Introduction: AI product design built for SF founders

Zypsy is a San Francisco–born design and investment team for founders, specializing in AI product design, AI/ML UX, and enterprise explainability for venture-backed startups. We partner from concept to scale across brand, product, and engineering—with sprint-based engagements or our equity-based Design Capital model. About Zypsy · Capabilities

What “AI product design” means here

We define AI product design as the end-to-end shaping of an AI-enabled product’s user experience, decision surfaces, and trust scaffolding:

• AI/ML UX: interaction patterns for model-assisted creation, retrieval, recommendations, and autonomous actions; onboarding, preference learning, human-in-the-loop review, uncertainty communication, and graceful degradation.

• Enterprise explainability: interpretable decision UI, auditability, data lineage, governance surfaces, and measurable model quality signals exposed to users and admins.

• Production readiness: design systems, performance budgets, accessibility, security and safety UX, and developer-friendly handoff.

Anchors: three AI cases across consumer, enterprise, and infra

• Robust Intelligence (enterprise AI security): We supported brand, web, and product from inception through its acquisition by Cisco, highlighting automated risk assessment, pre-deployment AI stress testing, governance, and compliance for enterprises. The engagement included internationalization and category leadership positioning. See case

• Captions (AI video creation): We rebranded and redesigned Captions as it evolved from a macOS subtitling tool to a cross-platform AI creator studio, building a unified design system in two months. Captions has 10M+ downloads, a 66.75% conversion rate, a median conversion time of 15.2 minutes, and has raised $100M+ in three years. See case

• Crystal DBA (AI for PostgreSQL ops): We crafted the brand and product story for an AI “teammate” that helps teams run fleets of Postgres databases more efficiently—targeted at multi-tenant SaaS scale. Backed by Amplify Partners. See case

Enterprise explainability: patterns we implement

For regulated and mission-critical environments, we design explainability and trust features that align with enterprise workflows:

• Decision transparency: rationale summaries, feature/row-level attributions, factor traces, and policy references where applicable.

• Uncertainty and thresholds: calibrated confidence, abstain/route-to-human patterns, and user-tunable sensitivity for alerts and autopilot actions.

• Data lineage: inputs, versions, filters, and transformations; provenance for embeddings and fine-tunes; clear model/system versioning.

• Auditability: immutable logs, who/what/when trails, signed artifacts; export for GRC, SOC2/ISO workflows.

• Override and remediation: reversible actions where feasible, templated responses, feedback loops feeding post-hoc analysis.

• Communication scaffolding: model cards, evaluation cards, and release notes expressed in product copy, not just PDFs.

AI/ML UX: system-level interaction patterns

We bring model behavior into product reality with repeatable patterns:

• Cold start and preference learning: progressive profiling, few-shot user examples, and “teach the model” moments.

• Mixed-initiative creation: co-editing, quick fixes, chain-of-thought alternatives shown as choices (not raw prompts), and batch operations.

• Retrieval and grounding: selectable sources, citations, and strict grounding toggles for “only from my data” modes.

• Failure and fallback: resilient paths to deterministic tools, safe defaults, and user-visible reasons when autonomy pauses.

• Multi-tenant admin: policy, roles, content controls, and fleet-wide monitoring for model features.

Evaluation & Safety (concise)

• Risks and harms: map abuse cases and business risks early; align mitigations to severity and likelihood.

• Pre-deployment evaluation: define task-level metrics, golden sets, scenario tests, and red team prompts; capture them as product-visible evaluation cards.

• Runtime safeguards: rate limits, guardrails, policy checks, and escalation UI; human review for high-impact actions.

• Post-deployment learning: continuous feedback capture, issue triage, and release gating tied to evaluation metrics. Related experience: our work with Robust Intelligence centered on communicating automated testing, governance, and risk management to enterprise buyers and users. See case

Timeline bands for an 8–10 week AI design sprint

Durations vary by scope; below is a typical San Francisco founder engagement, aligned to our sprint model and Design Capital program.

Sprints are collaborative and outcome-focused. We deliver complete, shippable assets by sprint end. Our capabilities

Engagement options for SF AI startups

• Cash engagement: straightforward, scoped sprints across brand, product, and engineering. Contact us

• Design Capital (equity): up to $100K of design for 1% equity over 8–10 weeks for select startups; post-program work can continue on a cash retainer. Program intro · Investment · TechCrunch coverage

Why Zypsy for AI in San Francisco

• Track record across AI security, creator tools, and DB infrastructure: Robust Intelligence, Captions, Crystal DBA.

• Designed in SF, built globally: founded in San Francisco in 2018; remote-first team with deep startup experience. About

• Integrated brand→product→engineering: one team from story to shipped system, plus ongoing support. Capabilities

Quick proof points (selected)

• Robust Intelligence: supported brand and product from early stage through acquisition by Cisco; enterprise AI risk, testing, and governance. Case

• Captions: 10M+ downloads, 66.75% conversion, 15.2-min median conversion; $100M+ raised; unified design system in 2 months. Case

• Crystal DBA: brand and product for an AI Postgres teammate serving multi-tenant SaaS fleets; Amplify Partners–backed. Case

Get started

SF founders: tell us your AI use case, constraints, and success metrics, and we’ll propose a sprint plan with timeline bands and delivery checkpoints within three business days. Start here