E-commerce
August 27, 2026
Wondering how to secure your margins and automate negotiations on a complex B2B catalog? Manual price management in distribution quickly becomes unsustainable in the face of cost volatility and revenue erosion from uncontrolled discounts.
Zilliant's artificial intelligence transforms this chaos into a governed strategy, generating precise recommendations and enforcing approval rules directly within your ERP and CPQ systems.
This is not just a pricing tool for wholesale, but a governance platform designed for organizations managing thousands of negotiated agreements and hundreds of thousands of SKUs with dedicated teams.
So how is artificial intelligence changing the game for your distribution? On the agenda:
Why do B2B distributors lose millions in unmonitored margin?
How does AI generate personalized prices without exhausting your sales teams?
What is the role of governance in preventing pricing deviations?
How to integrate Zilliant with your ERP and CPQ for seamless execution?
Does this fit your revenue structure and current processes?
Let's get started.
Summary
Why does manual B2B price management fail at scale?
The challenge of heterogeneous catalogs and agreements
In industrial distribution and wholesale, complexity is king. Manufacturers and distributors often manage thousands of items over years, with each customer having their own negotiated contracts, discount volumes, and specific lead times.
Managing this manually, via Excel files or scattered dashboards, becomes a bottleneck. An entry error, a forgotten update after an increase in raw material costs, or a poorly calibrated negotiation can swallow entire margins.
Sales teams often find themselves having to play accountant, spending their time verifying the viability of deals rather than selling. This reactive approach does not allow them to see the overall impact on revenue or profitability by customer segment.
The silent erosion of revenue
The real danger lies in what is known as margin leakage. This refers to discounts granted informally, beyond established policies, or prices applied too late in the face of inflation.
Without dedicated tools to track every transaction and compare it to defined profitability thresholds, these losses remain invisible until it is too late. They accumulate on the small details: a volume product, an undocumented third-party discount, or an expired agreement that continues to be applied.
For companies worth several hundred million dollars, this inefficiency is measured in millions of euros per year. The solution is not just to lower prices, but to understand exactly where and why the margin is compromised for each customer type or product family.
This is where the traditional approach shows its limits when faced with the need for rigorous, real-time governance over massive product portfolios.
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How does AI generate dynamic personalized pricing?
Machine learning at the service of strategy
Zilliant uses machine learning to analyze decades of historical data and compare them to current market conditions. The algorithm does not simply follow a fixed rule; it learns how demand, costs, and competition influence profitability at any given moment.
Unlike static methods that apply the same margin percentage to all products, Zilliant segments the catalog. It identifies which customers are price-sensitive, which ones buy in volume, and which products have different elasticity depending on the region.
This granularity allows for the generation of price recommendations that are not simple mathematical adjustments, but optimized business strategies. The AI suggests the best price to maximize margin while maintaining market share.
Responsiveness to market volatility
In a context where raw material costs or customs duties fluctuate rapidly, responsiveness is crucial. Zilliant can simulate the impact of a cost increase across all agreements in seconds.
It immediately identifies which contracts are at risk and recommends the necessary adjustments to restore profitability without sacrificing strategic customer relationships. This transforms a process that previously took weeks into a task of days, or even hours.
Pricing analysts can thus focus on exceptions and complex strategies rather than manual data entry across hundreds of files.
This agility is essential for distributors operating internationally or in industries with volatile costs, ensuring that every negotiation starts from a solid, up-to-date foundation.
What is the role of governance in preventing deviations?
Mastering Application Gaps
Artificial intelligence is useless if it is not framed by strict governance. Zilliant places the control of modification rights at the heart of its operation to prevent deviations.
The platform clearly defines who has the right to apply a deviation. A field sales representative cannot accept just any price without validation. The system strictly controls authorization thresholds and approval chains.
Complete Traceability of Negotiations
Every price adjustment is traceable. The finance or revenue operations team can see precisely who made a discount, why, and whether it complies with company policies.
This eliminates the culture of the automatic "yes" in field negotiations. Exceptions must be justified and validated by an authorized approver according to defined criteria (customer level, minimum margin, purchasing volume).
This transparency allows for the quick correction of deviant behaviors and reduces margin leaks caused by informal or undocumented agreements.
Centralized Control for Distribution
For companies managing hundreds of thousands of SKUs, this level of control is vital. It ensures that the strategy defined at headquarters is applied uniformly across the entire sales network, from the senior representative to the junior negotiator.
Governance is not a hindrance to sales speed, but a safeguard that ensures every transaction respects the financial objectives set for the company. This allows sales teams to negotiate with confidence, knowing they are guided by intelligent rules.
How does Zilliant integrate with your ERP and CPQ stack?
