Getting Started

CiteAura Workspace Guide

CiteAura is a Generative Engine Optimization platform that audits, measures, and improves brand citations, mentions, and visibility in generative AI engines, then closes the loop with engineering tickets and verification.

Maintained by CiteAura Editorial Team · Updated 2026-09-12

This guide shows how to turn a public website into an evidence-based GEO workspace: crawl the site, record labeled AI answers, assign tickets, verify changes, and export a delivery pack.

New projects queue an initial diagnostic automatically. AI sampling runs only when sampling access is available and sampling is enabled. Without it, the site diagnostic can run while AI visibility remains unmeasured.

Key facts

Fact	Value
New workspace trial	7 days
Model usage charges	Separate from the subscription; depend on provider, model, and call volume.

Step 1. Create a workspace and add a brand

Register at citeaura.com/app, then enter the canonical HTTPS URL for the brand. CiteAura creates the project and queues the initial crawl and analysis pipeline.

Step 2. Review the measurement inputs

Open Diagnostics → Target Questions and Diagnostics → Brand Fact Library. Keep questions representative of real buyer intent and give material facts a source and verification date.

Step 3. Connect model providers

Open Settings → Model Keys (BYOK). Configure one or more built-in providers, or add an OpenAI-compatible endpoint. Use Test to validate each saved key before sampling.

Step 4. Run and inspect AI sampling

Choose Fill Sample Gaps from Overview to complete evidence cohorts, or Sample Matrix Now from Monitor to start a full measurement run. Review the exact prompt, raw answer, sampling mode, citations, and cohort before interpreting any score.

Step 5. Execute, verify, and deliver

Prioritize evidence-backed tickets in Execution → Action Tickets, deploy the relevant assets, then run Verify Changes. Use Delivery → Client Delivery Packs to build the current handoff package.

Workspace Workflow

The workspace follows a repeatable measurement and execution loop. Each view has a distinct role; use the evidence from one stage as the input to the next.

Stage	What happens	Primary view
Establish	Crawl the canonical site and derive the initial question bank, facts, and audit evidence.	Overview
Measure	Record model answers for a versioned question set and classify each sampling mode.	Monitor
Diagnose	Inspect technical extraction issues, observed framing, questions, and official facts.	Diagnostics
Execute	Assign action tickets, edit generated assets, and prepare outreach or publishing drafts.	Execution
Verify	Re-crawl the site, evaluate acceptance checks, and reopen regressed tickets when required.	Closed-Loop Verify
Deliver	Compile the latest audit, ticket, verification, comparison, and asset files into a ZIP package.	Delivery

One active job per project: while a project job is queued or running, commands that start another job may remain disabled or return a conflict. Wait for completion or open the telemetry panel to inspect a failure.

Measurement Concepts

CiteAura records observations from a defined question set. Results describe that sample cohort; they are not a guarantee of how every user, model version, region, or future answer will behave.

Sampling modes

Label	Evidence source	Interpretation
API · Model knowledge	A provider API response without live retrieval.	Measures parametric model knowledge for the submitted question.
API · Web-grounded retrieval	A provider API response with web retrieval enabled.	Can produce source URLs used in Citation Sources.
Manual · Product surface	A transcript captured from a consumer product interface.	Kept distinct from API observations because the product surface can behave differently.

Core metrics

Mention rate: the share of successful, eligible answers that mention the configured brand. Questions containing the brand name are excluded from this calculation.
Reported rank: the matrix uses the median recorded brand position among qualifying mentions, although its column is labeled Avg Rank. Missing ranks remain unmeasured.
Sample count: the number of observations supporting the displayed metric.
Citation share: the share of captured source URL mentions attributed to a domain within web-grounded samples.
Unmeasured: no qualifying observation is available. It does not mean zero visibility.

Question sets and cohorts

Changes to Target Questions create a different measurement input. Compare trends only when question scope, provider configuration, sampling mode, and market are sufficiently consistent. Raw answers and cohort labels are the audit trail behind aggregate metrics.

Measurement is not placement. Adding an API key pays the provider for measurement calls. It does not alter model weights, buy recommendations, or guarantee citations.

Model Keys & Providers

Workspace owners manage model keys. CiteAura supports ten built-in providers: OpenAI, Anthropic, Google, xAI, Perplexity, DeepSeek, Zhipu GLM, Doubao, Kimi, and MiniMax, plus custom OpenAI-compatible HTTPS endpoints. BYOK usage is billed by the provider; sampling availability depends on the project market and configuration.

1. OpenAI

Visit the official OpenAI Developer Platform (platform.openai.com/api-keys).
In the dashboard, click Create new secret key.
Select your organization or project, assign permissions, and copy the secret key.
In CiteAura, open Settings → Model Keys (BYOK) → OpenAI and paste the key.

2. Anthropic

Visit the official Anthropic Console (console.anthropic.com).
Navigate to API Keys and click Create Key.
Name your key, copy the sk-ant-... token, and paste it into CiteAura under Settings → Model Keys (BYOK) → Anthropic.

