Next gen engine for unified retail intelligence and execution

Retail decisions that act on themselves.

Neurix unifies demand, inventory, pricing and store intelligence into one engine, then executes the transfers, markdowns and replenishments that follow. No dashboard hand-off. No lost week.

See the platform
SYSTEMS YOU ALREADY RUNDECISIONS IT RETURNSStore POSsales, footfall, basketE-commerce siteonline demand, returnsWarehouse managementcentral stock, dispatchInventory managementstock on hand, ageingMarketplaceschannel demand, priceSupply chain managementlead times, POs, in-transitOptimal Buy Quantityorder quantity by styleOptimal Allocation Timingtimed to cover and lead timeStore-Level Allocation Depthdepth per storeStore-Level Size Optimizationsize curve per storeOptimal Price & Markdownmarkdown depth and timingInventory Rebalancinginter-store transfers

94.2%forecast accuracy

Feeds in

  • Store POS
  • E-commerce site
  • Warehouse management
  • Inventory management
  • Marketplaces
  • Supply chain management

Decisions out

  • Optimal Buy Quantity
  • Optimal Allocation Timing
  • Store-Level Allocation Depth
  • Store-Level Size Optimization
  • Optimal Price & Markdown
  • Inventory Rebalancing
The Neurix platform

Insight is half the job.

Every retailer has dashboards. Very few have a system that closes the loop between what it knows and what actually happens in store. Six capabilities on one data model, and each one you add makes the others sharper.

Module 01 · Plan

Financial Planning

Turns a financial goal into a unit plan, moving units toward categories that sell and out of the ones that don’t, to lift overall sell-through and GMROI.

  • Target-to-Plan ConversionTranslate your business revenue goals into unit merchandise plans across stores, categories, sizes and month level.
  • Productivity-Led AllocationShift inventory away from slow-moving products and redirect it toward high-velocity products to maximize sales and inventory returns.
  • Dynamic Target ReallocationDynamically adjust product, category and store plans as actual performance unfolds, shifting future investment toward emerging opportunities.

Module 02 · Forecast

Demand Planning

Bottom-up demand forecasting at store × item × week, built from the lowest level up, so the numbers still hold wherever you aggregate them.

  • Store × Item × Week ForecastGranular demand forecast at store, item and weekly level.
  • Peak, Event & Seasonality CaptureAutomatically accounts for seasonal patterns, festivals, promotions and demand peaks.
  • New Item & Analog ForecastingForecasts new launches using comparable styles, attributes and historical analogs.
  • Dynamic Forecast ReconciliationContinuously adjusts forecasts using actual sales, recent trends, stock availability and forecast accuracy.

Module 03 · Flow

Inventory Management

An inventory model for every store, shaped by that store’s own characteristics, covering each path a unit can take to the shelf.

  • Store-Level Size & Depth OptimizationDefine the ideal size curve and inventory depth for every store based on local demand and selling patterns.
  • Warehouse-to-Store ReplenishmentIdentify store-level inventory gaps and determine what, where and when to replenish from the warehouse.
  • Store-to-Store Inventory RebalancingReallocate inventory from underperforming stores to stores with stronger demand, reducing the need for additional purchases.
  • Purchase Order Chase & CancellationIdentify purchase orders that need to be expedited, deferred or cancelled based on the latest demand and inventory position.

Module 04 · Watch

Watch Tower

A centralized inventory intelligence platform that provides end-to-end visibility into inventory health across the retail network, enabling data-driven decisions and recommending targeted corrective actions to improve stock productivity, availability, and overall business performance.

  • Identify excess inventory and overstock situations across stores and warehouses.
  • Detect dead stock, slow-moving inventory, and declining stock productivity.
  • Monitor stock-outs and track product availability across the retail network.
  • Identify opportunities for liquidation of aged and non-performing inventory.
  • Generate actionable recommendations for markdowns, inter-store transfers, replenishment, and liquidation to optimize inventory and maximize sales and margins.

Module 05 · Cluster

Store DNA & Clustering

Deep store profiles that inform every downstream decision and feed financial planning, so unit plans reflect how each store actually behaves.

  • Store DNA ProfilingBuild a unique profile for every store based on sales behaviour, customer preferences, price sensitivity, category mix, product affinity and inventory productivity.
  • Intelligent Store ClusteringAutomatically group stores with similar demand and shopping behaviour, going beyond traditional store grades or geographic clusters.
  • Customer Behaviour MappingIdentify whether a store’s customer base is value-driven, premium, trend-led, occasion-led, brand-driven or convenience-oriented.
  • Cluster-Specific Assortment StrategyRecommend differentiated assortment breadth, depth and size curves for each store cluster instead of applying a one-size-fits-all approach.

Module 06 · Price

Pricing & Promotion

Forecasts the right markdown for an upcoming event or for clearing ageing stock, then optimises depth and timing for revenue.

  • Event Markdown ForecastingIdentify the optimal markdown level for an event and forecast the expected sales and sell-through at that price.
  • Ageing Inventory & EOSS OptimizationIdentify ageing inventory and recommend the right liquidation timing and markdown strategy before inventory becomes a liability.
  • Markdown OptimizationIdentify the markdown strategy that delivers the optimal balance of sales and margin, helping achieve both top-line sales and margin targets.
Financial plan → unit plan
  1. 1

    Total budget

    Set your overall financial target for the business.

