Why MEGA
The experiment already ran. Inside a live $300M+ retailer, for years, against real OTB.
You are not betting on a v1. MEGA is the merchandising platform a multi-country, multi-category retailer built to run its own P&L, then productized. The origin retailer stays anonymous. The breadth is the evidence it was built for real multi-category operations.
Built inside a $300M+ multi-country, multi-category retailer to run its own P&L. It ran real OTB, allocation, and markdown cycles there for years. The five-vertical breadth stands in for a logo wall while the origin retailer stays anonymous.
One engine. Five verticals on one spine. Breadth by design, not bolt-on.
- Fashion size curves, seasons, newness
- Beauty shade depth, shelf rotation
- Eyewear prescription SKU sprawl
- Supermarket perishability, shelf-life
- Multi-brand mixed margin, mixed cadence
The architecture test
What breaks if you remove the AI?
In an AI-adopted company, productivity dips and the business keeps running. In an AI-native company, the business model breaks. MEGA is the second kind. The six pillars are AI in the core: real-time access to your own data, agents that take typed actions inside the workflow, a continuous learning loop. Not a chat box stapled to a legacy grid.
A hands-off supply chain.
We do not promise it. Industry analysts, Gartner among them, have cautioned that fully autonomous supply chains are years out. We claim one rung, execute-within-guardrails on narrow, measurable wedges, and we let you judge it on your data. The refusal to overclaim is the whole positioning.
The hard questions
The six objections, answered straight.
The framework is clarify, diagnose, answer with proof. Here are the answers we give, without "we can build that" or "everyone asks that."
01 You are an unproven startup. Too risky to put part of our buying on.
You are not the experiment. The experiment already ran. MEGA was built and run inside a live $300M+ multi-country retailer for years, against real OTB, allocation, and markdown cycles, then productized. You adopt a system that already ran a real merchandising operation, not a v1. And the pilot de-risks it further: run it alongside your current process, on your data, before you commit.
02 AI-native and agentic are everywhere. How is this not more agent-washing?
Fair. Gartner has been blunt that only a small fraction of self-described agentic vendors are doing anything real, and we will not insult you with end-to-end autonomy claims. Here is the test: what breaks if you remove the AI? For us the business model breaks. The agents take typed actions inside your workflow, on your governed data, and learn from feedback. We claim one honest rung, recommend then execute within guardrails, and each agent action is logged so it can be explained, approved, overridden, and audited.
03 We already have an enterprise suite. o9, Blue Yonder, Anaplan, RELEX.
Then you know the tax: 6 to 24 month SI-led rollouts, a dollar of services per dollar of license, scarce specialist modelers, vendor-hosted data, and UIs your merchants avoid. Those are engines you spend a year building. MEGA is a retailer operating model on day one, in your cloud in 30 to 90 days. You do not rip anything out. Prove it on one workflow in parallel.
04 Our security and data-residency review will take months.
That is exactly why your-cloud and your-data leads our security page, not the footnote. MEGA runs in your environment, your data stays region-pinned, and it is not copied out or used to train models. We ship a Trust Center with a pre-filled security questionnaire so the review does not add weeks. Many rivals run AI on a multi-tenant vendor cloud, which is what raises the security objection; your-cloud is the answer to it.
05 You are too expensive. We have no budget right now.
Let us compare the cost of the status quo instead. Excel-driven planning recalculates your OTB on bad inputs, and out-of-stocks bleed margin every season. We bind price to a modeled lever-to-result on your numbers, and the first step is small: a time-boxed paid pilot on one workflow that procurement can approve as Phase 1. We agree the KPI and the baseline up front, so you judge it on your own numbers.
06 This is fashion-bred or grocery-bred like everyone else. It will not fit us.
That is the trap with every specialty tool. Increff, Toolio, Nextail, Syrup, and Onebeat are fashion only. RELEX is grocery deep. None runs a multi-category retailer on one platform. MEGA supports Fashion, Beauty, Eyewear, Supermarket, and Multi-brand on one engine, because it was built inside a multi-category retailer, not a single-vertical point tool. Depth varies by vertical; in the pilot we walk the same OTB-to-sell-through cycle through two of your verticals on one engine to confirm fit.
Phase 1, not a platform bet
Stop evaluating the pitch. Evaluate it on your data.
Pick the workflow that hurts most, usually Allocate or OTB. We run MEGA alongside your current process, on your data, in your cloud, on pre-negotiated terms. Day 30 you are configured. Day 90 you see modeled lift on the one KPI you chose, measured against your own baseline.
Day 30 configured · Day 90 modeled lift on your chosen KPI · measured on your own data