ahmed-khalil-hafsi/P2Predict

**P2Predict turns your purchasing history into a price model your team can talk to. Ask what a part should cost, why, and how sure the answer is, in plain language, through the AI agent you already use.**
 
 
 
 
**Set up in one command:**
Built for procurement and engineering teams in automotive, semiconductors, electronics, industrials, pharma, and chemicals.
The problem
Most of what a part costs is decided upstream, in a design review procurement never sees. An engineer tightens a tolerance beyond what the application needs, or locks the part to a single supplier, and the cost rides downstream with no number attached. By the time the BOM reaches procurement, the expensive decisions are frozen and all that is left to negotiate is the rounding.
Then the quote arrives. The supplier knows their cost to the cent; you have last year's PO and a few days to respond. Multiply that across every line you buy and the leaks look the same everywhere: premiums no one benchmarked, specs no one costed, a number you cannot defend when finance asks where the savings went.
The answers are already in your purchase history. Nobody has had the time to dig them out.
What P2Predict does
P2Predict learns from your own purchasing data what drives the price of a part: supplier, material, size, spec, region. Then it gives your team a defensible target for any new or proposed part. Your category managers ask in plain English. The answer comes back grounded in what you have actually paid.
It runs on your machine, on your data, through any AI agent your team already uses. Nothing is uploaded. No vendor catalog, no cloud, no per-seat data-sharing.
What it is, and what it isn't
P2Predict is **parametric price prediction**. It learns the fundamental pricing structure in your historical buying data and benchmarks any part against it, the ones you are about to buy and the ones you already do. Being precise about that is the whole point, so here is the honest scope.
**What it does**
- Learns from the prices you have actually paid and predicts what a similar part should cost.
- Attributes that predicted price to each spec and to the supplier, so you can see what is moving the number.
- Puts a calibrated likely-range on every estimate and flags where the data is too thin to trust.
- Improves as you give it more of your own purchase history.
**What it does not do**
- **It is not a should-cost tool.** It does not build a part up from raw material, labor, and machine time, and it cannot tell you a supplier's true cost or margin.
- It only knows what your data has shown it. Ask about a part unlike anything in your history and it will widen the range or tell you to get a quote rather than guess.
- The per-spec breakdown shows what is *associated* with price in your data. It is a read on your market, not a causal or engineering model of why a part costs what it does.
- It does not invent data. No relevant history, no model.
The conversations it changes
This is where P2Predict earns its keep. Every one of these is a real question your team can now answer in seconds, with a number and the confidence behind it.
"Is this quote fair?"
Your category manager drops the quote on the agent.
> **Category manager:** *"Supplier quotes $14.20 for this part. What should it cost?"*
>
> **P2Predict:** *"$12.40. Nine times in ten the real price lands between $10.80 and $13.90. This quote is running about 15% high."*
Your category manager now knows exactly where they can push back, with real data behind it.
When the supplier pushes back: "Are you sure that's the right price?"
This is where most negotiations stall. Now you have an answer. Ask for the breakdown.
> **Category manager:** *"Why $12.40? Break it down."*
>
> **P2Predict:** *"Supplier choice +$0.85, rush delivery +$1.20, tighter tolerance +$0.42, size +$0.40."*
Now you argue the components line by line: *"We agreed standard lead time. Take the $1.20 rush charge off and we're aligned."* A line item is hard to wave away.
"What if we switch supplier?"