{ "company": "Wildcard", "slug": "wildcard", "website": "https://wild-card.ai", "audit_date": "2026-04-16", "overall_score": 46, "tier": "Human-Dependent", "tier_as_published": "H", "pillars": { "P1": { "name": "Signal Architecture", "score": 12, "max": 25 }, "P2": { "name": "Clarity Stack", "score": 14, "max": 25 }, "P3": { "name": "Trust Envelope", "score": 5, "max": 20 }, "P4": { "name": "Velocity Triggers", "score": 6, "max": 10 }, "P5": { "name": "Gravity Design", "score": 9, "max": 20 } }, "criteria": [ { "id": "P1-A", "pillar": "P1", "name": "Structured Data", "score": 3, "max": 5, "evidence": "FAQPage (13 Q&A items) and SoftwareApplication schema confirmed on homepage. Does not include Product/Offer/AggregateRating schema that agents need to evaluate commercial offers directly." }, { "id": "P1-B", "pillar": "P1", "name": "Machine-Readable Pricing", "score": 2, "max": 5, "evidence": "Pricing is clearly displayed in an HTML table with four tiers ($249/$499/$1,499/Enterprise), credit costs, SKU limits, and workspace counts. However, no schema.org/Offer markup or JSON pricing spec detected. An AI agent must parse prose/HTML, not a structured feed." }, { "id": "P1-C", "pillar": "P1", "name": "llms.txt / Agent Layer", "score": 0, "max": 5, "evidence": "Fetching /llms.txt returned a 404. No agent identity layer or LLM-consumption file is present, despite the company's core product being about AI discoverability." }, { "id": "P1-D", "pillar": "P1", "name": "API / MCP Availability", "score": 2, "max": 5, "evidence": "\"Headless systems + API\" is listed as a feature for Growth ($499/mo) and higher plans. No public API documentation URL, OpenAPI spec, or MCP server was found during research. API access appears to exist but is not agent-discoverable." }, { "id": "P1-E", "pillar": "P1", "name": "Discoverability (GEO)", "score": 4, "max": 5, "evidence": "Ironic but positive: Wildcard's entire product is Generative Engine Optimization, so their own content is well-structured, schema-tagged, and optimized for AI retrieval. They appear in AI shopping searches and have strong structured content. Minor gap: no llms.txt to self-describe for agent context windows." }, { "id": "P2-A", "pillar": "P2", "name": "Offer Completeness", "score": 4, "max": 5, "evidence": "Pricing page clearly defines what's included at each tier: credit quantities, SKU limits, workspace count, automation count, support tier. Competitive by category standards. Minor gap: some features (e.g., \"custom integrations\") lack specificity." }, { "id": "P2-B", "pillar": "P2", "name": "Scope & Limits", "score": 4, "max": 5, "evidence": "Credit costs per action explicitly listed (blog posts = 100 credits, quick wins = 50 credits), SKU caps per tier stated, workspace and automation maximums documented. Rate limits for the API are not published." }, { "id": "P2-C", "pillar": "P2", "name": "Substitution & Fallback Rules", "score": 1, "max": 5, "evidence": "No guidance on service unavailability, what happens if credit runs out mid-workflow, or fallback behavior for tracking failures. An AI agent operating on behalf of a brand would have no guidance on contingency." }, { "id": "P2-D", "pillar": "P2", "name": "Conditional Logic Transparency", "score": 2, "max": 5, "evidence": "Enterprise pricing requires contacting sales with no disclosure of what triggers enterprise eligibility. ACP integration terms are not machine-readable. Conditions scattered across FAQs and product pages." }, { "id": "P2-E", "pillar": "P2", "name": "Semantic Precision", "score": 3, "max": 5, "evidence": "Mix of precise and vague. Specific: credit costs per action, SKU limits, workspace counts, credit-to-action ratios (~1 credit = 0.01 action). Vague: \"leading GEO platform,\" \"AI visibility,\" \"win ChatGPT Shopping\" \u2014 terms not formally defined for agent evaluation." }, { "id": "P3-A", "pillar": "P3", "name": "Verifiable Performance Data", "score": 1, "max": 5, "evidence": "No public status page found. No G2, Trustpilot, or third-party verified reviews found for this specific product (GEO platform launched 2025). Claims of effectiveness are present on homepage but entirely self-reported from a seed-stage company." }, { "id": "P3-B", "pillar": "P3", "name": "Scoped Permission Model", "score": 2, "max": 5, "evidence": "Access is scoped by workspace and user tier, but there are no agent-specific permission boundaries (time-limited, action-bounded, or spend-capped agent scopes). A human workspace model, not an agent permission model." }, { "id": "P3-C", "pillar": "P3", "name": "Audit Trail / Transaction Log", "score": 1, "max": 5, "evidence": "No evidence of machine-accessible audit logs. Dashboard analytics exist for human review of product performance, but no programmatic audit trail for agent transactions or credit consumption." }, { "id": "P3-D", "pillar": "P3", "name": "Behavioral Consistency Signals", "score": 1, "max": 5, "evidence": "Seed-stage company (founded 2025, $500K raised per Tracxn). No versioned terms of service, no published change log, no stated notice periods for pricing or API changes. Stability track record is too new to evaluate." }, { "id": "P4-A", "pillar": "P4", "name": "Friction-Free Activation", "score": 3, "max": 5, "evidence": "Self-serve signup exists (credit card required for paid plans). \"AI Visibility Audits\" offered free. However, no API key instant issuance for Growth-tier API access confirmed \u2014 onboarding flow requires account creation and plan selection before API access." }, { "id": "P4-B", "pillar": "P4", "name": "Agent Decision Signals", "score": 3, "max": 5, "evidence": "Free entry point (free