AI Dashboard Design Services — Governance, RAG Evaluation & HITL

Zypsy provides AI dashboard design services for governance, model evaluation, RAG QA, and human‑in‑the‑loop workflows—turning complex model signals into clear decisions, actions, and audit trails.

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AI Dashboard Design: Governance, RAG Evaluation & HITL

Zypsy designs enterprise AI dashboards for governance, RAG evaluation, and human‑in‑the‑loop workflows—turning complex model behavior into clear decisions, actions, and audit trails.

AI/ML UX agency for governance, dashboards, and NLQ

Last updated: October 2025 Quick links: AI/ML UX patterns · HITL UX · NLQ UX · Case study: Robust Intelligence · Case study: Captions

Related work

• Robust Intelligence — AI security and governance UX from inception through Cisco acquisition; dashboards for risk assessment, evaluation evidence, and approvals. See the case: Robust Intelligence

- Captions — AI creator platform rebrand and product UX at scale; 10M+ downloads and $100M+ raised, with a unified design system delivered in 2 months. See the case: Captions

Introduction

AI products live and die by how well teams and customers can see, question, and govern what the system is doing. Zypsy designs data‑dense dashboards and analytics surfaces that turn complex model and system behavior into clear decisions, actions, and audit trails. Our work spans AI security and governance, microservice and API observability, database fleet health, and multi‑audience enterprise platforms—with shipped examples in sectors like AI risk, cloud connectivity, and data infrastructure.

What we deliver

• Data‑dense visualization systems that prioritize signal over noise, with progressive disclosure, metric grammar, and scalable component libraries. See our product and monitoring design work for Solo.io and enterprise‑ready brand/product systems for Cortex.

• Model and AI governance dashboards: risk assessment results, evaluation artifacts, lineage, approval workflows, policy mappings, and audit trails. Informed by our long‑running collaboration with Robust Intelligence from inception through Cisco acquisition.

• Natural‑language query (NLQ) UX: chat‑ and prompt‑driven analytics, schema‑aware disambiguation, safe defaults, and interpretable output with drill‑downs.

• Role‑based dashboards for executives, operators/SREs, data science, and risk/compliance, each with tailored KPIs, actions, and time horizons.

• Multi‑tenant and enterprise patterns: workspace scoping, granular permissions, audit logging, and export/embedding options—vetted on complex platforms like Crystal DBA and Solo.io.

• End‑to‑end product partnership: brand, product, web, and engineering under one roof; we deliver systems that are coherent in narrative and execution across surfaces. See our capabilities overview here.

Patterns for dense-data UIs

• Layout: two‑tier overview→detail, small‑multiple grids for service/model cohorts, left rail for persistent context, and timeline‑centric incident strips.

• Visual encodings: sparklines for trend density; anomaly ribbons and prediction intervals; severity chips; distribution plots for drift/quality; health summaries for fleets.

• Interaction: cross‑filtering and brush/zoom, pin‑to‑compare for A/B or canary cohorts, schema‑aware search, and saved views for reproducibility.

• Systemization: tokenized theming, accessibility contrast targets, responsiveness rules, and component specs that dev teams can implement quickly.

• Application examples: service mesh and API telemetry visualizations for Solo.io; enterprise‑friendly product graphics and information architecture for Cortex.

Governance states and workflows (AI/ML)

• Standard state machine: Draft → Evaluated → Approved → Monitored → At‑Risk → Blocked/Retired.

• Evidence surfaces: evaluation run cards (datasets, metrics, thresholds), red‑team/stress‑test results, lineage (dataset→experiment→model→deployment), policy mappings, and exception documentation. Our governance UI thinking is informed by AI risk and pre‑deployment testing work with Robust Intelligence.

• Actions and controls: request/approve with rationale, temporary overrides with expiry, automatic rollback conditions, reviewer assignment, and immutable decision logs.

• Compliance visibility: who changed what/when, data‑use disclosures, region/PII constraints, and export of signed reports for audits.

RAG Evaluation & HITL: Metrics and UX Patterns

Designing trustworthy QA for retrieval-augmented generation (RAG) requires clear metrics, repeatable run cards, and human-in-the-loop (HITL) decision rights.

