Replacement parts matched from any number or nameplate
Identifies replacements for obsolete or worn part numbers from nameplates, photos and past orders and lays out the best match and stock for the rep.
The customer's number is rarely your number.
Customers ask for parts by competitor numbers, discontinued codes or a photo of a nameplate. Finding a true equivalent means checking dimensions, ratings and interchange tables. Only a few senior reps know where to look, and a wrong guess comes back as a return.
A maintenance tech texts a photo of a worn motor nameplate and says the line is down. The counter rep spends 40 minutes on vendor sites and calls before finding an equivalent.
Part number cross-reference, step by step.
The agent does the work inside your systems. Your team approves what matters.
- 1
Read the part
Pulls the number, nameplate data or dimensions from texts, photos and emails.
- 2
Find equivalents
Searches interchange data, vendor catalogs and past orders for matching specs.
- 3
Confirm the match
Lists the best match with key specs side by side and stock on hand for the rep to confirm.
Your team keeps the judgment calls.
The agent takes the repetitive work. People approve, decide and talk to customers.
- Fewer returns from wrong parts
- Junior reps answer like senior reps
- Down lines back up sooner
Works inside the systems you already run
Before you ask.
Identifies replacements for obsolete or worn part numbers from nameplates, photos and past orders and lays out the best match and stock for the rep. It works in three steps: read the part, find equivalents and confirm the match.
It works inside the tools your team already uses, including ERP, vendor catalogs, text messaging and email. Nothing to rip out and nothing new to learn.
It starts in shadow mode, drafting the work for your team to approve or correct. You decide what it handles on its own, and every action is logged.
Your data is used only to run the workflows you approve. We do not use your proprietary data to train public or unrelated AI models.
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Start with one agent.
Pick the workflow that hurts most. We'll scope it on the first call.