3. Google

Visit the official Google AI Studio (aistudio.google.com/apikey).
Click Create API key and choose an existing or new Google Cloud Project.
Copy the generated AIzaSy... key and paste it into CiteAura under Settings → Model Keys (BYOK) → Google.

4. xAI

Visit the official xAI Console (console.x.ai).
Navigate to API Keys in your team management console.
Click Create API Key, set access permissions, copy the key, and paste into CiteAura under Settings → Model Keys (BYOK) → xAI.

5. Perplexity

Visit the official Perplexity API Settings (perplexity.ai/settings/api).
Ensure billing is active on your API account, then click Generate under API Keys.
Copy the pplx-... token and paste it into CiteAura under Settings → Model Keys (BYOK) → Perplexity.

6. DeepSeek

Visit the official DeepSeek Platform (platform.deepseek.com/api_keys).
Create an API key and copy the token.
In CiteAura, open Settings → Model Keys (BYOK) → DeepSeek and paste the key. DeepSeek’s official API is parametric knowledge only; it does not enable live web search.

Custom OpenAI-compatible providers

In Model Keys, choose Add Provider to configure a custom endpoint.
Enter a provider name, the HTTPS API base URL, and the exact model ID. Do not append /chat/completions to the base URL.
Enter the API key and save. CiteAura tests the endpoint before retaining the configuration.

Key and data handling

Saved API keys are encrypted at rest with AES-256-GCM and made available during authorized engine operations, then removed from that execution context.
API responses expose masked key identifiers, not the full secret.
CiteAura does not use workspace content to train its own models. Provider processing, retention, and training controls remain subject to the selected provider account and policy.
Deleting a saved key prevents later jobs from loading it. It does not cancel running jobs or revoke the key at the provider; revoke it there when access must end immediately.

Cost control: provider-side quotas, budgets, and rate limits are the final control for BYOK spend. Configure them in each provider account before enabling scheduled monitoring.

Monitor Visibility & Citations

AI Visibility Matrix

The matrix separates results by provider and sampling mode. Compare mention rate, reported rank, eligible sample count, and measurement status within comparable cohorts. Use Sample Matrix Now to queue new sampling.

Open Raw AI Sample Answers & Citations before acting on an aggregate. The replay contains the exact question, answer, provider, mode, detected brand identity, and returned source URLs.

Citation Sources

This view counts only source URLs returned by web-enabled model samples. Parametric answers without URLs do not create citation evidence. Domain share is calculated within the captured cohort, not across the whole web.

Use Draft Outreach when a cited third-party domain is relevant and there is a factual contribution worth proposing. Treat the list as observed evidence, not a directory placement guarantee.

Competitor Benchmark

Add direct competitors to compare recommendation frequency in recorded answers. Keep the set small and commercially relevant. A competitor configured by a user is different from a competitor observed in model evidence; CiteAura preserves that distinction.

Diagnostics & Brand Inputs

Site-wide GEO Audit

Review crawlability, page roles, semantic structure, extraction blocks, and structured data. Page grades summarize applicable checks; skipped or non-applicable checks are shown separately. Use Re-run Audit after the public site changes.

Perception Gaps

Perception Gaps summarizes recurring descriptors found in recorded answers. It reflects sampled phrasing and does not independently decide whether a statement is factually correct. Compare it with the Brand Fact Library and raw answers.

Target Questions

The question bank defines what the model matrix measures. Include real discovery, comparison, category, and problem-oriented questions. Avoid near-duplicates and prompts written only to force the brand name into an answer.

Trend discipline: changing the question set changes the measurement basis. Record the question-set version and avoid treating two materially different cohorts as a strict before-and-after comparison.

Brand Fact Library

Maintain official claims used by generated content and delivery assets. Prefer a source URL and verification date for material claims. The library is a controlled input; it does not override contrary evidence returned by a model.

Action Tickets & Execution

CiteAura maps audit and sampling evidence to a standard ticket library. The current project total can change when evidence changes, users add project-specific tickets, completed items regress, or a rerun preserves customized work.

Action Tickets

Prioritize by impact and effort, then open a ticket to review its rationale, target page, owner, requested update, and acceptance criteria.
Assign an operational status only when it reflects the real implementation state.
Attach decisions to the relevant question IDs so later verification remains explainable.
Create a manual ticket when project evidence requires work outside the standard library.

Sampling Evidence Workbench

Use the Workbench to compare recorded answers for a selected question and inspect the citations available to each response. It is an evidence review surface, not a second measurement engine.

Assets & Templates

Generated project files can include machine-readable discovery content, structured data, deployment notes, and editable text assets. Review every claim and destination before saving or deploying an asset.

Media Outreach & Publishing Destinations

In Settings → Media Outreach, review pitch drafts and confirm sending through configured SMTP. In Settings → Publishing Destinations, configure channels; for GitHub, select approved assets to create a branch and pull request. CiteAura does not merge that PR or deploy it automatically.

Using /llms.txt

/llms.txt is an emerging machine-readable discovery convention, not a universal indexing standard. It can present official facts and links in a concise Markdown file, but support varies by crawler and provider.

