AI Design Agency

We design production‑grade AI/ML UX for agents, RAG, evaluation dashboards, and governance—shipping engineering‑ready prototypes, specs, and audit assets in 3–6 weeks.

AI/ML UX

Explore our sprint approach and deliverables: AI/ML UX Sprint (3–6 Weeks). Proof of outcomes: Captions · Robust Intelligence

AI Practice at a Glance

We design production-grade AI UX across agents, RAG, evaluation dashboards, and governance. Typical engagements ship in 3–6 weeks with engineering‑ready prototypes, UX specs, and governance assets (model cards, decision logs, audit exports). Proof of outcomes includes Captions (10M downloads; 66.75% conversion; $60M Series C) and Robust Intelligence (acquired by Cisco for $400M). Based in San Francisco; remote‑first with Bay Area on‑sites; Design Capital available for eligible founders.

• See case work: Captions · Robust Intelligence

Quick links: AI/ML UX Sprint · Pricing> Raising an AI round?

AI Pitch Deck Design (2–4 weeks): Investor‑grade story, metrics, and visuals tailored for AI startups. Deliverables include narrative, traction and market slides, product/AI architecture, data advantage and moat, unit economics (inference cost), roadmap, team, and use of funds. Optional investor intros via Zypsy Capital when there’s a fit.

Learn more → /ai-pitch-deck-design · Start now → https://www.zypsy.com/contact

FAQ: AI investor decks

• How is an AI investor deck different? Beyond a standard SaaS deck, we surface AI‑specific proof: evaluation results (quality/safety/latency/cost), data advantage, model/infra choices, inference cost unit economics, governance and risk mitigations, and defensibility over time.

• What do we deliver in 2–4 weeks? A complete, investor‑ready deck with story arc, market/TAM, traction and KPIs, product and AI architecture, data strategy and moat, GTM, financials and unit economics, team, and use of funds—plus a visual system you can reuse.

Pricing

We scope AI/ML UX work in sprints with transparent, fixed pricing shared upfront.

• Format: 3–6 week sprints focused on critical-path UX (eval dashboards, HITL, drift/lineage, RBAC/audit) with engineering‑ready specs.

• What’s included: IA for model/data/policy objects, prototypes, UX specs, and governance assets (model cards, decision logs, audit exports).

• Equity option: For eligible founders, Design Capital can exchange an 8–10 week design sprint (up to ~$100k value) for ~1% equity via SAFE; follow‑on work can continue on a cash retainer. See Investment and coverage in TechCrunch.

• SF availability: Based in San Francisco with Bay Area on‑site sessions available; remote‑first delivery.

Contact us to price your sprint: https://www.zypsy.com/contact

AI UX Agency: Agents, RAG, Dashboards, Governance

AI UX agency for agents, RAG, dashboards, and governance. We design production‑grade UX that makes model quality legible, actions safe, and decisions auditable across enterprise AI.

• Proof: Captions — 10M downloads, 66.75% conversion, $60M Series C · Robust Intelligence — acquired by Cisco for $400M

Hub: Looking for our AI Product Design Agency page? → /ai-product-design-agency

See also

• Conversational AI Agency → /conversational-ai-agency

• AI Dashboards, Governance & NLQ → /ai-dashboard-design-governance-nlq

AI/ML UX agency: Agents, RAG, Governance

Design production-grade UX for AI systems with agents, retrieval-backed experiences, and enterprise governance. Jump to: Agents · RAG · Governance

Agents

AI teammates that act safely and transparently. We design fleet‑level observability and control, escalation paths, and rollback safeguards. Proof: Crystal DBA. See also in‑page: LLM evaluation dashboards.

RAG

Retrieval‑augmented UX that makes context, versions, latency, and cost legible across creation and review flows. Proof: Captions. See also: LLM evaluation dashboards and Human‑in‑the‑loop labeling and review.

Governance

Decision trails, safety gates, RBAC, and exportable evidence packs for regulated AI. Proof: Robust Intelligence. See also: RBAC and audit for AI platforms.

