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AI dynamic pricing agent
Custom AI dynamic pricing agent for ecommerce and marketplaces
We build a pricing agent around your catalogue, margin rules, approval process and sales channels, so your team can change the right prices faster without losing control.
Capabilities
What our dynamic pricing agent does
Each custom dynamic pricing agent is built around your catalogue, channels, margin rules and approval policy. It covers the full pricing loop: competitor price monitoring, ecommerce price optimisation, approval, publishing and measurement. If your pricing logic also touches ERP, CRM or fulfilment data, we connect those systems through our AI integration service.
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Competitor price monitoring
Reads competitor prices, product availability and promotions across the channels that matter to your category.
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Demand and price response modelling
Estimates how sales, conversion and margin may respond when price changes by SKU, channel, region or campaign window.
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Objective-based price optimisation
Optimises for the objective you choose, such as profit, revenue, sell-through, stock clearance or marketplace position.
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Inventory-aware repricing
Uses stock levels and replenishment rules so prices reflect what you need to clear, protect or prioritise.
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Price checks before publishing
Tests a proposed change against your objective and pricing limits before the price reaches the store or marketplace.
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Pricing limits and approvals
Applies margin floors, ceilings, vendor rules, change caps and approval steps before a price can be published.
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Channel publishing and rollback
Publishes approved prices to each channel, checks the result and rolls back changes when a feed or integration returns an error.
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Audit trail and learning loop
Logs why a price changed, measures the result and feeds that result back into the next recommendation.
Pricing stack
How your custom AI dynamic pricing agent connects to your ecommerce stack
The agent fits into the systems where pricing decisions already happen. It reads current data, recommends the next price and publishes changes only when they are approved or within your rules.
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Store and marketplace feeds
Connect Shopify, WooCommerce, Amazon, eBay, PIM, ERP, catalogue feeds, stock feeds and marketplace price data.
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Demand and price response signals
Use sales history, traffic, conversion rate, promotion calendars, seasonality and category behaviour to estimate how customers may react to a price change.
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Margin and pricing rules
Respect cost, fulfilment fees, marketplace commission, price floors, price ceilings, vendor rules and maximum change limits.
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Approval and publishing
Run in recommendation mode first, send sensitive changes to humans, then publish approved prices back to your store or marketplace with an audit trail.
Why Makeitfuture
Why build your dynamic pricing agent with Makeitfuture?
Makeitfuture was recognised as Make.com AI Partner of the Year, so your pricing agent is built by a team trusted for production AI automation.
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500+
Clients
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15000+
Automations
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7+
Years of experience
Use cases
Where does AI dynamic pricing create value first?
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Competitor-aware marketplace repricing
Protect the buy box on Amazon, eBay and marketplace channels without blindly chasing the lowest seller.
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Shopify and WooCommerce catalogue pricing
Update thousands of SKUs on your owned store using stock, demand, margin and promotion context.
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Inventory clearance and markdowns
Move slow or seasonal stock earlier, while keeping high-demand and low-stock products away from unnecessary discounts.
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Peak demand and campaign pricing
React to Black Friday, flash sales, seasonality and campaign traffic without building a spreadsheet war room.
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Margin recovery on high-value SKUs
Find products where demand can tolerate a higher price and recover profit without increasing ad spend.
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Channel and region price governance
Keep pricing consistent across markets, regions and channels while allowing controlled local variation where it makes sense.
Workflow
How does the pricing agent decide what to change?
We start with recommendation mode, prove your custom pricing policy on a focused SKU set, then automate only the decisions that stay inside your commercial rules.
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Read live signals
The agent reads sales history, live stock, traffic, competitor prices, promotions, fees, costs and channel rules from your systems and market feeds.
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Forecast the likely response
The agent estimates how demand, conversion, revenue and margin may react to price changes, using your past performance as the starting point.
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Recommend or update with approval rules
The agent recommends a price or publishes it automatically, depending on the pricing limits, approval policy and risk level you set.
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Measure and improve
Every price change is logged, compared against the expected result and used to improve future recommendations.
Why weekly pricing cannot keep up with live ecommerce signals
Ecommerce prices now depend on signals that change by the hour: competitor moves, stock levels, demand spikes, marketplace fees, promotions, margin floors and channel rules. When pricing stays in spreadsheets, teams react late, discount too far or miss margin that was already available.
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Competitor prices move before your team sees them
A daily or weekly review catches yesterday's market. The price that wins at 9am can be wrong by lunch.
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Inventory changes faster than price lists
Slow stock keeps occupying cash while scarce winners stay underpriced. Pricing and stock need to move together.
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Promotions distort demand signals
Flash sales, coupons and seasonal campaigns can make demand look stronger or weaker than it really is unless the model reads the context.
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Marketplace fees create hidden margin leaks
Commission, fulfilment, shipping and channel costs can turn a winning revenue line into a weak profit line.
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Approval delays cost the best timing
A good price recommendation loses value if it waits two days for a spreadsheet review and another day for upload.
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Static rules can start price wars
Rules like match the cheapest seller may protect volume for a moment, but they can pull the whole category into margin erosion.
