What does "AI visibility" mean for my business?

When someone asks ChatGPT, Claude, or Gemini "what is the best..." or "where can I find...", AI gives specific recommendations. AI visibility is a measure of how often — and how positively — these AI platforms mention your business. It's the new version of being on the first page of Google, except now there are no pages — just one answer.

Which AI platforms do you monitor?

Pendium monitors ChatGPT, Claude, Gemini, Grok, Perplexity, DeepSeek, and Google AI Overviews — seven major AI platforms that consumers use to research local services. We run real queries that your customers actually ask and analyze the responses for mentions of your business, competitors, and industry topics.

How does Pendium improve my visibility?

Three ways. First, we identify the exact queries and topics where you're invisible. Second, we generate AI-optimized content — articles, guides, social posts — designed to help AI agents understand your business and recommend your services to the right people. Third, we continuously monitor to track improvement and find new opportunities. Most clients see measurable improvement within 60 days.

How long until I see results?

You'll get your first visibility report within minutes of signing up. Actual visibility improvement varies, but our clients typically see a 47% average improvement within 60 days. Some see results faster — it depends on your current online presence and how competitive your local market is.

Do I need to be technical to use this?

Not at all. Enter your website URL and Pendium handles everything: scanning AI platforms, analyzing your visibility, generating content, and tracking improvement. The dashboard is designed for business owners, not engineers. If you can check your email, you can use Pendium.

What makes this different from SEO?

Traditional SEO optimizes for Google's search rankings — blue links on a results page. AI visibility is different: AI doesn't show links, it gives direct answers. Even if you rank #1 on Google, ChatGPT might recommend a competitor. Pendium optimizes for the new paradigm — making sure AI platforms know about, trust, and recommend your business.

Pricing

Get a demo

Airweave

Visibility0

Vibe50

Businesses/Software/Airweave

AI Visibility & Sentiment

Airweave

Airweave is a context retrieval layer for AI agents and RAG systems. It connects to apps, tools, and databases, syncs data in real-time, and exposes it through a unified search interface, enabling AI systems to retrieve grounded, up-to-date information on demand.

Active Monitoring

airweave.ai

Software Startups

AI Visibility Score

0/100

Invisible

Sentiment Score

50/100

Score by Priority

How often this business is recommended to users across different types of conversations — from direct product queries to broader open-ended conversations where AI could recommend this company's products and services

core

adjacent

aspirational

AI Perception

Key Takeaways

How AI platforms collectively perceive and describe Airweave today.

Airweave is currently a ghost in the technical conversations it should lead, suffering from zero visibility across all high-intent RAG and data integration queries. While competitors like LangChain and Pinecone dominate the architectural narrative, Airweave remains sidelined even in specialized searches for enterprise connectivity and context retrieval.

Working in your favor

Nascent brand recognition in AI Overviews for direct 'vibe check' queries, indicating the underlying models have basic awareness of the brand's existence.

Minimal presence in Google AI Overviews suggests a technical foundation that can be leveraged if content is optimized for specific architectural keywords.

Gaps to close

Complete absence in the 'RAG and AI Agent Infrastructure Planning' category, leaving the field open for LangChain and LlamaIndex.

Zero mention rate among high-value personas including Enterprise AI Architects and Data Engineering Leads.

Failure to appear in integration-specific queries despite being a solution for enterprise app data syncing with Slack and Jira.

Opportunities

Own the 'Context Retrieval Layer' category by creating definitive guides that position Airweave as a necessary component alongside vector databases.

Capture 'Enterprise Data Connectivity' traffic by publishing technical benchmarks on syncing speed and accuracy for common enterprise apps.

Differentiate from generalist competitors like Airbyte by focusing on AI-ready data streams specifically for RAG workflows.

Highest-Impact Actions

Publish a comprehensive 'State of Context Retrieval' whitepaper and corresponding technical documentation.

This directly addresses the total lack of visibility in queries related to best context retrieval tools and infrastructure planning.

Create deep-dive technical integration blogs detailing RAG implementation for Slack and Jira.

High-intent queries regarding these specific integrations currently result in zero mentions for Airweave, giving competitors like Airbyte an uncontested lead.

