component untouchable, and the specialist's constraint map separating physics from history. The project starts three months ahead of where it would have — at the frontier of what is actually known, instead of at zero.
What you would ask
“What have we tried to reduce the cost of the X40 housing?”
Returns the attempt history with failure modes and dates — including the one that failed for reasons that no longer hold, flagged by the temporal graph as worth revisiting.
“Which constraints in the current formulation are physics, and which are historical?”
Returns the specialist's constraint map from intake: two hard limits, one certification dependency, one 2018 supplier limitation that may have expired — a frozen recipe reopened as design space.
“What did the abandoned project Delta teach us that's still valid?”
Returns the salvage summary — the two findings that held, the premature conclusion, the data's location — so a dead project's paid-for lessons keep compounding.
What it requires from the Knowledge Space
Intake focused on the experiment graveyard and constraint history (see the R&D specialist's knowledge anatomy), with project meetings connected for ongoing rationale. The temporal graph is load-bearing here: every constraint carries its date, so 'still true?' is an askable question. Adoption tip: make the base query a standing item in every project kickoff template — the habit institutionalizes itself after the first prevented re-run.
