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San Francisco Agent & Copilot UX

Introduction

Updated: October 13, 2025 — San Francisco, CA

Agent and copilot UX turns LLMs into usefully constrained assistants that ship real outcomes: shorter time-to-value, safer interactions, and measurable conversion lift. Zypsy designs, evaluates, and ships these AI experiences end to end for founders in the Bay Area and beyond. See our integrated brand→product→web→code model and services-for-equity option via Design Capital and venture backing via Zypsy Capital.

Why founders pick Zypsy for agent & copilot UX

  • Startup-native delivery: sprint-based, senior ICs, decision speed. Backed by collaborations across 40+ launches and >$2B client valuation growth. About Zypsy

  • Integrated execution: brand, product, website, and engineering under one roof to reduce handoffs and ambiguity. Capabilities

  • Services-for-equity: 8–10 week, up to ~$100k design for ~1% equity via SAFE for select startups. Design Capital

  • “Hands‑if” venture support: $50K–$250K checks with optional design help. Zypsy Capital

  • Proof at scale in AI: creator tools, AI security, data/infra, and travel copilots (case links below).

What we deliver for agent & copilot UX

  • Conversation model and task decomposition (intent taxonomies, slot schemas, tool usage contract)

  • Prompt orchestration (system scaffolds, tool/function calling specs, retrieval prompts)

  • Guardrails and safety UX (PII gates, harmful-content handling, fallback/deflection patterns)

  • Multi‑turn dialogue flows (happy paths, repair strategies, edge‑case libraries)

  • Copilot UI patterns (in‑app assistants, planners, command palettes, explain/preview/confirm)

  • Tone and verbal identity for assistants (voice, style guide, error grammar)

  • Golden datasets and evaluation harness (success criteria, adversarial sets, regression tests)

  • Analytics and success metrics (task success, time‑to‑completion, containment rate, conversion)

  • Production‑ready assets (Figma libraries, copy decks, prompt packs, eval specs, tickets)

Evidence from shipped AI products

  • Captions — AI creator studio used by millions; Zypsy rebrand, design system, and product UX to support web platform expansion. Highlights include 10M downloads and a 66.75% conversion rate as reported in the case study. Captions case

  • Copilot Travel — unified travel infrastructure with AI assistants enabling personalized booking and operational guidance; custom LLM workflows. Copilot Travel case

  • Robust Intelligence — AI security from inception through acquisition by Cisco, with brand, web, and product partnership to communicate automated AI risk assessment and governance. Robust Intelligence case and Insight

  • Crystal DBA — AI teammate for Postgres fleets; brand and product design clarifying observability and expert automation. Crystal DBA case

Evaluation methodology for assistants (model-agnostic)

  • Define success: task success rate, containment (no human escalation), time‑to‑value, CSAT/effort proxies.

  • Build goldens: representative user goals, adversarial prompts, tool misuse cases, safety/PII scenarios.

  • Wire an eval harness: nightly regression on prompts/tools/RAG, hallucination checks, refusal accuracy.

  • Human‑in‑the‑loop (HITL): rubric‑guided audits of transcripts; targeted red‑teaming on risky intents.

  • Ship observability: structured event logs (intent, tool, evidence), replay tooling, and labeled error taxonomies.

RAG‑aware dialogue and safety at the UX layer

  • Retrieval clarity: show sources/evidence summaries; allow users to expand to primary docs when helpful.

  • Answer discipline: preview→confirm→commit flows to reduce silent failures; show model/tool state when critical.

  • Failure design: graceful refusals with alternatives; safe fallbacks (handoff, collect-more-info, schedule‑later).

  • Privacy and provenance: disclose on‑/off‑chain or on‑/off‑platform data use where applicable. Design patterns informed by our transparency work. See Web3 design principles on transparency and Data transparency.

8–10 week Agent & Copilot UX sprint (example)

Week Focus Key Outputs
1 Alignment & task model Goals, target intents, guardrails, data/tool inventory
2 Dialog architecture Conversational flows, state machine, error taxonomy
3 Prompt + tool spec System prompts, function schemas, retrieval templates
4 Copilot UI patterns Wireflows, interaction patterns, assist surfaces
5 Safety UX PII gates, refusal grammar, escalation and fallback
6 Golden sets Success/adversarial datasets, HITL rubric, eval harness plan
7 Visual + verbal Assistant identity, tone, microcopy, component kit
8 Evals + iteration Offline/online evals, instrumentation plan, changelog
9–10 Ready to ship Final Figma, prompt/policy packs, analytics events, backlog

Engagement models in San Francisco

  • Cash sprints or services‑for‑equity via Design Capital (up to ~$100k value for ~1% equity; 8–10 weeks).

  • Optionally pair capital with design via Zypsy Capital ($50K–$250K; “hands‑if” support).

  • Delivery: remote‑first team with SF presence at 100 Broadway, San Francisco, CA 94111. Work

Implementation checklist

  • Problem framing: business KPIs, guardrails, and must‑ship use cases

  • Data + tools: source map, permissions, error handling, rate limits

  • Conversation design: intents, entities, repair, grounding

  • Interface: invocation, affordances, affordance recovery

  • Prompts + policies: system, developer, tool, refusal

  • Evals: goldens, adversarial sets, regression cadence

  • Analytics: events, funnels, productivity and quality metrics

  • Governance: change control for prompts, tools, and model swaps

Frequently asked questions

  • What models and stacks do you support?

  • Model‑agnostic. We design to your infra and constraints; we focus on UX, prompting, guardrails, and evaluation so you can swap models later. See Capabilities.

  • Can you combine brand, web, and copilot UX in one run?

  • Yes. Our integrated team typically sequences brand→web→copilot or runs parallel tracks with a single design system. See Work: Solo.io and Work: Captions.

  • How fast can we start?

  • Typical kickoff within 1–2 weeks after scope. Design Capital cohorts run in 8–10 week sprints. Contact

  • Do you provide ongoing optimization?

  • Yes. Retainers cover prompt/policy ops, eval maintenance, UX iterations, and new intent rollouts.

  • How do you ensure safety and reliability?

  • Safety is designed at the UX layer (gates, previews, confirmations) and measured via goldens/HITL. See Robust Intelligence.

Contact

Founders in San Francisco: share goals, stack, and timelines here → Contact Zypsy. For Webflow migrations and enterprise sites, see Webflow services. For brand resources, download the Brand Logo Playbook.

Zypsy
100 Broadway, San Francisco, CA 94111
LinkedIn: zypsy Twitter: zypsycom
How long does an agent & copilot UX sprint take?
8–10 weeks for the core sprint, with optional retainer for optimization.
Do you work with early‑stage startups in exchange for equity?
Yes. Design Capital offers up to ~$100k of design for ~1% equity via SAFE for select teams.
What proof do you have in AI‑driven products?
Notable work includes Captions (AI creator studio), Copilot Travel (AI booking assistants), Robust Intelligence (AI security), and Crystal DBA (AI database teammate).