AEO Tools with API Access and Data Export: A Buyer’s Guide

AI visibility is graduating from experimental pilot to boardroom metric. 

As it does, the data needs to flow into the same infrastructure that powers every other performance channel, like marketing dashboards, BI tools, revenue attribution models, and executive reporting. AEO platforms without extensible APIs create data silos at exactly the moment enterprise teams need the opposite.

This guide evaluates four leading AEO platforms — Scrunch, Adobe LLM Optimizer, Bluefish, and Profound — specifically through the lens of API access, data export, and integration capabilities. It covers what each platform exposes programmatically, how that data moves into enterprise systems, and what questions to ask vendors before committing. 

Assessments are based on publicly available product documentation, developer documentation, published pricing and packaging information, third-party reviews, and market research. Here’s a brief overview of each platform before we dive into more details. 

What to look for in an AEO platform’s API and data export capabilities

Before evaluating specific vendors, here’s what actually separates a mature API from a marketing checkbox.

Public documentation. Can you read the API documentation before you buy? Public developer docs are a signal of maturity and confidence in the product. If the vendor requires an NDA or enterprise contract just to see endpoint schemas, that’s a meaningful procurement friction point and a sign that the API may still be early. Look for: published endpoint references, authentication guides, quickstart tutorials, response schemas, rate limit documentation, and code examples in multiple languages.

Data granularity. Enterprise data teams need both aggregated metrics and row-level data. Aggregated metrics — brand presence percentages, sentiment scores, position scores over time — power dashboards and trend reporting. Row-level data — individual AI responses, full text, citations, per-response competitor analysis — powers deep analysis, audits, custom modeling, and compliance workflows. A platform that only exposes aggregated data limits what your BI and data engineering teams can build.

API scope and permissions. Enterprise organizations need granular control over what each API key can access. Look for: brand-scoped keys (so an agency or internal team only accesses their data), role-based permissions on key creation, support for multiple concurrent keys, and the ability to restrict keys to read-only versus configuration access. This matters most for agencies managing multiple clients and enterprises with multiple brands or business units sharing one platform.

Warehouse and BI compatibility. The API should produce clean, structured data that flows into your existing data infrastructure without heavy transformation. Look for: JSON responses with consistent schemas, pagination support for large datasets, date-based filtering for incremental ETL, and documentation for loading data into common warehouses (BigQuery, Snowflake, Redshift, Databricks). Native BI connectors — Looker Studio, Tableau, Power BI — reduce time-to-value for teams that don’t want to build custom pipelines.

Export formats. Not every use case requires an API. For stakeholder reporting, ad-hoc analysis, and executive summaries, in-platform export matters. Look for: CSV, Excel, and PDF exports from dashboards and reports, customizable export templates, and the ability to schedule or automate exports.

Billing model. Some platforms bill per API call, which creates unpredictable costs for teams running automated pipelines. Others bill based on the underlying data — prompts tracked, responses collected — making API usage effectively unlimited once you’re paying for the data. Understand which model applies before you commit to building pipelines on top of a platform.

Rate limits and performance. Enterprise data pipelines need predictability. Look for: documented rate limits, support for batch pulls, response times that don’t degrade at scale, and the ability to request higher limits for production workloads.

Scrunch: The most complete API for enterprise AI visibility data

Scrunch is the enterprise choice for AI visibility, working with companies like Lenovo, Akamai, and ADP. Its developer-grade data API is one of the most fully documented and capable in the AEO category. It has two distinct endpoints, native warehouse loading guides, a Looker Studio connector, and billing that’s decoupled from API call volume.

API overview

Scrunch offers two primary data APIs and a set of configuration APIs, all publicly documented at developers.scrunch.com. 

The Query API returns pre-aggregated metrics — brand presence percentage, position score, sentiment score, competitor presence, and response counts — grouped by dimensions including date, AI platform, persona, funnel stage, competitor, source URL, and branded vs. non-branded. This is the same data that powers the Scrunch dashboard, exposed in a queryable format optimized for BI tools, reporting pipelines, and scheduled exports. 

The Responses API returns row-level data — one record per AI response — including full response text, citation URLs with domain and snippet, per-response brand presence, sentiment and position, per-response competitor evaluations, and prompt metadata. It’s designed for warehouses, audits, NLP analysis, and custom tooling. 

