How to prepare your B2B catalog to sell through AI assistants

The conversational commerce revolution has reached a turning point. Since OpenAI introduced Instant Checkout on September 29, 2025, product purchases have migrated from traditional web experiences directly into conversations with AI assistants. But while the consumer world captures headlines, the truly disruptive impact will manifest in B2B—where transactions are more complex, margins higher, and purchase intent more qualified.

The new paradigm: buying through conversation

The ecosystem is rapidly converging toward a model where AI assistants don't just respond, but execute complete transactions on behalf of users. OpenAI has developed the Agentic Commerce Protocol together with Stripe, enabling over 700 million weekly ChatGPT users to complete purchases without ever leaving the conversational interface. Google responded with the Agent Payments Protocol, leveraging its infrastructure of 50 billion listings in the Shopping Graph, updated at a rate of 2 billion listings per hour.

Market reaction confirms the scope of change: when OpenAI announced integration with Etsy, the platform's stock jumped 16% in a single day. Investors immediately understood that access to hundreds of millions of active users represents a distribution channel comparable—if not superior—to traditional search engines.

AI traffic conversion rates surpass all traditional channels

Early performance data reveals that traffic from ChatGPT converts at 11.4%, nearly double direct visits at 10.2% and decisively superior to:

• Paid search: 9.3%
• Organic search: 5.3%
• Email marketing: 4.6%
• Social media: 3.8%

Year-over-year growth from June 2024 to June 2025 shows a 45% increase in conversion rates, with particularly sharp acceleration from March 2025 onward.

These numbers demonstrate that AI search isn't simply another channel, but a high-intent channel that systematically outperforms every traditional discovery method. The question is no longer whether AI will revolutionize commerce, but how quickly and how deeply it will redesign the entire value chain.

The B2B-specific challenge

In the B2B context, complexity amplifies exponentially. AI assistants don't navigate catalogs like humans do. They formulate precise requests that include specific SKUs, detailed variants, price levels tied to commercial contracts, delivery times, logistics constraints, and payment terms—then attempt to complete the order entirely programmatically.

Consider a typical request: "Reorder 1,000 units of component XYZ in chrome finish if the price drops below €45, with delivery to the Milan distribution center, invoicing on 30-day net terms according to the active framework contract". An AI assistant must be able to:

• Uniquely identify the product and correct variant
• Verify the specific contractual price for that customer
• Check availability in real time
• Calculate delivery times for that destination
• Apply agreed payment terms
• Complete the transaction while respecting all company policies

If the catalog isn't structured with rich metadata—complete Schema.org Product/Offer, variant-level attributes, GTIN codes, UoM, MOQ—the agent simply won't be able to map intent to actually purchasable inventory. And if price and availability feeds aren't updated in real time, the supplier risks not appearing in results at all.

The standards war

The market is witnessing a battle to define the protocols that will govern agentic commerce. OpenAI has chosen to open-source the Agentic Commerce Protocol, aiming to establish itself as the de facto standard for the entire ecosystem. Integration for merchants already using Stripe requires, literally, a single line of code.

Google launched the Agent Payments Protocol two weeks earlier, already supported by over 60 merchants and financial institutions, with PayPal announcing a multi-year partnership to promote its adoption. Visa has also entered the arena with "Intelligent Commerce," in collaboration with Anthropic, Microsoft, Mistral AI, OpenAI, and Perplexity.

This fragmentation creates a concrete challenge for merchants: the need to potentially support multiple protocols simultaneously, analogous to how they currently support different payment processors today. The solution isn't choosing sides, but maintaining protocol optionality.

Rewix's response: native infrastructure for agentic commerce

Rewix is a B2B Ecommerce eXperience Platform designed to orchestrate multiple B2B sales journeys—direct, dealer, marketplace, RFQ, punch-out—and extend them to agentic channels. The platform integrates ecommerce, CRM, marketing automation, and analytics in a unified, fast, flexible, and reliable architecture.

Machine-readable product data

Rewix models variant-level catalogs with deep granularity: SKU/GTIN, sizes/colors/materials, pack sizes and units of measure, bills of materials, customer-specific assortments, multi-tier contractual price lists. The platform exposes these structures through clean, standardized APIs, enabling an agent to unambiguously determine: "this exact configuration is available at this specific price for this customer under this contract".

This precision is fundamental. When Google updates 2 billion listings every hour in its Shopping Graph, stale or imprecise data gets silently penalized in ranking algorithms. The difference between being visible or invisible to agents often comes down to the quality and timeliness of product metadata.

Protocol-ready checkout

Rewix implements an abstraction layer that maps orders to ACP and AP2 flows, while keeping merchant choices on payment service providers intact—Stripe, PayPal, Google Pay, and others. This architecture isolates standards evolution from the transactional core, allowing adaptation to market changes without having to redesign the ecommerce infrastructure.

