Pilot
Validate one workflow
Run a representative source sample and inspect both usable results and exceptions. Agree on acceptance criteria before expanding.
AI Scraper / AI Search Visibility & GEO
See where your brand appears in AI-generated answers
Collect answers for a defined prompt set and compare brand mentions and available citations across supported AI engines over time.
- Define prompts and supported engines
- Collect completed answers with source context
- Analyze sampled visibility and review evidence
Prompts
Define the question panel
Answers
Collect completed results
Citations
When supplied by the engine
Compare
Keep sampling context
Who uses AI Search Visibility & GEO
Collect answers for a defined prompt set and compare brand mentions and available citations across supported AI engines over time.
01
GEO teams
Define the business question and review the collected evidence.
02
SEO strategists
Use a repeatable process to compare the observations that matter.
03
Growth analysts
Connect reliable inputs to the product or research workflow.
04
Integration owners
Map the source output into your existing application and validate required fields.
05
Workflow operators
Monitor exceptions, maintain collection context and review incomplete runs.
Why this workflow breaks at scale
Design around the failure modes that affect a usable business outcome.
Failure 01
The context gets lost
One response is a sample, not a stable ranking. Repeat comparable prompts and report visibility with the collection context.
Keep comparable observationsFailure 02
A run ends without a usable result
A missing mention, an unavailable citation and a failed task describe different outcomes. Keep them distinct in reports and comparisons.
Validate the actual outcomeFailure 03
The next team cannot verify the result
Preserve source context and reviewable evidence alongside the result. Define who handles incomplete records before production collection.
Make the handoff traceableThe data your workflow can use
Keep the collection layer and the normalized business record clearly defined.
- prompt_id
- Stable prompt id assigned or retained by your application.
- engine
- Selected supported engine for this answer observation.
- brand_mentions
- Brand names identified by your analysis layer.
- citations
- Available source references retained with the observation.
NORMALIZED RECORD · ILLUSTRATIVE
{
"prompt_id": "prompt_sample_01",
"engine": "selected_supported_engine",
"brand_mentions": [
"Example Brand"
],
"citations": [
"https://example.com/source"
]
}Suggested downstream record, not a literal Scrapeless API response. Field extraction and normalization belong in your application.
Completed answer results from supported AI engines; available response fields and citations vary by engine.
Example in practice. Track whether a brand is mentioned for a fixed set of category discovery prompts.
How this workflow runs
A focused sequence from approved input to a verified result.
- 01
Define prompts and supported engines
Start with: A fixed prompt panel, engine selection and collection schedule. Define the minimum usable result before collection.
- 02
Collect completed answers with source context
Completed answer results from supported AI engines; available response fields and citations vary by engine. Record failures and partial results separately.
- 03
Analyze sampled visibility and review evidence
Completed answer coverage, prompt consistency and evidence-backed analytical labels. Retain evidence for records that enter the next stage.
Built for the way your team works
Product capabilities support the workflow; your application defines what success means.
- Prompt → task → answer
Use a task-based workflow
Submit a prompt to a supported AI engine, then retrieve or receive the completed result. Track task state before accepting an observation.
- Engine · prompt · time
Keep engine-specific context
Choose supported engines and review their current parameters. Preserve available engine and collection context for later comparisons.
- Answer + available citations
Collect available references
Retain citation URLs when present in the result. Availability and structure depend on the engine and response; do not manufacture missing sources.
- Collect → analyze → review
Build your analysis layer
Compute brand visibility, topic labels and evaluation scores in your own pipeline. Keep raw answers accessible for review of every derived label.
Plan the cost of your workflow
Estimate prompt count, selected engines and repeated sampling before choosing a plan. Verify current engine-specific pricing in the account.
Try for Free
Production
Measure real consumption
Completed answer coverage, prompt consistency and evidence-backed analytical labels.
Scale
Scope the next stage
Bring your workload mix, operating window and observed consumption to discuss the setup that fits your requirements.
These are workload stages, not named subscription plans. Current rates and account limits apply.
Choose how to run the workflow
Compare responsibilities and operating effort using your own representative workload.
Workflow responsibility
With AI Scraper
Self-managed stack
Manual process
Execution
AI answer collection
Your team runs and maintains the execution layer.
An operator repeats each collection or browser step.
Business rules
Prompt design, sampling, entity matching, scoring, trend analysis and review workflows.
Implemented in your application.
Applied by the researcher or operator.
Acceptance
Keep outcome and content checks in your workflow.
Build validation and exception handling.
Review the captured evidence.
Cost planning
Measure product consumption plus downstream processing.
Measure infrastructure and maintenance effort.
Account for time spent per completed task.
Connect it to the stack you already run
Keep business logic and analysis close to the systems that consume the result.
Task API
Python
Node.js
Your analytics pipeline
Prompt design, sampling, entity matching, scoring, trend analysis and review workflows. Open the product documentation for current setup and supported options.
Explore more AI Scraper workflows
Related workflows using the same product, each with its own inputs and acceptance criteria.
You are here
AI Search Visibility & GEO
Collect answers for a defined prompt set and compare brand mentions and available citations across supported AI engines over time.
Brand Monitoring in AI Answers
Capture answers to brand and category prompts, then review the wording, claims and available source references that users may encounter.
AI Answer Competitive Intelligence
Collect matched prompt responses and analyze which competitors appear, how they are described and which sources support the comparison.
AI Citation Tracking
Collect available citation references and connect them to prompts, answers and collection times for source-level analysis.
LLM Research & Evaluation
Collect prompt-response samples from supported AI interfaces and preserve the context needed for comparative research and evaluation.
AI-informed Content Strategy
Collect answers to audience questions and review recurring topics, descriptions and cited sources to prioritize useful content work.
AI Reputation Monitoring
Capture brand-related answers and supporting references so communications teams can examine inaccuracies and keep a documented review trail.
Looking for more AI Scraper workflows?
Explore more use cases across AI visibility, brand monitoring, and source analysis.
Questions teams ask before they build
Scope the first run with clear expectations.
What do we need to get started?
What does AI Scraper provide?
Which parts do we build ourselves?
What can make this workflow misleading?
What boundaries should we plan around?
How should we evaluate a pilot?
Put AI Scraper to work for your team
Bring your sources, expected answer tasks and acceptance criteria. Start with a focused pilot, then expand on evidence.