Introduction

AI products win on trust, clarity, and control. Zypsy designs production-grade UX for AI/ML systems—turning opaque models into explainable, governable software. Our patterns are field-tested across enterprise security, data infrastructure, developer platforms, and AI gateways, with proof from work on Robust Intelligence, Crystal DBA, Solo.io, and Cortex. Updated October 6, 2025

Why we’re an AI design agency

• Startup-first focus: We partner with founders from pre-seed to growth across AI, data, security, and developer platforms, bringing sprint-based velocity and enterprise-grade rigor (About, Capabilities).

• Governance by design: Auditability, access control, and evidence exports are built-in from day one—critical for regulated AI and data-sensitive workflows (Robust Intelligence).

• End-to-end craft: Brand to product to engineering under one roof ensures consistent UX across dashboards, labeling tools, lineage graphs, RBAC, and explainability.

• Incentives aligned: For select startups, our Design Capital program trades design for equity—aligning outcomes with your long-term success (Investment).

Results at a glance

Proof & Metrics

• Captions (AI video): 10M downloads; 66.75% conversion; 15.2 min median time-to-conversion; raised $60M Series C Case study · Press

• Robust Intelligence (AI security): Acquired by Cisco for $400M; enterprise governance and risk UX from inception to integration Case study · Press

• Solo.io (API and AI gateways): 31 new site pages, 512 CMS items migrated, 718 redirects; unified product design system Case study

FAQ: Governance, Evaluation, and Audit

• Do you design evaluation dashboards for LLMs? Yes. We ship test taxonomies, multi-metric views (quality, safety, latency, cost), drift windows, and A/B or canary orchestration with rollback safeguards. See LLM evaluation dashboards above.

• How do you handle governance and model risk? We design decision trails, safety gates, evidence exports, and pre‑deployment stress testing flows—patterns informed by our work on Robust Intelligence.

• Do you support RBAC and least‑privilege access? Yes. We define roles, scopes, resource hierarchies, and just‑in‑time access with approvals and automatic revocation. We add immutable audit logs and exportable evidence packs.

• Can you meet regulated workflow needs (audit, exports, retention)? Yes. Auditability, access control, and retention policies are first‑class in our IA and design systems; evidence packs and model cards are delivered by default.

• How do you work with in‑house teams? We co‑build with product, data science, security, and compliance, delivering sprint‑based UX and design systems ready for engineering handoff.

• Captions (AI video): 10M downloads; 66.75% conversion; 15.2 min median time-to-conversion; raised $60M Series C (Case study, Press).

• Robust Intelligence (AI security): Acquired by Cisco for $400M; enterprise governance and risk UX from inception to integration (Case study, Press).

• Solo.io (API and AI gateways): Delivered 31 new site pages, migrated 512 CMS items, and created 718 redirects ahead of KubeCon; unified product design system (Case study).

LLM evaluation dashboards

Design goal: make model quality legible, comparable, and actionable for product, data science, and risk stakeholders.

Key patterns we implement:

• Test taxonomy and run grouping: prompts, datasets, safety suites, and use‑case cohorts; freeze versions for fair comparisons.

• Multi-metric views: task success (exact/semantic match), human preference scores, latency, cost, safety/guardrail hits, regression deltas.

• Time slicing and drift windows: compare current vs. prior baselines; highlight statistically meaningful changes.

• A/B and canary orchestration UX: define traffic splits, eligibility rules, and rollback thresholds with clear safeguards.

• Error analysis workflows: failure clustering, outlier surfacing, prompt/template diffs, and dataset gap insights.

• Decision trails: “why we shipped this model” with signed summaries for audit.

Relevant proof points:

• Enterprise-risk UX, governance surfaces, and pre‑deployment stress‑testing flows for Robust Intelligence.

• Observability “single pane of glass” patterns adapted for data/LLM telemetry from Crystal DBA and platform‑scale monitoring work with Solo.io.

