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.