Wildcard

What do they actually do

Wildcard runs two live products.

First, a merchant-facing SaaS that helps e‑commerce brands see how their products show up inside AI assistants (like ChatGPT Shopping). Merchants connect Shopify, WooCommerce, BigCommerce, Magento, Square and similar platforms; Wildcard scans the catalog, tracks where each SKU is mentioned in AI shopping answers, surfaces the buyer queries that trigger those mentions, and recommends specific attribute/feed fixes to improve visibility. It then reports back on changes within roughly 24–48 hours and shows impact over time. The product is currently sold via demos and onboarding rather than pure self‑serve wild-card.ai.

Second, an open developer standard and SDK: agents.json (a spec layered on OpenAPI) plus Wildcard Bridge, which lets API providers describe multi‑step workflows and gives agents a runtime to invoke them safely. Wildcard maintains a public spec, registry, and demos (e.g., Google Sheets, Resend) so agents can discover and execute those workflows end‑to‑end github.com/wild-card-ai/agents-json, docs.wild-card.ai.

Who are their target customer(s)

• E‑commerce merchants / product managers on Shopify, WooCommerce, BigCommerce, Magento, Square: Their SKUs are inconsistently visible in AI shopping results, and they lack SKU‑level insight into which queries surface their products and which metadata gaps to fix to improve ranking and conversions wild-card.ai.
• Catalog, data, or SEO teams at larger retailers: They need to scale attribute fixes across thousands of SKUs and verify whether changes improve AI discoverability, but don’t have a way to monitor AI mentions and ranking movement over time wild-card.ai.
• Platform / engineering teams at commerce platforms or larger merchants: They need a secure, standard way to accept AI‑agent‑initiated orders without rewriting core systems; preparing for OpenAI’s Agentic Commerce Protocol (ACP) and Instant Checkout requires new checkout and payment plumbing OpenAI ACP docs.
• API providers and developer teams (email, payments, SaaS APIs): They lack a simple, machine‑readable contract for what an agent can safely do with their API and a runtime that handles auth, failure modes, and orchestration across multi‑step flows agents.json, Wildcard Bridge docs.
• Agent builders and assistant teams: It’s hard to discover trusted API workflows and stitch them together reliably; they need a registry and an execution layer to run chains described in a standard way Wildcard registry, agents.json.

How would they acquire their first 10, 50, and 100 customers

• First 10: High‑touch demos and pilots with merchants already asking about ChatGPT Shopping; convert YC/inbound leads into paid pilots with guided onboarding and short‑term discounts to instrument feeds and show SKU‑level AI mentions wild-card.ai.
• First 50: Publish 2–3 case studies showing measurable lift; start a referral/agency program with Shopify/WooCommerce integrators; launch a simple self‑serve connector for smaller merchants. In parallel, use the agents.json registry/demos to engage API providers and agent builders for integrations and technical partnerships wild-card.ai, agents.json.
• First 100: Scale content and marketplace listings (Shopify App Store, BigCommerce, etc.), plus webinars for catalog/SEO and platform teams. Run partner pilots for ACP/Instant Checkout and grow the open‑source/registry footprint so Bridge becomes a default runtime for agentable APIs Wildcard Bridge docs, YC page.

What is the rough total addressable market

Top-down context:

AI shopping is moving inside assistants: OpenAI launched Shopping Research and Instant Checkout in ChatGPT, backed by the Agentic Commerce Protocol (ACP) co‑developed with Stripe OpenAI Shopping Research, OpenAI Instant Checkout/ACP. Global ecommerce is a multi‑trillion‑dollar market (~$6.4T in 2025), with millions of merchants on platforms like Shopify Shopify global ecommerce report, Shopify About: “millions of merchants”.

Bottom-up calculation:

If 50,000 mid‑market+ merchants (those with material SKU counts and marketing ops) adopt AI‑shopping optimization at an average $5,000/year, that’s ~$250M ARR. Add a developer tooling layer: 10,000 API providers adopting agents.json/Bridge at an average $1,000/year contributes ~$10M ARR. Combined illustrative TAM: ~$260M for early AI‑shopping/agent‑enablement buyers, expanding as ACP rolls out.

Assumptions:

• Focuses on merchants with sufficient catalog size and marketing sophistication to invest; SMB tail not included.
• Average pricing bands (merchant $3k–$10k/year; developer/API $500–$2k/year) reflect early B2B tooling.
• Adoption estimates are conservative relative to the broader universe of Shopify/WooCommerce/BigCommerce merchants and do not include services revenue.

Who are some of their notable competitors

• Feedonomics: Feed management and syndication across ads, marketplaces and emerging AI channels; positions as connective tissue for commerce feeds and agentic discovery/checkout extensions Feedonomics.
• Productsup: Enterprise feed management and product content syndication to 2,500+ channels; processes trillions of product updates monthly and targets large retailers/brands Productsup.
• Rithum (CommerceHub + ChannelAdvisor): Commerce operations platform for listings, retail media, marketplaces and dropship—large network from the CommerceHub–ChannelAdvisor combination Rithum, acquisition press.
• LangChain OpenAPI Toolkit: Developer toolkit to let agents consume arbitrary OpenAPI‑described APIs; an alternative path to making APIs agent‑callable without a separate registry LangChain OpenAPI.
• Model Context Protocol (MCP): Open standard (led by Anthropic) to connect assistants to tools and data; growing ecosystem for agent/tool integrations that can overlap with agents.json use cases Anthropic MCP.