Core metrics (RAG)

• Faithfulness: Does the answer stay grounded in retrieved sources without introducing unsupported claims?

• Contextual precision/recall: Do retrieved passages contain the specific facts needed (precision) and are key facts covered (recall)?

• Answer relevancy: Does the answer resolve the user’s question and respect scope/constraints (e.g., time range, entity, policy)?

• Evidence completeness: Are citations/references sufficient to verify claims and enable drill-down?

• Sensitivity & safety flags: PII exposure, policy-violating content, risky actions, or compliance scope breaches.

Online evaluators and regression

• Lightweight online checks: retrieval coverage, hallucination heuristics, citation presence/consistency, latency/cost budgets.

• LLM-as-judge with calibrated rubrics: prompt the judge model with task cards and gold references; store scores and rationales alongside runs.

• Canary prompts and targeted suites: pin high-risk intents (e.g., finance, healthcare, security) for every deploy.

• Release gates: block promotion when faithfulness or relevancy dips below thresholds or safety flags rise above limits.

UI rubrics for HITL review

Evaluation run cards (example JSON)

{
  "run_id": "2025-10-07T18:41:22Z_9f31",
  "dataset_id": "governance_eval_v3",
  "model_version": "gpt-4o-mini-2025-09",
  "retrieval_config": {
    "k": 6,
    "reranker": "cross-encoder-msmarco",
    "filters": {"time_range": "365d", "tenant": "acme"}
  },
  "query": "Summarize failed model evals this week with failing policies and owners.",
  "retrieved_docs": [
    {"id": "eval_8342", "source": "s3://evals/ri/q3", "hash": "a9c..."},
    {"id": "policy_map_12", "source": "confluence/policies", "hash": "c03..."}
  ],
  "answer": "3 evaluations failed this week (E-8342, E-8349, E-8355) due to fairness F1<0.78 and PII leak risk. Owners: DS-Platform (M. Chen), Risk (A. Patel). Mitigations proposed...",
  "references": [
    {"doc_id": "eval_8342", "spans": [[120, 188]]},
    {"doc_id": "policy_map_12", "section": "Fairness-F1-threshold"}
  ],
  "metrics": {
    "faithfulness": {"score": 0.92, "threshold": 0.9},
    "context_precision": {"score": 0.88, "threshold": 0.85},
    "context_recall": {"score": 0.86, "threshold": 0.85},
    "answer_relevancy": {"score": 0.94, "threshold": 0.9},
    "safety_flags": {"pii": false, "policy_violation": false}
  },
  "timings": {"retrieval_ms": 132, "generation_ms": 842},
  "cost": {"input_tokens": 3121, "output_tokens": 412},
  "human_review": {
    "required": true,
    "assignee": "risk_reviewer_pool",
    "sla_minutes": 60,
    "decision": "approved",
    "notes": "Evidence spans align; thresholds correct; proceed to share report."
  }
}

Orchestrating evaluators and HITL (operational blueprint)

1) Pre-merge (offline): run unit evals on prompts + retrieval; block PRs failing faithfulness/relevancy thresholds. 2) Pre-deploy (staging): execute regression suites and canaries; generate release scorecard and require approver sign-off. 3) Shadow prod (online): sample N% of real queries to evaluators; route low scores or safety flags to HITL queue. 4) HITL queue: role-routed reviewers (Risk, DS, Support) apply UI rubric; approve, request revision, or block. 5) Post-deploy: weekly drift report on retrieval precision/recall, answer relevancy, safety trendlines; auto-create issues for regressions.

HITL workflow patterns

• Routing: map intents to reviewer pools; escalate by severity and customer tier.

• Controls: temporary overrides with expiry; immutable decision logs; rollback triggers when run-rate failures exceed budget.

• UX: side-by-side evidence view, citation jump-links, metric badges, and “why this was flagged” explainer.

RAG Evaluation (RAGAS, Tru

Lens, LangSmith) & Governance We design evaluation and governance dashboards that work with common frameworks—RAGAS, TruLens, and LangSmith—to visualize metrics, compare runs, and capture evidence for approvals.

