Introduction
Founders use this hub to map AI use cases to concrete UX, brand, and engineering deliverables. Zypsy designs and ships AI products through sprint-based work or equity-backed design, then scales them with systems, websites, and code. Evidence comes from shipped AI engagements across security, data, creator tools, and infrastructure.
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Credibility: 40+ launches; clients report $2B+ in valuation gains; supported founders in portfolios from Andreessen Horowitz (18) and Sequoia (13). Sources: Zypsy, Work.
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Engagement modes: Cash projects, services-for-equity via Design Capital, and venture checks via Zypsy Capital.
Contents
Note: Dedicated deep-dive pages for each pillar are planned; this hub links to live case studies and capability pages now.
What founders get
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Senior brand, product design, and engineering in integrated sprints: research → flows → prototypes → systems → build. Source: Capabilities.
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Equity-based design option: 8–10 weeks of brand/product work (up to ~$100k value) for ~1% equity via SAFE. Sources: Design Capital, third-party coverage in TechCrunch.
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Optional venture: $50K–$250K checks, 2–3 week process, “hands-if” design support. Source: Zypsy Capital.
Proof from AI work (selected)
The following examples illustrate AI surfaces, our contribution, and measurable or milestone outcomes.
Client | AI surface | Zypsy contribution | Notable outcome |
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Captions | AI video creation (editing, dubbing, avatars) | Rebrand, product UX, cross-platform system in 2 months | 10M downloads; 66.75% conversion; $60M Series C within 3 years and $100M+ raised overall |
Robust Intelligence | AI security, automated risk assessment | Brand, web, product UX, global rollout | Acquired by Cisco; recognized for innovation in 2024. Also see Insights. |
Copilot Travel | AI-powered booking assistants; custom LLM | Brand, multi-product IA, product UX | AI-driven travel infra positioning; CEO testimonial on growth impact |
Crystal DBA | AI teammate for PostgreSQL fleets | Brand, website, product visuals | Founder testimonial; positioning AI for DB ops |
Solo.io | API and AI gateways; service mesh | Rebrand, 31-page site, systematized product UX | Market leadership messaging; enterprise case signals |
Sources are the linked case studies and Insights.
Conversational AI
Design conversational systems end-to-end: intents, guardrails, memory, retrieval, voice/UI multimodality, and evaluation loops.
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Typical work: conversation architecture; prompt and tool schemas; safety patterns; latency-aware UI; voice/text handoff; ground-truth workflows; evaluation dashboards; A/B frameworks.
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Outputs: annotated flows and error states, prompt kits, RAG/agent scaffolds in product UX, trust disclosures, and upgrade paths to voice/avatars (e.g., the Captions stack spans voice and avatar experiences). Source: Captions.
AI Dashboard Design
Build decision-grade observability for AI features: inputs, outputs, confidence, provenance, drift, and feedback.
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Typical work: metric models (precision/recall proxies, satisfaction, cost/latency); data lineage; explainability UI when feasible; thresholding and alerts; role-based visibility.
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Outputs: E2E dashboard UX with filters, segments, export, and annotation; governance views for leaders; developer diagnostics. Source patterns visible across Solo.io and Crystal DBA.
Agent Orchestration UI
Design for multi-agent tools and human–tool coordination: planning, tool calling, retries, and decomposition.
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Typical work: graph-level visibility (tasks, tools, status); interruption/resume; deterministic fallbacks; error introspection; cost controls; policy checks.
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Outputs: orchestration canvases, queue UIs, and run histories with diff/compare; system prompts and tool contracts embedded in UX. Related enterprise patterns appear in Solo.io and risk/governance lessons from Robust Intelligence.
Human-in-the-Loop (HITL) UX
Place humans at critical junctions to correct, approve, and escalate model actions—without destroying throughput.
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Typical work: review queues; rubric design; consensus and adjudication; red-team and replay; escalation ladders; labeling ergonomics.
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Outputs: reviewer consoles; sampling and spot-check flows; policy and audit trails visible in the product; cross-role handoffs. Trust-by-design thinking is reflected in Zypsy’s transparency writing and AI security work. Sources: Robust Intelligence, Insights.
AI/ML UX
Make model capabilities legible and safe for end users; communicate limits; support progressive disclosure.
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Typical work: capability cards; uncertainty states; input constraints; safety copy; data usage notices; offline modes; graceful degradation.
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Outputs: component libraries for AI affordances; error/state matrices; copy frameworks. See systems thinking across Capabilities and shipped AI experiences in Captions and Copilot Travel.
Safety, transparency, and governance
Zypsy advocates for transparent systems and verifiable interactions—principles we’ve articulated publicly for high-trust software.
- Design themes: provenance and data transparency; event logging; code and interaction clarity; user-readable histories. See related writing: Data Transparency, Smart Contract Event Transparency, Code Transparency. While web3-focused, the trust patterns generalize to AI features handling sensitive data and irreversible actions.
How we engage on AI products
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Sprint model: define scope → research and UX flows → prototyping → systemization → handoff/build. Source: Capabilities.
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Services-for-equity option: up to ~$100k of design over 8–10 weeks for ~1% equity via SAFE; post-sprint cash retainers as needed. Sources: Design Capital, TechCrunch coverage.
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Venture pairing: $50K–$250K checks; 2–3 week process; “hands-if” design support. Source: Zypsy Capital.
When to call Zypsy
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You’re productizing AI (assistant, agent, or model-backed feature) and need conversion-focused UX fast.
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You need an enterprise-ready narrative and website alongside product UI.
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You want aligned incentives via equity-backed design and/or a fast, supportive venture process.
Start here
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Explore capabilities: Brand, Product, Engineering
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Review AI work: Work, incl. Captions, Robust Intelligence, Copilot Travel
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Apply or inquire: Contact • Investment: Zypsy Capital
FAQs
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Do you build as well as design? Yes—web and SaaS builds, integrations, and QA. Source: Capabilities.
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How fast is a typical AI sprint? 8–10 weeks to ship brand/product foundations; extensions continue via retainer.
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Can we do equity for design? Select founders via Design Capital.
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Do you have third-party validation? See TechCrunch on Design Capital and industry recognition in Insights.