Each one runs on real BOMs, real ERP exports and real distributor data. Names withheld, numbers real.
NPI · SRCBuilt in 2 days
NPI sourcing across six distributors
Every new product introduction means pricing a fresh part list. Buyers checked distributor sites one part at a time. Looking up distributor data by hand could take 3 to 12 hours per BOM.
Upload a part list; the tool queries six authorized distributors live: DigiKey, Mouser, element14, Future, TTI and TI
Everything is converted to US dollars with a dated exchange rate
Picks the best buy per line: stock first, then lowest total cost, with MOQs and order multiples
Exports to Excel for purchasing
6distributor APIs in one pass
108offers compared for 20 parts in a live test
18/20lines recommended. The other 2 were held for review, not guessed.
Lesson Missing prices never become zero. If data is missing, the tool says so.
Customer BOMs, schematics and pick-and-place files disagree more often than anyone likes. Engineers checked them by eye, and misses showed up at kitting or first article.
Checks the BOM, schematic PDF and pick-and-place file against each other, designator by designator
AI reads the schematic; every mismatch goes to an engineer to decide
The clean BOM can’t be exported while any conflict is still open
Each customer’s BOM format is saved once and reused
137designators checked on one real board
9issues caught before purchasing: 4 conflicts, 5 parts missing from the BOM
0exports allowed while a conflict is open
Lesson Parse broadly, decide narrowly. The tool reads everything; an engineer makes every call.
Customer knowledge lived in four places: the ERP customer list, an RFQ log, monthly business-development slide decks and trip reports. Preparing for a meeting meant digging through all four.
An import that a non-engineer re-runs every month
One record per customer, with opportunities and notes
AI research briefs on each customer, with sources
Meeting prep: what to know, what to ask, what to follow up
100+customers in one place
~1,000updates recovered from dozens of slide decks
~300RFQ and site-visit notes linked to the right customer
Lesson Slides aren’t text. Reading them as plain text misattributed about 40% of updates. Reading them as images fixed it.
The customer’s approved manufacturers for each part were spread across Word and PDF specifications. Building one usable list meant copying tables by hand, and conflicting entries were easy to miss.
Reads the tables in Word and PDF specs, with AI as a fallback for messy ones
Processes many documents in parallel
Merges duplicates and reports every merge
Never merges rows with conflicting status, such as “Preferred” vs “Do Not Use”
24documents processed at once
100%of merges shown in a report
0conflicting statuses merged
Lesson Merging the wrong rows loses the one decision a buyer needs.