A Native Connection with Execution Systems
Zilliant’s efficiency lies in its ability to act directly within the environment where sales are closed. It does not operate in a silo but connects with companies' ERPs and CPQs (Configure, Price, Quote).
Once a price recommendation is generated or validated, it is instantly propagated to the transactional system. Sales representatives no longer need to consult an external spreadsheet to find the correct price; they see the smart proposal directly within their daily work tool.
Bi-directional Data Flows
The integration is seamless and bi-directional. Actual transaction data flows back to Zilliant to refine the machine learning models, creating a virtuous loop of continuous improvement.
This means the system learns from every closed sale, adjusting its future recommendations based on real successes and failures. Price changes thus become immediate operational actions rather than simple post-event analyses.
Distributors using SAP, Oracle, or other major ERPs can deploy this solution without a complete overhaul of their infrastructure, as Zilliant layers over their existing systems to manage pricing complexity.
Elimination of Repetitive Manual Tasks
The automation of data flows eliminates copy-paste errors and delays in applying new pricing structures. Updating a selling price or a contractual condition is instantaneous for all affected channels.
This operational fluidity allows teams to focus on strategy rather than administrative execution, accelerating the sales cycle while improving transaction accuracy.
How to quantify and reduce margin leakage using AI?
Granular variance analysis
Zilliant provides unprecedented visibility into margin leaks. The platform analyzes every past transaction to identify deviations from established pricing strategies.
It segments these variances by customer account, product, or sales region. This makes it possible to see exactly which product lines or which customers are the root cause of profitability erosion.
Performance dashboards
Detailed reports highlight discount "abuses," meaning agreements where the discounts granted systematically exceed what is justified by volume or margin.
These insights allow pricing analysts to target their efforts on the most problematic accounts. By redefining approval thresholds for these customers or renegotiating contracts, the company can recover significant margin points.
Simulation and prevention
Beyond detection, the tool allows you to simulate the impact of new pricing policies. Even before implementing a restrictive strategy, teams can see how many margin leaks would be avoided.
This predictive capability transforms pricing management from a corrective function to a strategic and proactive one, ensuring that every decision contributes directly to the financial health of the company.
What are the ideal scenarios for adopting this solution?
Industrial Enterprises and Large-Scale Distributors
Zilliant is particularly well-suited for industrial manufacturers, national distributors, and wholesalers with significant revenue, often exceeding $500 million.
These organizations typically have dedicated pricing and financial operations teams, along with a budget for integration with their ERP systems. They manage complex catalogs with thousands of SKUs and a long tail of slow-moving but critical products.
Multi-Channel Environments and Negotiated Contracts
If your model relies on negotiated agreements, customized pricing for each client, or complex volume-based terms, this solution is ideal.
It excels in environments where sales are made through sales representatives using a quoting tool (CPQ) and where the price is not publicly displayed but privately negotiated.
Governance as a Strategic Priority
Companies where margin control is a concern of the board of directors or financial leadership will find an ideal partner here.
The ability to audit, control, and optimize every transaction in real-time meets the rigorous demands of these large groups that cannot afford uncertainty or revenue leakage.
Why is a simple DTC brand not the right audience?
Excessive complexity for simple catalogs
A DTC (Direct-to-Consumer) brand or a B2C retailer typically does not need a platform of this scale. Manual price management, often via basic rules or standard Shopify plugins, is sufficient for standard catalogs.
Zilliant is designed for the complexity of B2B supply chains and custom agreements, making it too heavy and expensive for simple online retail with fixed or slightly dynamic pricing.
Lack of a dedicated pricing team
Smaller B2B companies without dedicated pricing analysts and an ERP integration budget will not be able to leverage Zilliant's advanced features.
Implementation requires significant technical and functional expertise. Without this foundation, the tool risks becoming an expensive, underutilized investment with no tangible ROI.
Focus on competitive responsiveness rather than strategy
B2C distributors or small retailers often look to react instantly to competitor prices (public dynamic repricing). Zilliant does not target this market; it focuses on internal profitability, contract negotiation, and long-term margin management.
For these players, lighter solutions or those focused on unit selling price are often more suitable than this complex ecosystem.
How does Zilliant complement the Qstomy offering for distribution?
Optimizing the B2B Customer Journey
While Zilliant ensures the profitability of your transactions in the background, Qstomy focuses on the customer experience and conversion. For distributors using Shopify for their B2B orders, the alignment between negotiated prices in Zilliant and the front-end display is crucial.
Qstomy helps customize the online sales interface, ensuring that each customer sees the correct pricing and specific offers related to their contracts managed by Zilliant. This guarantees total consistency between negotiation and purchase.
