Introduction

Data platforms win on trust, time-to-insight, and governance. Zypsy designs UX for data and AI infrastructure so operators, analysts, and executives can observe systems end‑to‑end, verify lineage, and ask complex questions with natural language—safely and explainably. Our portfolio spans AI security and governance, cloud connectivity, and data infrastructure, informing pragmatic patterns founders can ship quickly. See related work in Crystal DBA, Solo.io, Covalent, and Robust Intelligence.

Observability UX: From telemetry to decisions

Design observability for decisions, not just dashboards.

• Unify signals: model a “single pane of glass” with metric, log, trace, event, and health states aligned to business SLOs. Crystal DBA’s posture centers on fleet‑level PostgreSQL observability and control in one surface—use this pattern for multi‑tenant data fleets. Crystal DBA

• Golden signals by domain: latency, throughput, errors, saturation plus data‑specific signals (freshness, staleness, schema drift, row‑level error rates, null inflation).

• Entity‑centric navigation: datasets, pipelines, services, models, and tenants as first‑class objects with roll‑ups and drill‑downs. Solo.io’s service‑mesh and API visibility patterns generalize to data pipelines for fast triage. Solo.io

• Incident workbench: snapshot the failing state (last good version, diffs, recent deploys, upstream/downstream blast radius), with pre‑filled runbooks and reversible actions.

• Correlation, not correlation bias: pair cause hypotheses with evidence—deployment diffs, lineage deltas, workload spikes—before recommending action.

• Alert hygiene: budget by SLO burn rate; route to service owners; annotate alerts with lineage-aware impact (e.g., “affects CFO dashboard KPI X”).

Data lineage UX: Provenance, policy, and blast radius

Lineage UX must verify “what, where, who, and why.”

• End‑to‑end and column‑level graphs: express sources→transforms→models→BI; support temporal playback to explain “why this number changed yesterday.”

• Trust surfaces: show dataset contracts, quality tests, and last validation; expose data owners and policy bindings (PII, retention, residency).

• Change awareness: visualize upcoming schema changes with auto‑generated impact lists across jobs, dashboards, and NLQ definitions.

• Policy by design: consent, purpose binding, access scope, retention windows; include request/approval trails with accountable owners.

• Verifiability patterns: link to explorers, audits, or network operators when data is external or decentralized. Covalent’s network emphasizes transparent, verifiable data infrastructure—reuse its clarity for provenance UIs. Covalent

Natural Language Query (NLQ): Safe, explainable analytics

NLQ accelerates exploration only when outputs are auditable.

• Semantic clarity: bind NLQ to a governed metrics store and semantic layer; require entity disambiguation (dimensions, time windows, filters) before execution.

• Show your work: display generated SQL or API calls, cost/latency estimates, and lineage of sources; let users re‑run or edit the query directly.

• Guardrails: enforce row/column‑level security and data residency; block unsafe operations by policy; support “why can’t I see this?” explanations.

• Conversational memory with citations: persist query history with dataset versions and owners; anchor each answer to datasets, tests, and dashboards.

• NLQ‑to‑dashboard: one‑click promotion of a validated NLQ result to a governed metric or panel with owner assignment and alert hooks.

• Transparency patterns: adopt code, data, event, and transaction transparency principles so users can verify sources and logic. See Zypsy’s design principles on data transparency, code transparency, transaction history, and event transparency.

AI dashboard and governance patterns

Enterprises need auditable AI systems—governance must be visible in product.

• Model and dataset catalog: owners, versions, cards (intended use, training data summary, test coverage), approval status.

• Risk controls: pre‑deployment stress testing, continuous monitoring for drift/toxicity/PII leakage, and exception workflows. See our AI security collaboration on Robust Intelligence (acquired by Cisco), where automated risk assessment and governance UX were central.

• Decision logs and audits: immutable trails for prompts, configs, policies evaluated, and human approvals; exportable for compliance.

• Separation of concerns: builders vs. reviewers vs. approvers; least‑privilege defaults and explainable overrides.

6–8 week pilot: Design, validate, and de‑risk

Time‑boxed pilot to ship a governance‑ready analytics surface and NLQ workflow.

Delivery options: cash engagement or, for eligible founders, Zypsy’s services‑for‑equity model Design Capital (8–10 weeks, up to ~$100k of design for ~1% equity via SAFE; additional work as cash), or combine with a cash investment via Zypsy Capital ($50K–$250K) with hands‑if design support.

Evaluation KPIs

Define baselines in week 1; report weekly deltas.

• Time‑to‑insight: median time from question to verified answer (target: −30–50%).

• Query success rate: percent of NLQ prompts yielding policy‑compliant, accepted answers without analyst rewrite.

• First‑look coverage: fraction of top business KPIs observable with health, freshness, and lineage context in one view.

• Incident MTTR: median time to resolve data incidents with lineage‑aware workbench (target: −25–40%).

• Data quality: test pass rate, freshness SLA adherence, schema drift detection time.

• Governance: percent of high‑risk actions executed with approvals; audit log completeness; access policy violations per 1k queries.

• Adoption: WAU/MAU for dashboards and NLQ; creator→consumer ratio of governed metrics.

Services and scope

Zypsy integrates brand→product→web→code for data platforms. See full capabilities.

• Research: stakeholder interviews, task analyses for operators/analysts/executives, risk mapping.

• IA and object modeling: datasets, pipelines, services, tenants, models; permissioning and scopes.

• Observability UX: golden‑signal dashboards, incident workbench, alert routing, SLO views.

• Lineage UX: end‑to‑end and column‑level graphs, change‑impact previews, policy surfaces.

• NLQ experience: semantic bindings, explainability UI, guardrails, promotion to governed metrics.

• Governance and audit: approval flows, decision logs, exportable reports, role separation.

• Design system: accessible components, data‑viz patterns, states, and motion.

• Handoff: specs, event taxonomy, metrics dictionary, and QA with engineering.

Case links and relevance

• Crystal DBA: AI teammate for PostgreSQL fleets; “single pane of glass” observability and control patterns.

• Solo.io: API/AI gateways and service‑mesh leadership; large‑scale data‑viz and monitoring UX foundations.

• Covalent: modular data infrastructure with verifiable provenance—lineage and transparency cues for trust.

FAQ

• What stacks do you support? We design against common lakehouse and streaming patterns; object models generalize across warehouses, meshes, and ETL/ELT systems.

• How do you handle compliance? We design RBAC/ABAC, approvals, audit exports, and policy surfaces (PII, residency, retention) into first‑run flows.

• Can NLQ be safe for regulated data? Yes—semantic binding, policy checks pre‑execution, and “show your work” SQL/API with lineage and owners.

• Do you build or only design? We deliver production‑ready designs and partner with engineering; see our integrated capabilities.

• What if we need funding plus design? Consider Design Capital and Zypsy Capital for combined support.

• How fast can we start? Typical kickoff within 1–2 weeks; pilot completes in 6–8 weeks with measurable KPIs.

Start a pilot

If you’re building an AI/data platform and need observability, lineage, and NLQ that ships with governance from day one, start a 6–8 week pilot. Contact us at zypsy.com/contact. For equity‑for‑design or capital options, see Design Capital and Zypsy Capital.