    Total budget
    ₹ 100 Cr
    Margin58%
    Growth+15%
  2. 2

    Disaggregation of budget

    Allocate the total budget across categories, channels and seasons.

    • Apparel45%
    • Accessories30%
    • Footwear25%
  3. 3

    AI model

    Consumes multiple inputs to optimise unit allocation.

    • Recency biastrends & seasonality
    • Historical performancesales, sell-through
    • Store insightslocation, cluster
    • Store DNAcustomer profile
    • ASPby category, store, month
    AI
  4. 4

    Unit plan

    Get the exact number of units for each style, store and month combination.

    StyleStoreMonthUnits
    ST-001Store AJan-261,200
    ST-001Store AFeb-26980
    ST-002Store BJan-262,450
    ST-003Store CMar-261,760
    Style × Store × Month= Unit plan
Forecast vs actual · units per week
ForecastActualProjectedAccuracynow2,0000W-3W-2W-1W+1W+2W+396.2%96.8%97.3%
Every route to the shelf
Inventory sourcesDestination storesInventory actions & operationsManufacturerdirect purchaseCentral warehousecompany owned / 3PLAnother storestore-to-store transferInbound · purchase / transferInventorymanagementTrack · Allocate · Move · ReplenishOutbound · allocationStore Adirect from warehouseStore Bfrom another storeStore Cfrom manufacturerReceiveAllocateTransferReplenishReturn
What the tower is watching
Too much, too slowAbout to run outSelling to plan or betterSales velocity →Weeks of cover →slowfasthighlowDead stockExcess stockAgeing inventorySlow moversSales & SOH on trackSuper itemsDepleting stockStock-out riskEach store × item sits in one state, with an action attached
Store clusters · 240 stores
Core Loyalists74 storesRepeat buyers on staple linesTrend Hunters56 storesFirst to new arrivalsPremium Shoppers45 storesFull-price led, higher basketValue Seekers41 storesPromotion and event drivenGen-Z / Hypebeast24 storesDrop-led, size-skewed
Markdown timing over an item’s life
LaunchPeakDeclineLife of the item →Markdown pointMarkdown given earlierDelayed EOSS markdownSell-through across the life 73% marked down in time vs 63% delayed
How it works

From raw sales file to shelf, in four moves.

Neurix sits on top of what you already run. No rip-and-replace, no data-warehouse project first.

Step 1

Connect

Point Neurix at your ERP, POS and stock feeds. Daily files work too.

Step 2

Unify

Sales, stock, price and store attributes resolve into one clean model.

Step 3

Decide

The engine forecasts demand and ranks every action by expected margin.

Step 4

Execute

Approved actions push straight back to your systems and get measured.

Explore the modules

Six integrated modules, from planning to pricing.

01 / 06

01Financial PlanningOpen moduleBudget by sell-thruSampleSample buy plan: last year LN204 sold 95% of its stock at Store 128, so its target rises 10%, from ₹1.2L to ₹1.32L. DS318 sold 60% at Store 321, so its target falls 10%, to ₹1.08L. Overall sell-through goes from 78% to 82% on the same budget. The ₹2.4L budget at an average selling price of ₹1,200 buys 200 units.
02Demand PlanningOpen moduleZX100 · Wk 43 forecastSampleSample forecast for style ZX100 in week 43: last year sold 4,000 units in 100 stores. Ten more stores add 400, the recent trend adds 440, the Diwali peak adds 1,210, stock-outs add back 350 and last year’s markdown takes out 350, for 6,050 units. Divided across 110 stores, that is 55 units per store, item and week.
03Inventory ManagementOpen moduleStore replenishmentSample12 units ship from the warehouse before Store C runs out.
04Watch TowerOpen moduleStyle ZX100 · Store 112SampleStyle ZX100 at Store 112 has 36 units but will sell only 16 in the 8 weeks left. Option 1: move 20 units to Store 120, where the same style is about to run out. Option 2: 30% off at Store 112 lifts sales from 2 to 4.5 a week and clears the stock within the season.
05Store DNA & ClusteringOpen moduleTwo clusters comparedSampleCluster 1 books less revenue, but earns more per sale and per sq ft.
06Pricing & PromotionOpen moduleMarkdown forecastSampleAt 20% off, 1,650 units sell for the highest margin of the four.

Drag the card, or pick a module below · the top card opens itHover a card to lift it · click it, or pick a module below, to open it

About us

Who’s building Neurix.

Our team brings together 9+ years of deep retail expertise and 5+ years of experience in building scalable technology products. With hands-on experience across merchandise, demand, assortment, financial, supply chain, pricing, and promotion planning, combined with expertise in full-stack engineering, system architecture, cloud, data, and AI, we bridge the gap between retail intelligence and technology. Having worked with industry leaders including Target, Landmark Group, and Accenture, we combine deep domain understanding with strong technical capabilities to transform complex retail challenges and data into intelligent, reliable, and high-impact commercial decisions.

Get started

See Neurix run on your own data.

Send one season of sales and stock. We’ll come back with a live forecast, a transfer plan and the margin it unlocks.

Contact us

Tell us where to reach you.

Leave your details and the Neurix team will follow up. No obligation, and nothing installed to find out what the engine would do with your data.

Please use your work address.

Fields marked * are required.