Comment by erader
7 days ago
First, I've spent a ton of time becoming opinionated about a normalized data model that supports the product experience I'm trying to build. This applies both to the extraction (line items, warranty sections, vendors, etc.) and the analysis portion. The latter is imperfect, but aligns philosophically with what I'm willing to stand behind. For example
- building outputs for price fairness (based on publicly available labor data)
- scope match (is vendor over/under scoping user's intent)
- risk (vendor risk, timeline, price variability, etc.)
- value (some combination of price, service, longevity, etc.)
I don't get much hallucinations in my testing, but overall it's pretty complex pipeline since it is broken down into so many steps.
i work in cost and pricing, and while i see the allure of AI helping out with it and I would love to be able to hand it over and work on other things, i feel like so much of this work involves things outside the sandbox.
Take price fairness, for example. i feel like the human part is core to this work. it ultimately all comes down to a test of reasonableness. A wide brush for costs is sometimes used because it keeps things trackable by the humans involved. An AI is able to generate an amount of work thats unreasonable to verify. At the end of the day, pricing is a negotiation not a logic puzzle.
If it does work though, i think it could open a huge door for Cost Plus Fixed Fee contracts which seem like the fairest contract type but often come with too much burden of paper work compared to the more popular firm fixed price option