Bukunmi Adegbonmire

Apparel brands make their most expensive decisions — what to reorder, mark down, or stop buying — using fragmented and incomplete data.

Shopify shows sales and inventory. Supplier invoices hold product costs. Freight, duties, and FX change the real cost after a purchase order is placed. Returns reveal fit problems by size. Ad platforms optimize for sales, not margin.

As a result, a style can look profitable while specific sizes quietly lose money, sell out too early, or become dead inventory that must be heavily discounted. Founders and operators end up reconciling spreadsheets and making inventory bets without a clear view of the cash and margin consequences.

The core problem: apparel brands lack a trusted system for deciding where to put their next inventory dollar.

Revnce is an apparel landed-margin decision platform for Shopify-first brands.

It is designed to become the source of truth for the economics of every style, color, and size, connecting product cost, purchase orders, freight, duties, inventory, sales, discounts, returns, and eventually advertising spend.

Rather than trying to replace Shopify, accounting software, a WMS, or a full ERP, Revnce sits above those systems as the decision layer.

The initial wedge is deliberately narrow: help apparel brands make better reorders at the size-color variant level.

From there, the same data foundation can expand into markdown decisions, returns and fit intelligence, cash-aware open-to-buy planning, supplier performance, and approved execution workflows.

Revnce turns fragmented commerce data into a clear weekly action plan. For each style, color, and size, it can show:

• True landed cost. Factory cost plus freight, duty, brokerage, FX, and other inbound costs.
• Return-adjusted contribution margin. What the brand actually keeps after discounts, returns, fulfillment, fees, and relevant acquisition costs.
• Variant-level inventory health. Which sizes are selling, stocked out, slow-moving, or likely to become stranded inventory.
• Margin-safe reorder decisions. Whether to reorder, hold, or stop buying — including recommended units by size, cash required, lead time, MOQ constraints, expected margin, and markdown exposure.
• Markdown decisions. Which variants should remain full price, receive a light discount, be cleared, bundled, or liquidated, based on likely cash recovery.
• Returns-to-buying intelligence. Which sizes or products are creating fit-related returns, and how that should change future size curves and buying decisions.
• Explainable recommendations. Every recommendation shows the underlying assumptions, data source, timestamp, and financial trade-off.

Nigerian businesses make high-stakes technology and operational decisions using evidence that was produced somewhere else, under conditions that do not match reality.

A vendor demo runs on reliable infrastructure. A benchmark assumes clean connectivity. A compliance product may work in another market but fail under local network interruptions, power instability, device constraints, traffic patterns, data rules, or regulator expectations.

The consequence is expensive:

• Buyers commit money before knowing whether a system will work in their real environment.
• Vendors struggle to prove that their product is ready for Nigeria.
• Compliance, security, and operational failures are discovered after launch, when changing course is costly.
• Smaller regulated institutions carry the same obligations as larger ones, but do not have the people or resources to test properly.

There is no trusted, independent place where a business can test a decision under the conditions it must actually survive — before committing to it.

Lemuel is a decision-evidence lab for Nigeria.

It starts as a controlled proving environment where businesses and technology vendors can test critical systems before deployment.

The first focus is narrow:

• Cybersecurity and fraud-monitoring systems.
• E-invoicing and regulated integrations.
• Payment, banking, and API resilience.
• Technology decisions subject to regulatory, operational, or commercial risk.

Over time, the same evidence discipline can expand beyond systems into commercial and market decisions, helping Nigerian and African businesses understand how customers, operators, or affected groups may respond to a change before it is launched.

Lemuel takes a decision a business is about to make and tests it before the commitment is made. For example:

• A bank wants to purchase a fraud-monitoring platform.
• A fintech needs to prove that a new payment integration will hold up.
• A large business is preparing for e-invoicing compliance.
• A software vendor needs credible local proof that its product works in Nigeria.

Lemuel recreates the operating conditions that matter, such as:

• Unreliable or variable network conditions.
• Latency, packet loss, bandwidth limits, and mobile-network behavior.
• Power interruptions and recovery events.
• Real device constraints.
• Nigerian traffic patterns, including peak loads and retries.
• Relevant regulatory or compliance requirements.

Then Lemuel runs the system, documents what happened, and produces an independent evidence report showing what worked, what failed, under which conditions it failed, who is affected, what breaks first, and whether the result can be reproduced and defended to a board, regulator, or internal risk team.

The result is not a sales recommendation or a generic consulting deck. It is evidence that lets a business make a more defensible decision — before the cost of being wrong becomes real.