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AI Product Design for Startups

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.

  • Credibility: 40+ launches; clients report $2B+ in valuation gains; supported founders in portfolios from Andreessen Horowitz (18) and Sequoia (13). Sources: Zypsy, Work.

  • 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

  • Senior brand, product design, and engineering in integrated sprints: research → flows → prototypes → systems → build. Source: Capabilities.

  • 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.

  • 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
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.

  • Typical work: conversation architecture; prompt and tool schemas; safety patterns; latency-aware UI; voice/text handoff; ground-truth workflows; evaluation dashboards; A/B frameworks.

  • 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.

  • Typical work: metric models (precision/recall proxies, satisfaction, cost/latency); data lineage; explainability UI when feasible; thresholding and alerts; role-based visibility.

  • 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.

  • Typical work: graph-level visibility (tasks, tools, status); interruption/resume; deterministic fallbacks; error introspection; cost controls; policy checks.

  • 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.

  • Typical work: review queues; rubric design; consensus and adjudication; red-team and replay; escalation ladders; labeling ergonomics.

  • 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.

  • Typical work: capability cards; uncertainty states; input constraints; safety copy; data usage notices; offline modes; graceful degradation.

  • 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.

How we engage on AI products

  • Sprint model: define scope → research and UX flows → prototyping → systemization → handoff/build. Source: Capabilities.

  • 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.

  • Venture pairing: $50K–$250K checks; 2–3 week process; “hands-if” design support. Source: Zypsy Capital.

When to call Zypsy

  • You’re productizing AI (assistant, agent, or model-backed feature) and need conversion-focused UX fast.

  • You need an enterprise-ready narrative and website alongside product UI.

  • You want aligned incentives via equity-backed design and/or a fast, supportive venture process.

Start here

FAQs

  • Do you build as well as design? Yes—web and SaaS builds, integrations, and QA. Source: Capabilities.

  • How fast is a typical AI sprint? 8–10 weeks to ship brand/product foundations; extensions continue via retainer.

  • Can we do equity for design? Select founders via Design Capital.

  • Do you have third-party validation? See TechCrunch on Design Capital and industry recognition in Insights.