# Mode ICP and ECP ## Summary Mode's current segmentation model separates early adopters, ECP, and ICP. `ECP` means `Early Customer Profile`: the subset of ICP Mode should target first to learn quickly and grow before roughly `$0.5M-$1M ARR`. This approved note summarizes Mode's ICP/ECP segmentation and is ready for Zep ingestion as internal company context. ## Segmentation model ### 1. Early adopters Early adopters are designers and design engineers who experiment heavily with AI tools, post about them, and can create word-of-mouth. They are not necessarily Modeinspect's ICP, but may be a useful launch wedge into ECP and ICP buyers. Common early-adopter signals include AI-native design identity, high engagement with new AI design/code tools, public experimentation, agency/startup/midmarket context, teaching/writing/speaking/tutorial activity, and themes around AI-ready design systems, design-system governance, context engineering, Figma MCP, Claude, or Claude Design. Launch-model priorities also include AI-native designers, design engineers/code-adjacent product designers, design-system operators, AI-ready design-system builders, and AI design creators/educators for public beta distribution and proof. ### 2. ECP: Early Customer Profile ECP is Mode's first commercial/customer-learning target inside the broader ICP. The profile is midmarket product companies with roughly 50-600 employees, 1-3 designers, and a modern AI-friendly stack such as React. The target person is an IC designer or design lead; the design lead is often also an IC. The `50-600 employees` plus `1-3 designers` profile should be treated as a directional ECP hypothesis, not as a strict search filter by itself. It is plausible in product-led companies where a small product-design function supports a much larger engineering/product org, but the upper end can become too restrictive if interpreted literally as exactly 1-3 total designers across the whole company. A safer working recommendation is: use `50-300 employees with 1-3 product designers` as the tightest ECP prospecting filter; include `300-600 employee` companies only when there is clear evidence of a very lean product-design function, a specific product/workstream with 1-3 responsible designers, or a low design-to-engineering ratio / design QA pain signal. Keep `1 designer to 7-10 engineers` and design QA friction as higher-confidence qualification signals than the company-size band alone. The core ECP trigger is a low design-to-engineering ratio where the team needs higher design output. A strong-deal ratio is approximately 1 designer to 7-10 engineers, and design QA is a strong wedge when Modeinspect can reduce timelines. The feature evidence behind the ECP wedge is that design QA loops stretch across multiple handoffs and implementation rounds, and the design QA workflow is high relevance for teams with low designer-to-engineer ratios. Public/audience messaging for ECP should emphasize increasing design output when design is small relative to engineering, reducing design QA timelines, and letting designers fix visible product UI issues directly in the browser without local setup or code literacy. The launch plan treats ECP design leads at small/midmarket product companies as a commercial-signal/manual-assist segment with the target profile: roughly 50-600 employee product companies, 1-3 designers, many engineers, design QA/handoff/visual polish friction, and a modern supported stack. ### 3. ICP: Long-term high-ACV profile Modeinspect is likely strongest for software companies with mature design teams, active design systems, complex production UI, and enough engineering process that design handoff and QA loops are visibly expensive. The long-term high-ACV ICP profile is small-mid enterprise companies with roughly 1,000-5,000 employees, design teams of roughly 10-50, and strongest vertical hypotheses around consumer/B2C, travel, hospitality, and e-commerce; B2B SaaS can also fit. The target ICP people are middle-management design managers/leads, leadership such as director/head of design/VP, design operations, and design systems management. Engineering leaders approve if code quality, security, and PR review workflow are credible; procurement/security evaluate SOC 2, data retention, LLM data-use posture, deployment options, third-party processors, and source-code handling. ICP triggers include upper-management AI adoption pressure, design-system teams tasked with exploring AI-tool adherence, and a private signal that roughly 30% of pipeline deals have had a design-system-management champion. Feature evidence supports the ICP profile through design-system enforcement, DeepCode/code-understanding proof, managed secure development environment, and enterprise packaging/deployment options. ## Firmographic and behavioral fit signals Strong-fit account signals include B2B SaaS or marketplace/product-led software, 50+ employees with clearer fit at 200+ when design systems/platform teams exist, multi-product or complex product surface area, a dedicated product design team, an existing design system or component library, shared primitives, active AI prototyping/design experimentation, React/Next/Tailwind or supported-framework alignment, and frequent front-end shipping cadence. Relevant user/persona hypotheses include product designers working on production UI, design system designers, design engineers, designers prototyping product changes with AI tools before engineering involvement, front-end engineers reviewing Modeinspect PRs, PMs/product leaders needing concrete prototypes, and engineering reviewers needing normal PR review workflows. ## Trigger events Trigger events include AI adoption pressure, low design-to-engineering ratio, long design QA loops, inconsistent design-system adoption in code, need for realistic prototypes, distrust of existing AI tools for production output, AI tools breaking design-system consistency, desire to collaborate on AI-created prototypes, dissatisfaction with Claude Design/Cursor/MCP-dependent workflows, engineering rejection of AI-generated design/code output, and engineering bandwidth blocking visual polish. ## Disqualifiers and exclusions Disqualifiers to validate include no real design system or component discipline, unsupported tech stack, low product UI complexity, very small team with no handoff pain, incompatible security constraints, no executive sponsor across design and engineering, too-simple design systems, primary need for unconstrained 0-1 ideation, canvas-only exploration needs, inability to connect a real codebase, stack outside current public support, and desire for a generic full-stack app builder rather than product UI changes inside an existing codebase. Excluded verticals from core ICP/ECP targeting are fintech/banking, insurance, cybersecurity, devtools, native mobile-only platforms, companies handling highly sensitive data where codebase access creates disproportionate friction, companies without a user-facing platform/product of their own, and companies with low iteration frequency on their websites or product surfaces.