Human‑in‑the‑loop labeling and review

Design goal: collect high‑quality human signals safely and efficiently; reduce reviewer toil; guarantee provenance.

Key patterns we implement:

• Role‑aware queues: assignment by expertise, conflict‑of‑interest guards, and SLA visibility.

• Schema‑first tasks: instructions, exemplars, edge‑case libraries, and rubric scoring with inter‑rater reliability checks.

• Quality controls: gold‑sets, blind review, consensus thresholds, and escalation paths.

• Active learning UX: suggest “informative” samples; batched review with keyboardable workflows.

• Data lineage: each judgment signed with annotator, policy version, model snapshot, and source trace.

Relevant proof points:

• Developer‑centric task flows and service catalogs from Cortex inform scalable reviewer tools.

• Production reliability and policy guardrails inspired by AI security work on Robust Intelligence.

Model drift and lineage observability

Design goal: show what changed, why, and with what impact—from data through model through release.

Key patterns we implement:

• End‑to‑end lineage graph: datasets → features/prompts → training/eval artifacts → serving endpoints → apps.

• Drift detection UI: input/feature distribution shifts, output class/embedding drift, cohort impacts, and alert fatigue controls.

• Version pinning and compare: side‑by‑side metrics, schemas, hyperparameters, and environment hashes.

• Release runbooks: checklists with automated gates (coverage, bias, safety, cost) before promotion.

Relevant proof points:

• Service maturity and microservice visibility patterns from Cortex.

• Fleet‑level observability and actionability from Crystal DBA.

RBAC and audit for AI platforms

Design goal: least‑privilege access with complete, queryable audit trails across data, models, and decisions.

Key patterns we implement:

• Permission model design: roles, scopes, resource hierarchies (workspace/project/model/dataset), and break‑glass flows.

• Just‑in‑time access: temporary elevation with approvals, purpose binding, and automatic revocation.

• Data‑sensitivity tiers: PII/PHI/secret handling, masked previews, and policy‑as‑UI for denormalized access rules.

• Audit UX: immutable event logs, human‑readable diffs, exportable evidence packs, and retention policies.

Relevant proof points:

• Governance and risk surfaces for regulated enterprises via Robust Intelligence.

• Enterprise‑scale platform IA and policy clarity from Solo.io.

Enterprise explainability

Design goal: make model behavior understandable to non‑experts without compromising rigor or privacy.

Key patterns we implement:

• Multi‑layered explanations: quick summaries for end users; drill‑downs for analysts; technical detail for ML teams.

• Local and global views: per‑prediction rationales, counterfactuals, feature/prompt attributions, and segment‑level drivers.

• Reliability framing: confidence ranges, applicability domain, known failure modes, and data coverage warnings.

• Policy‑aware redaction: explain what cannot be shown and why; provide dispute and appeal flows.

• Documentation surfaces: model cards, eval suites, and change logs linked directly in‑product.

Relevant proof points:

• Clear, enterprise‑friendly product storytelling and graphics at Cortex.

• Security‑first communication for sensitive AI decisions at Robust Intelligence.

What we deliver

• Research and alignment: stakeholder maps, risk registers, data/process inventories.

• Experience architecture: IA for data, model, and policy objects; navigation for multi‑tenant platforms.

• Design systems for AI: data‑viz components, evaluation widgets, lineage graphs, alert patterns, and policy UIs.

• Prototype to production: clickable prototypes, accessibility reviews, UX specs, and design‑to‑dev handoffs.

• Governance assets: model cards, decision logs, audit exports, and change‑management playbooks.

Patterns mapped to proof

Engagement approach

• Sprint cadence: discovery → system mapping → critical‑path UX → design system → prototype validation.

• Compliance‑ready by design: auditability, access control, and evidence exports are first‑class.

• Co‑build with your teams: we integrate with product, data science, security, and compliance from day one.

AI/ML UX Sprint (3–6 Weeks)

A focused, production‑grade UX engagement for AI teams. Clear weekly milestones, governance built‑in, and engineering‑ready outputs.

