How do you preserve an R&D specialist's design rationale?
An R&D specialist's most valuable knowledge is negative: everything that was tried and failed, and why. Notebooks and reports record the successes that shipped; the dead ends — the most expensive knowledge to acquire — live in his memory. When he leaves, the company starts paying for the same experiments a second time.
The anatomy of this knowledge
R&D knowledge has the largest gap between what is documented and what is known:
The experiment graveyard
Every material, geometry and process that was tried and abandoned — with the failure mode. This is where R&D budgets actually went, and it exists as institutional memory in exactly one head. Repeating a known-dead experiment costs the full price twice.
Design rationale and constraint memory
Why the formulation is exactly this. Which constraint is physics, which is a supplier limitation from 2018, and which is habit. Successors who cannot tell these apart either never improve anything or break something load-bearing.
Material and supplier boundaries
Which supplier's spec sheet can be trusted, which material behaves differently than its datasheet, what the real process window is versus the certified one. Bought with scrapped batches.
Regulatory and testing memory
How the certification was actually achieved, which test setups have quirks, what the auditor cared about. Knowledge that resurfaces as panic at every renewal.
Why a standard handover misses it
R&D handovers transfer the archive: reports, notebooks, data. But reports are written for what worked — failure documentation is thin, unindexed, or absent, because nobody budgets writing time for dead ends. The successor inherits a library of successes and none of the map of where the mines are. The gap is invisible until an old failure gets expensively rediscovered.
What a MentX intake focuses on here
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. For an R&D specialist, the intake track mines the graveyard first: per product, what was tried and dropped; per constraint, whether it is physics or history. Meeting ingest catches the running project discussions where the rationale is used. In the temporal graph each finding keeps its date and context, so the difference between a 2015 limitation and a current one stays visible.
Who asks the DigiME — and what
Once the base has depth, the team converses with the expert's digital knowledge copy through DigiME. Three questions it answers for this role:
“Why don't we use polymer X in the housing — has it been tried?”
The DigiME returns the 2017 trial: passed the mechanical tests, failed on UV aging after 18 months, and the supplier reformulation since — sourced and dated, so the team knows both the failure and that it might be worth revisiting.
“Which constraints in the current formulation are actually fixed?”
His constraint map: two are physics, one is a certification dependency, one is a supplier limitation from 2018 that may have expired — turning a frozen recipe back into a design space.
“What did we learn from the abandoned project A?”
The salvage summary: the two findings that were valid, the one conclusion that was premature, and where the data lives — so the investment keeps paying although the project died.