Automation of Support and Pricing Inquiries
B2B customers often ask complex questions about their accounts, payment terms, or discount details. Qstomy allows you to create intelligent conversational agents that can answer these inquiries based on customer data.
This reduces the workload of customer service teams and allows sales representatives to focus on complex sales, while reassuring customers about their contractual information.
Integration of SEO and Content Advice
A good B2B strategy is not just about pricing. By integrating support answers into a useful e-commerce SEO strategy, you can attract more qualified buyers.
Qstomy facilitates the creation of editorial content that meets the real needs of B2B buyers, enhanced by artificial intelligence to generate relevant and personalized responses based on the profile of the user visiting your site.
How is AI transforming B2B lead qualification?
Preparation for the Sale
Even before negotiation takes place, AI can help qualify prospects. A B2B chatbot based on artificial intelligence can query leads to understand their expected volumes, budget constraints, and specific needs.
This data then feeds into Zilliant's pricing engine, allowing a pre-qualified offer to be proposed from the very first contact. This accelerates the sales cycle and enables sales teams to step in with proposals already aligned with profitability.
Automatic Segmentation
AI identifies the leads most likely to become high-value customers. By cross-referencing interaction data with pricing models, it can suggest which pricing strategies to test to convert the prospect.
This data-driven approach avoids wasting time on unprofitable opportunities and allows focus on deals that maximize margin and volume.
What is the difference between an AI agent and a chatbot for B2B?
Functional Distinction
It is crucial to distinguish a simple chatbot from a complex AI agent in the context of B2B commerce. A chatbot can answer frequently asked questions or guide the user, whereas an AI agent can make autonomous decisions based on real-time data.
In your strategy, using an agent capable of managing complex scenarios, such as stock verification, validation of negotiated pricing, and quote generation, transforms the user experience.
Synergy with Zilliant
An AI agent can act as the bridge between the website and the pricing engine. When a customer requests a specific rate, the agent queries the ERP or the Zilliant database to check if this request is valid according to the governance rules.
This allows for an immediate and personalized response, while ensuring that the transaction complies with internal policies. It is the fusion of the speed of automatic dialogue with the rigor of price control.
How does Qstomy optimize the customer experience for Shopify stores?
Order Tracking and Proactive Customer Support
At Qstomy, we believe that parcel tracking management is as important as the sale itself. For B2B clients, who often place large recurring orders, transparent tracking builds trust.
Qstomy integrates tracking data directly into the conversational flow. Customers can find out where their order is at any time without needing to contact support, thus reducing unnecessary inquiries and improving satisfaction.
Account and Policy Management
The customer interface must reflect the complexity of B2B. Qstomy allows the configuration of personalized customer portals where users can access their negotiated prices, order history, and invoices.
This centralization is essential for effective long-term account management, particularly when fueled by Zilliant's pricing recommendations.
Conversion and Average Cart Value
By automating the buying journey and providing the right information at the right time, Qstomy helps reduce cart abandonment. B2B customers with questions about their payment terms or delivery times can be reassured instantly.
The goal is to transform every interaction into an additional sales opportunity, while maintaining the profitability defined by advanced pricing strategies.
What checklist should be applied before deploying Zilliant?
Data and Integration Audit
Before starting, ensure that your product and customer data are clean and structured. The efficiency of Zilliant depends on the quality of information regarding costs, historical pricing, and active contracts.
Also, verify the robustness of your connections with the ERP and CPQ. A fragile integration can compromise price updates and harm the customer experience.
Clear Definition of Governance Rules
You must have clear approval rules for each discount level. Define who can validate which deviation and what critical thresholds must not be exceeded.
This ensures that the AI will correctly apply your policies without requiring constant human intervention for every standard transaction.
Sales Team Training
Representatives must understand how to use the recommendation tools and accept the new validation processes. Adequate training is essential for rapid and effective adoption.
To go further: Integrating Customer Service Responses into an E-commerce SEO Strategy Useful to Customers - Qstomy, AI Chatbot to Qualify B2B Leads on Shopify Without Slowing Down the Sale - Qstomy, Customer Support for Paper Catalogs Linked to an Online Store - Qstomy, Training an E-commerce Chatbot with Shopify: Using the Right Data Without Creating Bad Answers - Qstomy, AI Agent, Chatbot, or Shopping Assistant: What is the Difference for an E-commerce Store? - Qstomy, AI Chatbot vs Live Chat: Which to Choose for an E-commerce Store? - Qstomy, B2B E-commerce Customer Support: Quotes, Accounts, Negotiated Prices, and Recurring Orders - Qstomy.
Enzo
August 27, 2026
