Unusual

What do they actually do

Unusual audits what leading AI assistants (e.g., ChatGPT, Copilot, Perplexity) currently say about a company, then helps fix the gaps that cause wrong or unhelpful recommendations. They run structured prompt “probes” across multiple models to see how a brand is described, which sources are cited, and what facts or framing are missing, and then propose concrete fixes YC page site docs.

Customers get a prioritized plan and, often, ready‑to‑publish machine‑readable assets (schema/JSON‑LD, pricing.json, knowledge cards, llms.txt guidance) aimed at making assistants cite authoritative sources. A SaaS dashboard and free checkers track when AI Overviews/Copilot/Perplexity pick up the changes and whether assistant‑driven traffic or leads move, with a mix of self‑serve and hands‑on support available docs app site YC page.

Who are their target customer(s)

• Heads of growth/marketing at B2B SaaS startups: Assistants often describe the product incorrectly or omit pricing and key differentiators, steering prospects to competitors. They need a way to correct assistant outputs and tie fixes to qualified leads site YC page.
• Ecommerce or product teams with large catalogs: Assistants pull outdated or inconsistent specs/pricing from third parties, leading to wrong recommendations. Teams struggle to publish and maintain machine‑readable, citable product/pricing facts at scale docs site.
• Enterprise SEO, communications, or brand teams: They can’t reliably control how assistants summarize the brand and lack governance to keep fixes from regressing as models update. They need repeatable playbooks and monitoring across stakeholders docs.
• Agencies and PR/SEO consultancies: Running many LLM probes, producing structured assets, and proving impact is manual and slow. They want standard templates, validators, and dashboards to deliver consistent results across clients docs.
• Heads of sales or demand‑gen relying on inbound: Little visibility into how often assistants drive qualified traffic or leads makes it hard to prioritize fixes. They need dashboards and attribution to connect assistant citations to conversions docs app.

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

• First 10: Run personalized, free “model probe” audits for targeted B2B SaaS and large ecommerce teams, then convert with short pilots that ship a few machine‑readable assets and show assistant citations changing within weeks; publish concrete case outcomes to win referrals site docs.
• First 50: Productize the audit into a self‑serve assistant checkup and sell a fixed‑scope package (templates + 30‑day monitoring) to agencies, SEO teams, and mid‑market companies; distribute via validators/templates and targeted outbound to agencies and marketing teams docs YC page.
• First 100: Add paid LinkedIn/SEM for growth leaders, partner with CMS/PIM/TMS vendors and SEO agencies, and launch a self‑serve tier with usage pricing. Invest in connectors and automated publish/verify loops and surface ROI dashboards to close sales/demand‑gen buyers docs app.

What is the rough total addressable market

Top-down context:

If assistant discoverability becomes a first‑class channel, spend can draw from broader martech and digital‑marketing budgets, which analysts estimate in the hundreds of billions (e.g., Forrester: ~$148B martech in 2024) Forrester Grand View.

Bottom-up calculation:

Conservative, directly relevant TAM ≈ $90–100B by summing adjacent categories Unusual replaces/productizes: SEO services (~$75B), PIM tools/services (~$17–20B), and enterprise knowledge/graph tools (~$1–3B) Mordor SEO Mordor PIM MarketsandMarkets KG.

Assumptions:

• SEO/PIM/knowledge spend is partially substitutable toward AI assistant visibility work.
• Includes software plus associated services/implementation commonly bought for these outcomes.
• Timeframe aligned to ~2025 industry estimates; excludes broader ad spend.

Who are some of their notable competitors

• Yext: Enterprise platform for structured brand data (pages, schema, listings) and distribution to search/assistants; also offers an AI visibility/monitoring agent (Scout). Overlaps on structured data and monitoring content Scout.
• Profound: “Answer‑engine optimization” tool that scans ChatGPT/Gemini/Perplexity, surfaces citations/gaps, and recommends or generates content to improve AI visibility—direct overlap with Unusual’s probing and content recs site Forbes.
• Schema App: Schema/structured‑data platform and consultancy with guidance on making content “model‑ready” for LLMs (JSON‑LD/MCP workflows). Competes on templates, validation, and publishing playbooks brief.
• Rank Prompt: Monitoring tool that scans multi‑LLM prompts to track brand mentions and prioritizes fixes (schema, citations, content). Overlaps with Unusual’s probing + tracking features site announcement.
• Passionfruit (Passionfruit Labs): Agency + product focused on AI search/GE0; tracks LLM citations, ties mentions to attribution, and helps produce LLM‑friendly content—competes for agencies and teams needing both tracking and content pipelines tools list site.