01
Seasonal markdown plan
Markdown optimization for one season's stock in one category.
Retail / Price Optimization
Price optimization software merchants will actually use
A price model that ignores your cost floors, MAP agreements or price ladder soon gets overridden by every merchant. Agnotic Technologies builds price and markdown optimization on your own sales history, with guardrails and approvals, so merchants can see why each price was recommended.
Elasticity
From your own sales
Guardrails
Floors, MAP and ladders
Markdowns
Planned by week and store
Trusted by global innovators
Industry Overview
What is retail price optimization software?
Retail price optimization software estimates how demand responds to price and recommends regular prices and markdowns that meet a retailer's goals within its pricing rules. Agnotic Technologies builds it on the retailer's own sales history, with guardrails such as cost floors and MAP, merchant approval workflows and controlled price tests.
Industry Challenges
Why do merchants override price recommendations?
Usually because the tool broke a rule the merchant knows and the model does not.
The model recommends a price below cost or below a supplier's MAP, and the merchant has to catch it.
Sizes of the same product end up out of order, so the larger pack costs less per unit than it should.
Prices change too often for shelf labels, store staff and customers.
Markdowns follow habit at fixed percentages, and aged stock stays on the floor.
A 'was' price shown with a markdown was never charged long enough to count as a genuine former price.
Nobody records which recommendations were accepted, changed or rejected, so the model never learns from merchants.
Guardrail checklist
Which price rules keep an optimizer safe?
We set each guardrail as a rule your pricing team owns, applied after the model and before any price reaches a store, site or label.
| Rule | Why it exists | Where it is enforced |
|---|---|---|
| Price floor from cost | Stops recommendations below landed cost plus your minimum margin | Rules service, before merchant review |
| Minimum advertised price (MAP) | Keeps advertised prices within the supplier MAP policies you have agreed to | Rules service and the channel that publishes prices |
| Maximum change per period | Limits how far and how often a price moves, for customers, labels and store labour | Rules service, with exceptions approved by a pricing manager |
| Price ladder consistency | Keeps good, better and best tiers and pack sizes in a sensible order | Rules service, checked across the whole product family |
| Competitor match limits | Caps how closely and how fast you follow a competitor, and never below your floor | Rules service, using competitor prices you collect lawfully |
| Former price evidence | A 'was' price must be a price you actually offered for a reasonable period | Price history service, before a markdown label is printed or published |
| Personal data pricing disclosure | Prices set by an algorithm using personal data may need a disclosure, as in New York | Checkout and display layer, by jurisdiction |
Our Approach
How is price elasticity estimated from your own sales?
From the price changes, promotions and markdowns already in your history, with everything else that changed accounted for.
Clean the history first
Stockouts, promotions, holidays and store openings are marked, so the model does not read an empty shelf as low demand.
Model at the right level
Where single items have too little price variation, elasticity is estimated by product family and store cluster, and the confidence is shown.
Show the uncertainty
Recommendations carry a range. Low-confidence items go to a price test instead of an automatic change.
Capabilities
What do our price optimization services cover?
We build retail pricing and markdowns. Hotel and venue dynamic pricing is on our revenue management page, and demand forecasting has its own retail page.
Elasticity models
Demand response to price by item, family and store cluster, refreshed as new sales arrive.
Guardrail rules service
Floors, MAP, change limits, ladders and competitor limits as rules your team edits.
Markdown optimizer
Timing and depth of markdowns by week and store cluster to clear stock by the exit date.
Regular price recommendations
Everyday price suggestions with the reason and the expected effect shown.
Price test manager
Test and control groups, test windows and readouts for test-and-learn pricing.
Merchant approval workbench
Accept, change or reject each recommendation, with a reason that feeds the next run.
Price history and evidence
Every price offered, where and for how long, kept as evidence for former price claims.
Publishing connectors
Approved prices sent to POS, ecommerce and electronic shelf labels.
How It Works
How does a markdown recommendation reach the shelf?