A set of configuration APIs lets you programmatically update brand context, competitors, and personas. This is useful for agencies onboarding new clients at scale or enterprises managing multiple brands. 

Scrunch charges based on the number of prompts tracked, not the number of API calls. Once you’re paying for the data, API usage is effectively unlimited for reporting and pipeline purposes.

Integration capabilities

Scrunch’s native Looker Studio connector exposes all standard Query API fields and includes a pre-built dashboard template. The V2 version supports multi-brand data, agent traffic, and comparison fields. 

For teams that want to go deeper, Scrunch’s developer documentation includes detailed guides for loading Responses API data into BigQuery, Snowflake, Redshift, and Databricks, including recommended schema designs, incremental loading strategies, and deduplication patterns. Teams building ROI models can push Scrunch data into GA4 to attribute revenue directly to AI search. 

And at the CDN layer, Scrunch’s agent traffic tracking — integrated with Akamai, Cloudflare, Vercel, and others — surfaces which AI bots are visiting your site, which pages they hit, and how frequently. That data is also accessible via API.

Here’s also what G2 reviewers say about Scrunch’s data integration capabilities:

“The data exports in particular give us clarity and flexibility beyond what the dashboard alone can provide.” — Verified User, Marketing and Advertising Agency

“Scrunch gives us access to data that isn’t easily available elsewhere — and it does so affordably. We especially value the citation presence and traffic tracking across all AI platforms.” — Sebastian B., Administrator

“It connects to my Google Analytics, which is a nice integration.” — Scott S., User

Scrunch holds a 4.7 out of 5 rating on G2.

Pricing

API access is an Enterprise-tier feature. The Core plan ($250/month) does not include API access but does include CSV, Excel, and PDF exports from the dashboard. The Enterprise plan (custom pricing) includes Query API access, Looker Studio access, and Advanced API access, along with MCP access and CLI access when those ship. Teams evaluating Scrunch specifically for API and integration capabilities should plan for Enterprise pricing.

Adobe LLM Optimizer: Deep integration within the Adobe ecosystem

Adobe LLM Optimizer is a GEO/AEO application within the Adobe Experience Cloud ecosystem, designed primarily for organizations already running Adobe Experience Manager. It reached general availability in October 2025.

API and integration overview

Adobe LLM Optimizer does not offer a standalone, publicly documented data API for extracting AI visibility metrics. Its integration strategy is tightly coupled to the Adobe ecosystem.

The Adobe Analytics integration is the primary data integration path. LLM Optimizer connects to Adobe Analytics to correlate AI visibility data with website engagement and conversion metrics. Data refreshes daily. This integration is included in the paid offer but not available during the free trial.

The Adobe Experience Manager Sites integration enables one-click content deployment for optimization recommendations. This is a content publishing integration, not a data export path.

CSV export from dashboards is available — users can export data from the Brand Presence dashboard and Share of Voice table. The export option surfaces from within the data tables in the platform.

Adobe has referenced A2A and Model Context Protocol (MCP) as enterprise integration standards on the LLM Optimizer product page. Specifics and documentation are not yet publicly available as of early 2026.

To unlock Agentic Traffic and Referral Traffic dashboards, organizations must configure CDN log forwarding. This is an inbound data integration — bringing data into LLM Optimizer — not an outbound API.

Pricing

Adobe LLM Optimizer uses one core SKU based on annual contracted prompt volume. The minimum purchase is 1,000 prompts, scaling in increments of 200. Public pricing is not disclosed — Adobe directs prospects to request a quote. CSV export and Adobe Analytics integration are included in the paid offer. The free trial (up to 100 prompts for new activations after April 2026) does not include Adobe Analytics integration.

Bluefish: Enterprise brand protection with limited public API transparency

Bluefish is an enterprise AI marketing platform that differentiates on brand protection and safety. It tracks not just visibility but accuracy and sentiment to identify reputational risks across AI channels.

API and integration overview

Bluefish does not publish public API documentation or a developer portal. The platform is a product-service hybrid, and public product demos are not available.