More agents
Explore the rest of the agent line-up
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GEO Optimization Agent
Get your brand cited inside AI answers.
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Product Enhancement Agent
Product data enrichment at catalogue scale.
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Website Growth Agent
Maintain, improve and grow your site on autopilot.
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Quotation Agent
Automated quoting from any inbound request.
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AI SDR Agent
A custom AI SDR that books meetings, not spam.
Manual pricing vs rule-based repricing vs AI dynamic pricing
The difference is how much context the pricing decision can use before a price changes.
| What matters | Manual pricing | Rule-based repricer | AI dynamic pricing agent |
|---|---|---|---|
| Signal coverage | Mostly sales reports, spreadsheets and ad hoc competitor checks. | Competitor prices and a small number of fixed conditions. | Competitor prices, demand, stock, margin, promotions, fees and channel rules. |
| Decision logic | Human judgement, often applied too late across too many SKUs. | Fixed instructions such as match, undercut or hold a floor. | Goal-driven recommendations that balance profit, revenue, stock and risk. |
| Stock and margin awareness | Usually checked separately, often after prices are already live. | Basic floors can exist, but stock context is limited. | Inventory, cost and margin rules are part of the pricing decision itself. |
| Governance | Depends on people remembering every rule and exception. | Rules are enforced, but exceptions can multiply quickly. | Pricing limits, approvals and audit trails are designed into the workflow. |
| Best fit | Small catalogues or low-change categories. | Commodity SKUs where competitor price is the main signal. | Large catalogues, marketplaces, complex costs and margin-sensitive categories. |
| Learning from results | Manual review after the fact. | Usually fixed until someone edits the rule. | Measures price outcomes and improves the next recommendation. |
Safety and control
How do you avoid price wars and unfair pricing?
A pricing agent should not chase every competitor move. We design it to follow clear business rules: protect margin floors, respect vendor pricing rules, limit the size and frequency of changes, keep an audit trail and require human approval for sensitive categories.
Customer-level personalised pricing is off by default unless your legal, privacy and commercial teams explicitly approve it.
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Margin floors and price ceilings
Hard boundaries stop the model from choosing prices the business cannot defend.
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Vendor, marketplace and channel rules
The agent can respect vendor rules, marketplace policies and country-specific constraints before publishing.
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Price change caps
Limit how far and how often a price can move so customers do not see erratic behaviour.
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Human approval for sensitive changes
High-value SKUs, regulated products or unusual recommendations can wait for a manager.
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Audit trail for every decision
Store the signals, rule checks and approval path behind each price update.
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No personal pricing by default
Optimise product and channel prices first. Avoid customer-level pricing unless there is a clear legal and commercial reason.
AI dynamic pricing agent FAQs
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What is an AI dynamic pricing agent?
An AI dynamic pricing agent connects live market signals with your pricing rules. It can recommend or update prices based on competitor prices, demand, stock, margin floors, marketplace fees and approval policy.
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Do you build a custom dynamic pricing agent or configure existing software?
We build a custom AI dynamic pricing agent around your catalogue, channels, margin rules and approval process. If an existing repricing tool already fits part of the job, we can integrate it, but the decision logic and workflow are designed around how your business prices products.
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How is AI dynamic pricing different from repricing software?
Traditional repricing software usually follows fixed rules, such as matching or undercutting a competitor. AI dynamic pricing uses more context, including demand, stock, margin, promotions and historical results, then optimises toward a business objective.
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Does it work with Shopify, WooCommerce, Amazon and eBay?
Yes. We can connect the agent to Shopify, WooCommerce, Amazon, eBay, PIM, ERP and marketplace feeds through integrations or structured exports. The exact integration plan depends on where your prices, stock, costs and approval rules live today.
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What data do we need before we can use it?
The useful starting set is SKU data, price history, sales history, stock, cost, margin rules, promotion calendars and competitor or marketplace pricing data. We can start with imperfect data, but the first phase normally includes cleaning the fields that drive pricing decisions.
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Can a human approve price changes before they go live?
Yes. Most teams start in recommendation mode. The agent proposes changes, shows the reason and waits for approval. Once the policy proves itself, low-risk categories can move to automatic updates while sensitive changes still require approval.
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How do you avoid price wars?
We do not build agents that simply chase the lowest competitor. The workflow uses margin floors, price ceilings, change caps, marketplace rules, stock context and human approval for sensitive moves. That keeps the agent focused on profit and sell-through, not blind undercutting.
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Is dynamic pricing legal and fair?
Dynamic pricing is common, but the design matters. We build around transparent business rules, audit trails, no customer-level personalised pricing by default and legal review where pricing touches regulated categories, consumer protection rules or sensitive markets.
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What kind of uplift is realistic?
It depends on category, margin, stock pressure, competitor intensity and pricing freedom. The practical way to answer is a controlled pilot: choose a SKU set, compare it with your current policy and decide whether the measured result is worth scaling.
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How long does implementation take?
A focused pilot usually starts with discovery, data mapping and recommendation mode. More complex builds take longer when ERP, PIM, marketplace approvals, regional pricing or compliance rules need to be connected. We scope that before build work starts.
