OLIMPO combines three analytical models to forecast demand and optimize inventory. Our dedicated server processes pre-loaded markets and products, analyzing exact market and product variability using pure statistical math. Align historical data with dynamic projections to plan production accurately. Get enterprise-grade precision via a flexible pay-per-calculation credit system for B2B supply chain professionals.
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Maker
📌
"Hi Product Hunt! 👋 I’m a Logistics Engineer and the creator of OLIMPO.
Throughout my career, I've seen countless businesses struggle to forecast demand and optimize inventory. They are usually trapped between fragile, broken Excel spreadsheets and massive, expensive ERPs.
That’s why I built OLIMPO. It’s not another 'generic AI' wrapper. It’s a pure statistical and mathematical calculation engine built for real supply chains.
Here is what makes it different:
⚙️ 3 Specialized Models: Epsilon, Sigma, and Poseidón handle everything from calculating safety stock to dynamic production planning.
📊 Context-Aware: Our dedicated server processes pre-loaded market and product data to analyze real variability in milliseconds.
💳 Accessible B2B: We use a pay-per-calculation credit system. You get enterprise-grade precision without the heavy annual contracts.
I designed the interface with a dark, minimalist approach to keep the focus entirely on the data and strategic decisions.
I’d love to hear your feedback on the UI, the mathematical approach, or our credit system! Drop any questions below, I'll be here all day answering them. Cheers! 🚀"
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How does the pay-per-calculation credit system actually work in practice for a business running weekly forecasts across multiple product lines — does the cost stay predictable or does it fluctuate a lot depending on data complexity?
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Maker
@resulboncokqfx Good question — it's flat-rate, not usage-based on complexity. Each forecast run costs 1 credit regardless of how much historical data you feed it or how many SKUs are involved, so weekly runs across multiple product lines stay fully predictable — you're just multiplying credits × runs, no surprise scaling.
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How does the pay-per-calculation credit system actually work in practice for someone running forecasts weekly, and are there any minimum purchase tiers or volume discounts baked in?
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Maker
@savamuslukepnm There are 3 tiers (Starter/Pro/Business), each with a different monthly credit allotment — the higher tiers work out to a better cost-per-credit, so it functions like a built-in volume discount without needing to negotiate anything. No forced minimum beyond picking a plan, and you can move between tiers as your usage changes.
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How does the pay-per-calculation credit system actually work in practice, like is there a minimum commitment or can I just run a few forecasts to test the accuracy first?
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Maker
@mltuana63097 No minimum commitment — plans are month-to-month, cancel anytime. And you don't even need to subscribe to check accuracy first: new accounts get 3 free credits to run real forecasts before putting in a card.
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How does the pay-per-calculation model actually work in practice, like do credits roll over month to month or is it strictly pay as you go with no commitments?
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Maker
@yaarkzlj Right now it's strictly monthly — credits reset each billing cycle, no rollover yet. We're still early and actively shaping the product around real user feedback, and rollover is something we're seriously considering based on exactly the kind of question you're asking.
What we didn't want to compromise on is the engine itself — the forecasting math is solid and consistent regardless of how the credit system evolves. If you want to see if the accuracy holds up for your use case, the free credits are enough to run a couple of real forecasts and judge for yourself before committing to anything.
Appreciate you asking directly — this kind of feedback is genuinely what's shaping what we build next.
How does the pay-per-calculation credit system actually work in practice for a business running weekly forecasts across multiple product lines — does the cost stay predictable or does it fluctuate a lot depending on data complexity?
@resulboncokqfx Good question — it's flat-rate, not usage-based on complexity. Each forecast run costs 1 credit regardless of how much historical data you feed it or how many SKUs are involved, so weekly runs across multiple product lines stay fully predictable — you're just multiplying credits × runs, no surprise scaling.
How does the pay-per-calculation credit system actually work in practice for someone running forecasts weekly, and are there any minimum purchase tiers or volume discounts baked in?
@savamuslukepnm There are 3 tiers (Starter/Pro/Business), each with a different monthly credit allotment — the higher tiers work out to a better cost-per-credit, so it functions like a built-in volume discount without needing to negotiate anything. No forced minimum beyond picking a plan, and you can move between tiers as your usage changes.
How does the pay-per-calculation credit system actually work in practice, like is there a minimum commitment or can I just run a few forecasts to test the accuracy first?
@mltuana63097 No minimum commitment — plans are month-to-month, cancel anytime. And you don't even need to subscribe to check accuracy first: new accounts get 3 free credits to run real forecasts before putting in a card.
How does the pay-per-calculation model actually work in practice, like do credits roll over month to month or is it strictly pay as you go with no commitments?
@yaarkzlj Right now it's strictly monthly — credits reset each billing cycle, no rollover yet. We're still early and actively shaping the product around real user feedback, and rollover is something we're seriously considering based on exactly the kind of question you're asking.
What we didn't want to compromise on is the engine itself — the forecasting math is solid and consistent regardless of how the credit system evolves. If you want to see if the accuracy holds up for your use case, the free credits are enough to run a couple of real forecasts and judge for yourself before committing to anything.
Appreciate you asking directly — this kind of feedback is genuinely what's shaping what we build next.