The path each markdown takes. Every step is logged against the product and store.
Stock and sales position
Aged and seasonal stock by store, with weeks left until the exit date.
01
1
Markdown plan
The optimizer proposes timing and depth per week and store cluster to clear stock by the exit date.
02
2
Guardrails
Floors, ladders and former price evidence are checked, and any blocked price is shown with its rule.
03
3
Merchant approval
The merchant accepts, changes or rejects the plan, and the reason is stored.
04
4
Publish
Approved prices go to POS, ecommerce and shelf labels on the agreed date.
05
5
Learn
Sell-through is compared with the plan, and the differences feed the next plan.
06
6
Engineering Stack
Data, models and apps for price optimization
Built on your data, in your cloud, with a workbench merchants use daily.
Data
Sales and returns by store and dayPrice and promotion historyInventory and costCompetitor prices collected lawfully
Models
Python with statistical and ML librariesHierarchical elasticity modelsMarkdown optimization solvers
Apps
Merchant workbench on the webRules servicePublishing to POS, ecommerce and shelf labels
Platform and security
Role-based access and SSOApproval audit trailCloud data warehouseEncryption in transit and at rest
Measured Impact
What should a pricing team track?
Measures we build into the pricing workbench.
Accepted
Recommendations accepted
Recommendations approved as proposed.
Overrides
Changed by merchants
Recommendations changed, with the reason given.
Sell-through
Seasonal stock cleared
Stock sold by the exit date against the plan.
Blocked
Guardrail hits
Prices stopped by a rule, by rule type.
Use Cases
Where do price optimization projects start?
First releases we scope, each one category or one season.
02
Regular price review
Everyday price recommendations for one category with guardrails.
03
Price ladder clean-up
Tiers and pack sizes put back in order across a product family.
04
Price test programme
Test-and-learn pricing for items the model is unsure about.
05
Guardrail service
Rules placed in front of an existing pricing tool before prices publish.
06
Price optimization for startups
A first pricing model and workbench for a growing direct-to-consumer brand.
Integrations
Which systems does price optimization software connect to?
Data comes in from sales, stock and cost systems; approved prices go out to every channel.
Sales and published prices
Point of sale
Online prices
Ecommerce platform
Cost and inventory
ERP
Seasons and exit dates
Merchandise planning
Store prices
Electronic shelf labels
Market prices
Competitor price data
Promotions
Promotion management
History and reporting
Data warehouse
What we check
How do we run a test-and-learn price experiment?
When the model is unsure, we test instead of guessing.
Design
- Test and control stores or regions matched on past sales
- One price change per product family per test
- Test length set before the test starts
Run
- Prices published only to the test locations
- Stockouts and promotions logged during the test
- Guardrails still apply to test prices
Read
- Units, sales and margin compared with control
- Results fed back into the elasticity estimates
- Decision recorded: roll out, adjust or stop
Compliance-First Development Services Backed by Global Standards
We build secure, scalable products designed for privacy, interoperability, and regulatory readiness from day one across every sector we serve.
General Data Protection Regulation
Implement lawful consent flows, data minimization, and secure processing for global data privacy.
Information Security Management
Adhering to the international gold standard for managing information security risks.
Our Edge
Why Global Leaders Choose Us
We combine deep technical expertise with industry-specific knowledge to deliver solutions that aren't just functional, but transformational.
Enterprise-Grade Security
We implement rigorous security protocols and compliance standards (HIPAA, GDPR, SOC2) across all industrial solutions to protect sensitive data.
High-Performance Scaling
Our architectures are built to handle massive data loads and user bases, keeping performance steady whether you're serving ten or ten million.
Accelerated Time-to-Market
Using our suite of internal tools and proven frameworks, we cut setup work so your first release reaches users sooner.
Embedded AI Integration
Beyond simple wrappers, we build deep-learning integrations and predictive analytics directly into the core of your industry-specific workflows.
Engagement Model
How does a price optimization engagement run?