• Integrations by design: ingest scores and rationales (LLM-as-judge or heuristics), retrieved context, and citations from RAGAS/TruLens; track datasets, prompts, runs, and experiments from LangSmith.

• Side-by-side compare: evaluate cohorts across faithfulness, relevancy, and context precision/recall; pin canaries and show deltas before/after changes.

• Release gates: bind evaluator thresholds to approve/block states with immutable logs and policy mapping; export audit-ready reports.

• Evidence continuity: store artifacts (run cards, references, judge rationales) alongside governance decisions for full lineage from dataset → eval → model → deployment.

Mini FAQ

• What’s the difference between faithfulness and relevancy? Faithfulness checks if claims are supported by retrieved evidence; relevancy checks if the answer addresses the user’s question and scope.

• Which tools do you integrate? We integrate with client-standard stacks, including RAGAS, TruLens, and LangSmith for evaluators, run tracking, and experiment comparison.

RAG/HITL KPI glossary

• Faithfulness: Degree to which answers are grounded in retrieved sources without unsupported claims. Common scale: 0–1 or 1–5; gate on minimum threshold.

• Answer relevancy: How well the response addresses the user’s intent and constraints (time, entity, policy). Measured via LLM-as-judge or task-specific rubric.

• Context precision: Share of retrieved tokens/passages that are actually used to support the answer. High precision = less retrieval noise.

• Context recall: Coverage of required facts in the retrieved set. High recall reduces the chance of omissions and hallucinations.

• Hit@k: Percent of queries where at least one supporting passage appears in the top-k retrieved results. Useful for retrieval tuning.

• MRR@k (Mean Reciprocal Rank): Average of 1/rank for the first relevant passage within top-k. Rewards higher placement of relevant context.

• nDCG@k: Normalized Discounted Cumulative Gain over top-k passages. Captures graded relevance and ordering quality.

• Citation consistency: Alignment between cited spans and claims in the answer. Penalize broken links, mismatched sources, or uncited assertions.

• Evidence completeness: Sufficiency of citations to verify each major claim; favors deep links/spans over document-level refs.

• Escalation rate (to HITL): Fraction of queries routed to human review due to low metric scores or safety flags. Track by intent/severity.

• Safety flag rate: Rate of PII, policy, or risky-action flags triggered per 100 queries. Segment by model, dataset, tenant.

• Latency and cost per answer: Online performance budgets across retrieval, generation, and post-processing; include token and infra costs.

RAG/HITL FAQs

• What’s the difference between faithfulness and relevancy? Faithfulness checks if claims are supported by evidence; relevancy checks if the answer addresses the question and scope.

• Which retrieval metric should we start with? Start with Hit@k and MRR@k; add nDCG@k when you have graded labels and care about ranking across multiple relevant passages.

• How do we set release gates? Define thresholds for faithfulness, relevancy, and context recall; block deploys when scores dip below gates or safety flag rate exceeds limits.

• When should HITL be required by policy? For high-risk intents (e.g., financial, healthcare, security), when safety flags trigger, or when evaluator confidence is low.

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  "mainEntity": [
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      "@type": "Question",
      "name": "What’s the difference between faithfulness and relevancy?",
      "acceptedAnswer": {
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Natural-language query (NLQ) UX

• Intent patterns: metric summaries ("Latency p95 last 7 days by cluster"), comparisons ("drift vs. prior model"), diagnostics ("top features driving error today"), and governance queries ("models approved in Q3 with human feedback").

• Disambiguation: expose the semantic layer (entities, measures, time grain), auto‑suggest valid dimensions/filters, preview generated SQL/DSL before run, and offer example prompts tied to the active context.

• Safety: role‑scoped data access, query cost/time estimates, result confidence cues, and quick pivots to pre‑built dashboards when NLQ is insufficient.

• Example prompts and responses:

• "Show p95 latency and error rate for service gateway‑api by region last 24h; flag anomalies" → Overview chart + anomaly table + link to incident detail.