# Brand Name
> Concise, verifiable description of the organization.

## Official Resources
- Product documentation: https://example.com/docs
- Pricing: https://example.com/pricing
- Contact: https://example.com/contact

## Verified Facts
- Fact supported by an official source and review date.
          

Review the generated file in Execution → Assets & Templates.
Remove unsupported claims and confirm every URL is canonical and public.
Deploy it at https://example.com/llms.txt with a successful text response.
Re-crawl and verify availability. Do not interpret availability as proof that a model consumed it.

Verification & Delivery

Closed-Loop Verify

Run verification after deploying website changes. It re-crawls and audits the site, then checks tickets against the latest audit and existing metrics. Results can be pass, fail, or manual review; failed checks can reopen completed tickets. Verification does not collect fresh AI answers.

Deploy the selected ticket changes to the canonical public site.
Open Execution → Closed-Loop Verify and choose Run Verification.
Inspect the verification history and failed acceptance checks.
Run a separate AI sample when you also need a fresh model-answer cohort.

Client Delivery Packs

Open Delivery → Client Delivery Packs and choose Build Diagnostic Pack. The downloadable ZIP contains the current diagnostic, tickets, evidence, and assets. Check whether it is a review, diagnostic, or implementation pack; templates and unmeasured results do not become deployment-ready simply by being packaged.

On Agency or Enterprise, an owner can configure Settings → White-Label Branding before building. Rebuild after material workspace changes. A diagnostic pack can be produced before implementation and verification are complete; its limitations remain disclosed.

Workspace Administration

Settings view	Use it for	Important behavior
Brand Settings	Update the canonical website URL used by future crawls.	Confirm redirects and domain ownership before changing it.
Automated Schedule	Run monitoring every 1, 7, 14, or 30 days.	Scheduled cycles consume plan allowance and provider quota. Verification remains an explicit step.
Team Members	Invite Owners, Editors, and Viewers.	Owners administer the workspace, Editors execute project work, and Viewers have read-only access.
Billing & Plans	Review subscription state, usage, limits, and billing interval.	BYOK provider charges are separate from the CiteAura subscription.
Enterprise Security	Configure an OIDC identity provider and review access audit events.	CiteAura exposes technical controls but is not currently SOC 2 certified.
Backup Snapshots	Owners can create and restore project-file snapshots when backup storage is configured.	Restore merges snapshot files into the project and can overwrite matching paths after confirmation. Files absent from the snapshot are retained; this is not a full rollback.

FAQ

How to start a CiteAura diagnostic?

Create a workspace, add a public website, configure sampling access, and inspect labeled results. You can build a diagnostic pack before implementing fixes; run verification after deployment.

Does CiteAura guarantee AI mentions?

No. CiteAura does not guarantee AI mentions, rankings, or citations. It produces diagnosis, tickets, and verification evidence.

Jobs & Troubleshooting

Job states

Queued: the command is waiting for a worker.
Running: the worker is executing the displayed pipeline stage.
Done: the task completed and the affected views can be refreshed.
Failed: open telemetry for the recorded error. Use Retry Job when the action supports retry.

Common checks

Symptom	What to check
Engine is Unmeasured	Confirm a key is configured and tested, the question bank is not empty, and the latest sample completed successfully.
No citation sources	Confirm the cohort includes web-grounded samples that actually returned source URLs. Parametric samples do not populate this view.
Provider authentication error	Re-test the key, provider account, model permission, custom base URL, and exact model ID.
Provider rate or quota error	Review provider-side billing, rate limits, and budgets before retrying.
Project command is unavailable	Check your role, active trial or subscription, usage limits, and any queued or running job. A project allows only one active job at a time.
Delivery pack looks outdated	Finish the latest audit, ticket, or verification job, then choose Build Diagnostic Pack.

Crawler Edge Caching & Proxy Configuration

When robots.txt or discovery file updates fail to take effect, edge caches or proxy directive ordering are typically responsible:

# Caddyfile example: ensure robots.txt is served before reverse proxy handlers
www.example.com {
  root * /srv/www
  handle /robots.txt {
    file_server
    header Cache-Control "public, max-age=3600, must-revalidate"
    header Content-Type "text/plain; charset=utf-8"
  }
  handle {
    reverse_proxy localhost:3000
  }
}
          

Cloudflare Edge Purge: When using Cloudflare CDN, purge the path directly via Caching → Custom Purge for https://yourdomain.com/robots.txt, and set origin headers to Cache-Control: max-age=3600, must-revalidate.

Crawler Log Analysis

Inspect server access logs to confirm AI crawlers (GPTBot, OAI-SearchBot) are reaching your origin and receiving valid HTTP 200 responses:

# Filter Nginx access logs for OpenAI crawler requests
grep -Ei "gptbot|oai-searchbot" /var/log/nginx/access.log | tail -n 20

# Filter Caddy systemd journal logs
journalctl -u caddy --since "7 days ago" | grep -Ei "gptbot|oai-searchbot"
          

When reporting a persistent failure, include the project, job ID, action, stage, and telemetry error. Do not include full API keys or access tokens.