We start with one category and its sales history. We agree a fixed first release on the scoping call.
01
Scoping call
Categories, channels, pricing rules and the systems in place today.
02
Data audit
Sales, price, promotion, stock and cost history checked for gaps.
03
Fixed-scope first release
One category with guardrails, recommendations and the approval workbench.
04
Expand
More categories, markdowns and price tests on the same model.
05
Support
Model refreshes and rule changes under agreed support.
Facts and sources
Which pricing rules shape price optimization software?
Short facts we design around, each linked to its primary source. Your counsel confirms how they apply to your channels.
Under the federal Guides Against Deceptive Pricing, a reduction from a former price is a legitimate comparison only if the former price is the actual, bona fide price at which the article was offered to the public on a regular basis for a reasonably substantial period of time.
Source: 16 CFR 233.1(a) (eCFR)A former price is not fictitious merely because no sales were made at it, but the article should have been openly and actively offered at that price for a reasonably substantial period, in the recent, regular course of business, honestly and in good faith.
Source: 16 CFR 233.1(b) (eCFR)When a retailer advertises that it sells below the prices charged in its trade area, the higher comparison price should not appreciably exceed the price at which substantial sales of the article are being made in that area.
Source: 16 CFR 233.2(a) (eCFR)New York's Algorithmic Pricing Disclosure Act took effect on 10 November 2025 and requires most companies that use algorithmic pricing based on consumers' personal data to clearly display a disclosure saying so.
Source: Office of the New York State Attorney General, consumer alert (November 2025)
Voice of success
We don't just build products; we forge lasting partnerships. See how we've helped industry leaders transform their vision into technical reality.
“I can clearly see how Agnotic has a unique way of handling end-to-end development. They are always active on quick chat and provide support quickly.”
Aaron Phelan
Founder, Benchmark
“Agnotic combines deep technical expertise with strong domain knowledge. They understand the business context, anticipate challenges, and make collaboration smooth and effective.”
John Pasmore
Founder, Latimer
“Agnotic is the best technical team we evaluated. Their engineering excellence made our work dramatically easier and allowed us to stay focused on what matters most for maternal care outcomes. They took full ownership of the technical execution, and we are always happy to continue working together.”
Kim Smith
Founder, My Lauren
Related pages
Related retail software services
This page covers retail prices and markdowns. These pages cover forecasting, analytics and other pricing models.
Revenue management software
Dynamic pricing for hotels, venues and perishable inventory.
Retail analytics software
Sales, promotion and pricing analysis after the fact.
AI-powered demand forecasting engine
Forecasts that feed markdown and replenishment plans.
Retail software development
Our wider retail and ecommerce practice.
More in Retail
More Retail Solutions
Explore other production-grade engineering platforms we deliver across retail.
Cross-Industry Reach
The Same Engineering Patterns, Across Other Industries
The technical patterns behind this platform translate naturally into adjacent verticals.
Frequently Asked Questions
Dynamic pricing changes prices often in response to live demand, as hotels and venues do. Retail price optimization sets regular prices and markdowns from elasticity, within guardrails, and changes them on a cadence stores and customers can follow.
It can be when competitors pool nonpublic pricing data through a shared tool or algorithm. We build models on your own data and lawfully collected public prices, never pool data between clients, and leave the policy decision to your counsel.
The number of categories and channels, the state of your price and promotion history, the guardrails to configure, the systems that publish prices and whether markdowns and price tests are in scope. A data audit of one category comes before any estimate.
In New York, most companies that set prices by algorithm using personal data must display a disclosure, and the legislature passed a further surveillance pricing bill in 2026. We build disclosures into the display layer per jurisdiction; your counsel confirms the current position and where it applies.
Guardrails run as rules after the model and before merchant review, so a recommendation that breaks MAP, a floor or a ladder is blocked with the rule shown, and never reaches a shelf.
Yes. A growing brand often has short history, so we start with guardrails, simple elasticity by product family and price tests, and add markdown optimization as data builds up.
Industries