audit) provides a programmatic taste of value. Credit-to-action mapping published (e.g., 100 credits = 1 blog post). However, no explicit agent-legible \"try before commit\" signal or programmatic evaluation path for autonomous decision-making." }, { "id": "P5-A", "pillar": "P5", "name": "Integration Depth / Switching Cost", "score": 3, "max": 5, "evidence": "Shopify integration creates moderate switching cost via synced SKU data and tracked product history. Enterprise PIM connect and headless CMS integrations deepen lock-in. Not extreme \u2014 a brand could export data and rebuild elsewhere within weeks." }, { "id": "P5-B", "pillar": "P5", "name": "Agent Memory / Personalization Layer", "score": 2, "max": 5, "evidence": "Platform accumulates product performance data over time (which prompts surface which SKUs), but this history is not accessible via agent-readable API. No documented memory endpoint or context layer for agents to query historical performance." }, { "id": "P5-C", "pillar": "P5", "name": "Programmatic Renewal Signals", "score": 1, "max": 5, "evidence": "Standard monthly/annual SaaS subscription. No agent-accessible renewal API, no programmatic re-up signal. Renewals happen through a human billing flow." }, { "id": "P5-D", "pillar": "P5", "name": "Compounding Value Signal", "score": 3, "max": 5, "evidence": "The platform's value genuinely compounds: as more prompts are tracked and competitors are benchmarked, the data becomes richer and the recommendations more precise. However, this compounding value is not surfaced in an agent-readable format. An agent evaluating \"is this still worth renewing?\" has no signal to query." } ], "strongest_signals": [ { "title": "GEO-native content architecture", "detail": ": Wildcard's own product pages use FAQPage and SoftwareApplication schema, making them well-discoverable by AI \u2014 they eat their own cooking, though incompletely (no llms.txt, no Offer schema)." }, { "title": "Clear offer scoping", "detail": ": Credit costs per action type are explicitly documented on the pricing page, giving an AI agent enough structure to calculate per-task economics without human interpretation." }, { "title": "ACP protocol support", "detail": ": Integration with the Agentic Commerce Protocol (maintained by OpenAI/Stripe) signals early mover positioning in the agent-native commerce layer \u2014 Wildcard enables instant checkout in ChatGPT for brands that use their platform." }, { "title": "Compounding product intelligence", "detail": ": The platform builds a richer picture of AI visibility over time (prompt tracking, competitive benchmarking, SKU history), which creates genuine long-term value even if not yet agent-readable." } ], "critical_gaps": [ { "title": "No llms.txt", "detail": ": A GEO platform that helps brands win AI visibility has no /llms.txt for itself. This is a glaring contradiction \u2014 the company's primary insight (AI agents need structured signals) is not applied to their own offer." }, { "title": "No verifiable performance data", "detail": ": Zero third-party verified reviews, no status page, no uptime SLA published. A newly funded seed company with no external validation is opaque to autonomous evaluation. Agents and agent-adjacent buyers default to rejecting offers they cannot verify." }, { "title": "No agent-scoped permissions", "detail": ": No time-bounded, spend-capped, or action-limited permission model exists. An AI agent authorized to \"manage AI visibility\" on behalf of a brand has no granular permission scaffold to operate within." }, { "title": "API undiscoverable", "detail": ": API access exists (Growth+ plan) but no public docs, no OpenAPI spec, no MCP server. An agent trying to programmatically integrate Wildcard's services cannot find them." } ], "priority_actions": [ { "action": "Publish /llms.txt", "points_gain": 4, "pillar": "P1", "effort": "Low" }, { "action": "Publish OpenAPI spec + API docs", "points_gain": 3, "pillar": "P1", "effort": "Medium" }, { "action": "Add schema.org/Offer markup to pricing page", "points_gain": 2, "pillar": "P1", "effort": "Low" }, { "action": "Launch a public status page + publish SLA", "points_gain": 3, "pillar": "P3", "effort": "Low" }, { "action": "Publish agent-scoped permission model", "points_gain": 3, "pillar": "P3", "effort": "Medium" } ], "executive_summary": "Wildcard sits at 46/100 in the Human-Dependent tier \u2014 a company with genuine agent-native product vision that has not yet applied that vision to its own offer infrastructure. Their strongest pillar is Clarity Stack (14/25), where credit-based pricing with per-action costs is notably transparent, and their ACP protocol support signals real commitment to the agent commerce era. The most critical gap is trust: a seed-stage company with no third-party reviews, no public status page, and no verifiable SLA is opaque to autonomous evaluation. The single highest-ROI action is also the most ironic: Wildcard \u2014 a GEO platform \u2014 should immediately publish its own /llms.txt, which would both demonstrate their framework and signal that their offer is readable by the AI agents they're helping brands reach.", "rubric_version": "v1-2026-04 (20 criteria, 100 raw points; P3-E Agent Registration added to rubric v2 in 2026-06, not scored in this audit)", "framework": "Agent Native Offers \u2014 The Agent Sale framework", "source_file": "2026-04-16 \u2014 Wildcard \u2014 Agent Native Offer Audit.md", "data_note": "Criterion-level scores sum to 45; the published pillar totals (authoritative) sum to 46. Discrepancy traces to the original audit document and is preserved for transparency.", "rank": 24 }