• "List deployments currently in At‑Risk with failing fairness threshold" → Governance list with owners, failing checks, and recommended actions.

• "Compare traffic and SLO burn per tenant for Enterprise plan vs. Pro" → Small multiples + SLO burn‑down + top offenders.

• "Which models moved from Draft to Approved this week?" → Timeline of approvals with reviewer and evidence links.

• "Top three queries degrading Postgres CPU on cluster east‑prod" → Ranked workload view with execution plans and fix suggestions; aligns with fleet views used in Crystal DBA.

Role-based dashboards (starter blueprint)

Data quality, lineage, and observability

• Fleet‑level observability and a "single pane of glass" matter when teams run many services or databases. We design cohesive cross‑asset views—alerts roll‑ups, noisy‑signal suppression, and drill‑downs—that accelerate triage and root‑cause analysis, aligned with the fleet management principles showcased in Crystal DBA and the service mesh/API leadership of Solo.io.

Process and timeline

• 0–1 week: Discovery and data/metric inventory. Stakeholder interviews by role; map governance requirements; audit existing visuals and semantic layer.

• 2–3 weeks: Information architecture and system scaffolding. Define metric grammar, layout grid, color/shape encodings, and accessibility targets.

• 3–5 weeks: Dense‑data patterns and NLQ flows. Prototype cross‑filters, anomaly views, lineage, approvals, and NLQ disambiguation + SQL/DSL preview.

• 5–8+ weeks: Hardening and handoff. Component library in Figma, redlines/specs, empty/error state patterns, performance budgets, and dev pairing. Typical sprints mirror our 8–10 week investment‑grade engagements described in Design Capital.

Engagement models

• Cash projects: fixed‑scope or retainer across brand→product→engineering; see our capabilities.

• Services‑for‑equity: up to ~$100k of brand/product work over 8–10 weeks for ~1% equity via SAFE through Design Capital.

• Venture support: optional cash investment paired with design on a "hands‑if" basis via Zypsy Capital.

Relevant work

• AI security and governance UX: Robust Intelligence.

• Engineering platform and enterprise repositioning: Cortex.

• API/service connectivity and observability patterns: Solo.io.

• Database fleet visibility and AI teammate for DBAs: Crystal DBA.

Proof gallery: dense-data modules

• AI dashboard — Governance risk heatmap

• Alt text: AI dashboard showing governance risk heatmap with severity chips, policy status, and approval workflow states.

• Caption: Governance module visualizing model risk by policy control, with role-based actions for request/approve and audit trail export.

• Downloadable PNG: ai-dashboard-governance.png

• Model lineage graph — Evidence and approvals

• Alt text: End-to-end lineage graph connecting dataset → experiment → model → deployment with attached evaluation artifacts and approvals.

• Caption: Governance evidence surface mapping lineage, eval runs, and exception handling to support audit-ready decisions.

• Downloadable PNG: model-lineage-graph.png

• NLQ explain — Schema-aware query with SQL/DSL preview

• Alt text: Natural-language query (NLQ) interface showing semantic disambiguation, generated SQL preview, and drill-down results.

• Caption: NLQ pattern that keeps queries safe by role, explains outputs, and pivots to curated dashboards when needed.

• Downloadable PNG: nlq-explain.png

• Role-based executive overview — Compliance and impact

• Alt text: Executive role-based dashboard summarizing adoption, revenue impact, compliance posture, and policy exceptions.

• Caption: Role-based overview tailored for execs; pairs KPIs with governance insights and shareable reports for stakeholders.

• Downloadable PNG: role-based-exec-overview.png

Acceptance criteria and handoff package

• Role‑mapped IA, metric dictionary, and semantic layer glossary.

• Figma component library with density‑ready variants and documentation.

• NLQ intents, prompt templates, and disambiguation flows with example datasets.

• Governance state machine, approval/exception flows, and audit export templates.

• Redlines and implementation notes; empty/load/error states; performance budget targets.

How to start

Tell us about your product, roles, and data sources via our contact form. We’ll propose scope, timeline, and the right engagement model, then begin with a focused discovery sprint.