• Week 1 — Discovery & System Mapping: stakeholder alignment, risk register, current stack audit (data → model → serving), KPI/guardrail definitions.

• Week 2 — Eval Dashboards: test taxonomy, multi‑metric views (quality, safety, latency, cost), drift windows, A/B or canary orchestration with rollback.

• Week 3 — Human‑in‑the‑Loop: role‑aware queues, schemas/rubrics, gold‑sets and consensus flows; provenance and evidence exports.

• Week 4 — Drift & Lineage: end‑to‑end lineage graph, version pinning/compare, release runbooks with automated gates (coverage, bias, safety, cost).

• Week 5 — RBAC & Audit: roles/scopes, just‑in‑time access, data‑sensitivity tiers, immutable audit logs and exportable evidence packs.

• Week 6 — Design System & Prototype: component library (data‑viz, evaluation widgets, alerts), clickable prototype, spec and handoff.

Deliverables by default

• Evaluation dashboards, HITL flows, drift/lineage observability, RBAC/audit UX

• IA for model/data/policy objects, UX specs, prototype, governance assets (model cards, decision logs)

Budget and engagement

• Cash: transparent, sprint‑scoped pricing shared upfront (3–6 week cadence)

• Design Capital: for select startups, up to $100K of design over 8–10 weeks for 1% equity via SAFE, then optional cash retainer (Investment; also covered by TechCrunch)

SF Bay Area availability

• Based in San Francisco; on‑site working sessions available across the Bay Area. Remote‑first delivery with scheduled in‑person workshops on request.

Sprint FAQ

• What do we get at the end of 3–6 weeks? Engineering‑ready UX: prototypes, specs, and a lightweight design system plus governance assets (model cards, audit exports, decision logs).

• Can you adapt the schedule? Yes. We tailor weeks based on your critical path (e.g., accelerate eval dashboards or RBAC first for regulated launches).

• Who owns the IP? You do. Per Zypsy Terms for Customers, clients own deliverables and inventions created for them, excluding pre‑existing Zypsy tech.

• How do you work with our ML/eng teams? We co‑build: weekly rituals, async reviews, and paired sessions with product, data science, security, and compliance.

• What if we need more than one sprint? We extend in additional sprints or continue on a monthly retainer; for eligible founders, Design Capital can kickstart the engagement.

• Do you cover compliance needs? Yes. Auditability, access control, retention, and evidence exports are first‑class in our IA and design systems.

Glossary & queries we answer

Related services

Related cases

• Captions — AI video creation at scale: 10M downloads, 66.75% conversion, $60M Series C (Case study).

• Robust Intelligence — AI security and governance UX from inception to Cisco acquisition (Case study).

• Crystal DBA — Fleet‑level observability and control for PostgreSQL with an AI teammate (Case study).

Start in SF

Based in San Francisco with Bay Area on‑site sessions available. Kick off your AI/ML UX sprint with governance and evaluation built‑in. Start in SF →

• B2B UX Audit: End-to-end review of IA, workflows, and conversion for AI/SaaS products; prioritized fixes and a roadmap you can ship. See our full Capabilities.

• Engineering for Startups: From MVPs to production systems—responsive web, SaaS, integrations, CI/CD, and QA—with designers and engineers under one roof. Explore Capabilities.

• AI Pitch Deck Design: Investor-grade story, metrics, and visuals for fundraising—paired with optional intros via Zypsy Capital when there’s a fit. Learn about Investment or Contact us.

FAQ: What AI UX deliverables are included?

By default, AI/ML sprints include evaluation dashboards (quality/safety/latency/cost), HITL labeling/review flows, drift and lineage observability, RBAC and audit UX, IA for model/data/policy objects, a lightweight design system (data‑viz, evaluation widgets, alerts), clickable prototype, UX specs, and governance assets (model cards, decision logs, audit exports). Details on typical outputs are also covered in our Capabilities.