Flexibility becomes a competitive advantage when the protocol landscape is rapidly evolving and no one can predict with certainty which standard will prevail.

Real-time availability

Price and stock events are propagated in real time to downstream destinations: AI modes of search engines, shopping graphs, marketplaces. This continuous synchronization preserves offer eligibility and ranking when agents query the catalog.

In agentic commerce, data latency is literally invisibility. Platforms that update feeds only every few hours or daily will discover that their products simply aren't presented to buyers, regardless of offer quality.

Distributed commerce by design

Rewix supports a peer-style B2B2C model where each network node can act simultaneously as buyer and seller, with governed catalogs, differentiated price lists, and clear inventory perimeters. This approach is ideal for complex ecosystems—brands, distributors, franchisees, partners—that want to share assortments and expand territory without fragmenting data governance.

In the agentic era, this capability becomes even more valuable: each node can expose its catalog to AI assistants while maintaining control over prices, availability, and commercial policies specific to each relationship.

Integrated AI oriented toward revenue

Artificial intelligence in Rewix isn't an accessory feature but a structural element focused on measurable economic impact:

• Sales Copilot guides sales teams in quoting and identifies cross-sell opportunities based on historical data and behavioral patterns
• Recommendations and dynamic pricing respond in real time to demand elasticity and stock levels
• Churn and customer journey analytics activate proactive playbooks before the customer drifts away

These tools don't generate descriptive dashboards, but concrete actions that protect margins and accelerate growth.

Digital Sales Room for complex negotiations

In B2B, many sales require negotiation and multiple approvals. Rewix's Digital Sales Room brings together quotes, customized bundles, discounts, rebates, and approval workflows in a secure, collaborative workspace that converts directly to orders—usable by both human teams and compatible with transactions initiated by AI agents.

A pragmatic three-phase path

Rewix proposes a structured implementation that aligns technical capabilities with business objectives:

Phase 1 – visibility and data hygiene (30 days)

Complete audit of Schema.org markup, completion of variant attributes, correction of feed gaps. Activation of real-time synchronization for prices and availability. Validation of presence in ChatGPT, Perplexity, and Google AI Mode to understand how products are currently perceived by agents.

Phase 2 – protocols and guardrails (90 days)

Enabling ACP/AP2 connectors through Rewix's abstraction layer. Testing checkout paths for single-line and multi-line B2B carts. Encoding approval chains, incoterms, tax logic, and payment terms to ensure agents never violate company policies.

Phase 3 – performance at scale (12 months)

Controlled experiments by SKU cohorts, price bands, and inventory classes. Reinforcing quality signals—reviews, expertise content, offer uniqueness—that AI algorithms value beyond simple advertising spend. Expanding the distributed model to new geographies or partner networks when conversion is proven.

Why the window of opportunity is now

Amazon has developed Rufus, its own AI assistant, but with a fundamentally defensive strategy: it operates entirely within Amazon's walled garden, using traditional checkout. It doesn't create new distribution channels, it simply protects existing market share.

Perplexity has instead executed a sophisticated multi-partner strategy—integrations with Shopify, Firmly.ai, and PayPal—seeing a fivefold increase in purchase-intent queries. Perplexity's "answer engine" approach intercepts users already mid-funnel, with specific questions like "Which 55-inch TV under €2,000 fits in a 10x10 room?". These people aren't looking for a website to reach, but an answer that helps them take action.

The direction is unequivocal: AI search is becoming AI commerce. For B2B, the potential is superior to consumer retail because agent requests often arrive with already-defined intent and clear contractual parameters. Sellers capable of responding programmatically—with the correct contractual price, verified purchasing rights, and reliable delivery times—win by default. Those who cannot, simply aren't seen.

Beyond compatibility: competing in the agentic era

Being compatible with agents is the bare minimum. Being competitive requires orchestration across catalog, pricing, inventory, and customer experience:

• Rewix's data model maintains contracts, price lists, and assortments consistent across all channels, human and agentic
• AI functions operationalize real growth levers, respecting B2B entitlements and margin constraints
• The distributed architecture enables geographic scalability and partnerships without duplicating catalogs or fragmenting governance

Rewix doesn't add AI as a superficial layer. It weaves it into the commercial core, making every discovery, quote, and order machine-readable, policy-compliant, and conversion-ready.

Getting started in 90 days

Most brands can generate tangible results in one quarter: resolve structured data quality and activate real-time feeds, enable connectors to agentic commerce protocols, pilot channels on a curated portion of the catalog, scale what converts.

It's a repeatable and measurable path. And the market is already rewarding those who move first: early adopters gain algorithmic preferences, brand recognition, operational expertise, and data insights that followers cannot easily replicate.

The transactional web is being rewritten. The choice is between contributing to building the new architecture or finding yourself forced to operate within systems defined by others. For those who are ready, Rewix offers the infrastructure to win in this new paradigm.