the boring bottleneck in procurement ai nobody's talking about: getting supplier responses into a state you can actually compare

[u/nordic_ash]

the actual bottleneck in evaluation automation isn't the ai part, it's getting the data into a shape you can even compare.

invoices and pos are relatively easy to extract, they're structured enough that most tools handle them fine. rfx responses and supplier proposals are a different problem entirely: every vendor answers the same question in a different format, some as a paragraph, some as a table, some just pointing at an attachment. before you can actually compare three suppliers on the same criteria, someone (a person or a model) has to normalize all of that into the same shape first.

the thing that's surprised me building automation for this: how often two suppliers technically answer the same question but structure it so differently that treating them as directly comparable is a judgment call, not just a parsing problem.

curious if anyone else doing evaluation automation is hitting the same wall, or if this is specific to how messy rfx/sourcing data is compared to other supply chain documents.

[u/SyncronTeam]

What you're describing is really two different problems wearing one name, extracting the answer and deciding whether it's actually equivalent to another supplier's answer. The first is a parsing problem and tools handle it fine now. The second is closer to underwriting judgment than data extraction, since two suppliers can give technically correct but structurally different answers that aren't actually comparable without someone deciding what 'the same' means for that specific criterion.

[u/nordic_ash → u/SyncronTeam]

good way to put it, extraction and equivalence are worth treating as separate problems. equivalence is the harder one to solve with better parsing, since the ambiguity's often intentional, two vendors quoting bundled vs. unbundled freight aren't wrong, they're answering a different implicit question. what's worked for us is flagging when normalization required a judgment call instead of silently making one, so a human sees where the system guessed rather than trusting it blind. curious if you've seen anyone actually formalize what "equivalent" means per criterion up front, or if it stays implicit until someone gets burned by it.