Bluefish’s product pages reference “Robust Data Integrations” and “Data Integration: Brand data transformation and connection” as capabilities, but specific integration endpoints, supported systems, and technical details are not publicly documented.

Third-party reviews consistently note that Bluefish’s API surface is restricted on lower tiers. Teams on Starter or Growth plans report limited channel and data source connectivity, with broader API coverage gated behind enterprise pricing.

At the enterprise tier, Bluefish reportedly offers custom data integrations tailored to the organization’s needs — including custom seat counts, brand-mention ranges, and integration configurations. These appear to be negotiated during the sales process rather than documented as standard API capabilities.

Pricing

Pricing is not publicly disclosed, with custom enterprise pricing negotiated. Third-party reviews suggest a Starter tier between $99–$299/month with limited API access and channel coverage, and a Growth/Professional tier around $299–$799/month with expanded integrations but still below enterprise-level flexibility. Full API coverage and deep integrations appear to require enterprise-tier pricing.

Profound: Growing API with enterprise-only access

Profound is an AEO platform that has expanded from AI search monitoring into content creation via customizable “agents.” It holds SOC 2 Type II and HIPAA certification.

API and integration overview

Profound offers a REST API documented at docs.tryprofound.com. The API is currently in beta and available only upon request — it is not automatically enabled for all customers.

There are several types of API offered:

• The Reports API supports querying visibility, sentiment, citations, and fanouts (query decomposition analysis). It uses POST requests with category identifiers and date range parameters and returns JSON. 
• The Answers API provides access to individual AI-generated answers including full response data. 
• The Agent Analytics API supports querying bot traffic logs and bot identification data, with V2 endpoints supporting hourly granularity. 
• Organization endpoints let you retrieve regions, models, domains, assets, personas, categories, topics, and tags. 
• A Content Optimization API retrieves optimization lists and analysis for specific pages.

Profound also offers Python and JavaScript SDKs — available on PyPI and npm respectively — which lower the bar for engineering teams building custom integrations. On the integrations side, Agent Analytics supports connections with Akamai, AWS, Cloudflare, Fastly, Google Analytics, Google Cloud Platform, Netlify, Vercel, and WordPress across all tiers. Tableau is documented for BI reporting.

Pricing

API access is gated to the Enterprise tier, which requires a custom pricing conversation. The Starter plan includes no exports and no API access. The Growth plan adds CSV and JSON exports but still no API access. Enterprise unlocks CSV and JSON exports plus API access by request. Profound does not publish plan prices at any tier.

Side-by-side: API and data export capabilities compared

What to ask vendors about API and data export during evaluation

Most AEO platforms look capable on a demo call. The questions below are designed to get past the slide deck and surface what actually matters for enterprise data teams.

Before the demo:

• Is your API documentation public? If not, ask for access before committing to a demo. Your data engineering team should be able to evaluate feasibility independently.
• What plan tier includes API access? Understand the total cost of ownership for API-inclusive plans, not just the base monitoring price.
• Is the API in production or beta? Beta APIs can change without notice. Ask about the stability commitment and deprecation policy.

During the demo:

• Show me a live API call. Ask the vendor to make a real API request during the demo — not a slide deck or screenshot. You want to see the actual response schema, latency, and data structure.
• Can I get row-level data, or only aggregates? If you need to build custom analysis, audit AI responses, or feed data into ML workflows, you need per-response data — not just percentages and scores.
• How do API keys work? Ask about scoping (can you restrict a key to a single brand?), permissions (read-only vs. write access), and rotation (can keys be revoked and replaced without downtime?).
• Walk me through loading this data into [your warehouse]. If the vendor has done this before with other customers, they should be able to describe the schema, incremental loading strategy, and common pitfalls. If they can’t, their enterprise customers probably aren’t using the API at scale.

After the demo:

• Can I test the API during a trial? If the vendor gates API access behind enterprise pricing, ask for a limited API trial so your team can validate the integration before signing a contract.
• How are API calls billed? Billing per API call creates unpredictable costs for automated pipelines. Billing based on underlying data — prompts, responses — is more predictable for enterprise budgeting.
• What happens to my data if I leave? Understand the data portability story. Can you bulk-export everything via API before contract end? Is there a data retention period after cancellation?