The right price is already in your data.
P2Predict learns the fundamental pricing structure in your own spend and turns it into a defensible number for any part, new or current, through any AI agent your team already uses, on your own machine.
Set up in one command: pip install p2predict[mcp]
Most of your cost is decided upstream, before you ever negotiate it.
Cost is set in design reviews procurement rarely joins. A tolerance is tightened beyond what the application needs, a part is sole-sourced, a premium is accepted because no one has a number to challenge it. By the time the decision reaches procurement it is fixed, and the savings are already gone.
What it does, and what it doesn't.
P2Predict does one thing: parametric price prediction. It learns the fundamental pricing structure in your own data and benchmarks any part against it, the ones you're about to buy and the ones you already do. That's a specific job, so here's the honest line on where it helps and where it doesn't.
What it does
- Learns from the prices you've actually paid and tells you what a comparable part should cost.
- Shows you what each spec and the supplier add to that price, read from your pricing structure.
- Puts a range on every number and tells you when it isn't sure.
- Gets better the more of your own buying history you feed it.
What it doesn't
- It won't build a part up from its raw materials and labour. It's not a should-cost tool, and it can't tell you a supplier's real cost or margin.
- It only knows what you've shown it. Ask about something nothing in your history looks like and it'll widen the range or tell you to go get a quote.
- The breakdown shows what moves the price in your data, not the engineering reason a part costs what it does.
- It won't conjure data out of nothing. No history, no model.
A number you can stand behind
A defensible target for any part, built from what you've actually paid. Not a catalog price, and not a hunch.
Where the number comes from
Every estimate breaks down into what each spec and the supplier contribute, in dollars. So you negotiate the components, not a total.
How sure it is
Every number carries a confidence range. Tight where your data runs deep, wide where it's thin, and clear about which is which.
A math layer for the numbers. A judgment layer for the call.
You don't use P2Predict. Your agent does. There's no dashboard and no app, just two layers running on your own machine behind whatever agent you already use, Claude, GPT, or a local model: a math layer that models the price, and a judgment layer that decides what to run, what to ask you for, and how far the answer can be trusted.
inside p2predict
Tells your agent what to do
It reads the math and steers the conversation: which analysis to run, when to stop and ask you for more, and whether a result is solid enough to quote or needs a real RFQ. It even holds weaker agents back from overstating a number.
Does the modeling
It trains on your spend, predicts the price, attributes it spec by spec, and puts a calibrated range on every estimate. The defensible statistics underneath.
The questions behind every negotiation, answered from your own data.
Quote fairness, supplier spread, spec trade-offs, overpayment across the book — each one a single exchange.
Is this feature worth it?
Price the spec before it's frozen.
Where are we overpaying?
A cost-recovery list ranked by dollars.
What does this chip cost across suppliers?
4× from value pick to premium. That's your range.
Price these, and how sure are you?
Tight, negotiate hard. Wide, get a quote first.
How the model holds up.
What drives price, how accurate it is on parts it never saw, and how price scales with package complexity. From the Battery Management ICs case study.
100% local, nothing uploaded
Spend data is among the most sensitive you hold, so P2Predict runs entirely on your own machine and never uploads it. Point it at a local model to keep the whole loop offline, or at Claude or GPT if you prefer. Either way, your raw spend stays put.
Agent-agnostic by design
It connects to any AI agent over a standard interface, Claude, GPT, or a local model. Your agent drives it, not you, so there's nothing new for your team to roll out or learn.
Free inside your company
Free for internal use, with every line of code open to read. No seats to count, no sales call to sit through.
It admits what it doesn't know
When the data is too thin to support a number, it says so and points you to a quote instead. Every estimate traces back to the evidence behind it.
Three worked examples you can run yourself.
Battery Management ICs
About as close to a real procurement job as it gets. A supplier premium you can actually quote, and a straight answer on where the numbers hold up and where they don't.
Read it →Used vehicles
A clear walkthrough on prices that span orders of magnitude.
Read it →Aerospace fasteners
The honest case: where the data itself sets the limit, and the model says so.
Read it →Walk in knowing your number.
Free, runs in your own environment, and one command from your first benchmark.
pip install p2predict[mcp]