Develop comparative 'Airweave vs. LangChain' and 'Airweave vs. LlamaIndex' architecture guides.

LangChain and LlamaIndex are the most mentioned competitors; targeting their user base will help penetrate the Architect and CTO personas.

Value Proposition

Shared context retrieval infrastructure that eliminates fragile, per-application retrieval pipelines by providing a unified search interface across all enterprise apps and databases for AI agents

Overview

Mission

Turning scattered data into the intelligence AI agents rely on to act with clarity

Products & Services

Context retrieval layer for AI agentsPrebuilt connectors for 50+ data sourcesReal-time data sync infrastructureSemantic and hybrid search capabilitiesAirweave Academy educational resources

Current State

Visibility Landscape

A high-level view of how Airweave performs across AI platforms, broken down by strategic priority level — from core brand queries to growth opportunities.

	ChatGPT	Claude	Gemini	AI Overviews
Reputation1q Brand recognition & direct queries	70	70	70	91
“What do you know about Airweave? What do they do and what's their reputation?”	Yes	Yes	Yes	#3
Core2q Product/service category queries	0	0	0	0
“best context retrieval tools for AI agents right now”	No	No	No	No
“what is a context retrieval layer and do i need one for my LLM app”	—	No	No	No
Growth Areas4q Adjacent, aspirational & visionary	0	0	0	0
“how do i build a RAG system that pulls from slack, jira, and google drive at the same time”	No	No	No	No
“easiest way to sync data from 20 different enterprise apps into a vector database”	No	No	No	No
“help me understand semantic vs hybrid search for my rag project”	No	No	No	No
“best architecture for an AI agent that needs real-time access to company documents”	—	No	No	No

“What do you know about Airweave? What do they do and what's their reputation?”

Yes

“best context retrieval tools for AI agents right now”

“what is a context retrieval layer and do i need one for my LLM app”

—

“how do i build a RAG system that pulls from slack, jira, and google drive at the same time”

“easiest way to sync data from 20 different enterprise apps into a vector database”

“help me understand semantic vs hybrid search for my rag project”

“best architecture for an AI agent that needs real-time access to company documents”

—

Competitive Landscape

LangChain

38 mentions

Pinecone

37 mentions

LlamaIndex

32 mentions

Weaviate

31 mentions

Slack

25 mentions

Milvus

17 mentions

Airbyte

17 mentions

Qdrant

16 mentions

Unstructured.io

16 mentions

Cohere

16 mentions

Airweave

0 mentions

Analysis

Insights & Recommended Actions

What's working, what's not, and specific steps to improve Airweave's AI visibility.

Key Findings

Strength

Nascent brand recognition in AI Overviews for direct 'vibe check' queries, indicating the underlying models have basic awareness of the brand's existence.

Strength

Minimal presence in Google AI Overviews suggests a technical foundation that can be leveraged if content is optimized for specific architectural keywords.

Gap

Complete absence in the 'RAG and AI Agent Infrastructure Planning' category, leaving the field open for LangChain and LlamaIndex.

Recommended Actions

Publish a comprehensive 'State of Context Retrieval' whitepaper and corresponding technical documentation.

This directly addresses the total lack of visibility in queries related to best context retrieval tools and infrastructure planning.

Create deep-dive technical integration blogs detailing RAG implementation for Slack and Jira.

High-intent queries regarding these specific integrations currently result in zero mentions for Airweave, giving competitors like Airbyte an uncontested lead.

Develop comparative 'Airweave vs. LangChain' and 'Airweave vs. LlamaIndex' architecture guides.

LangChain and LlamaIndex are the most mentioned competitors; targeting their user base will help penetrate the Architect and CTO personas.

Content Engineering

Content Ideas

Content designed to help AI agents learn about your category and recommend your brand.

Programmatic Testing

Sample Conversations

We programmatically analyze questions that real customers are asking to AI agents and chatbots, extract brand mentions and sentiment, analyze every response, and synthesize the data into an action plan to increase AI visibility.

ChatGPT

Claude

Gemini

AI Overviews

RAG And AI Agent Infrastructure Planning(3 queries)

“how do i build a RAG system that pulls from slack, jira, and google drive at the same time”

0/4 platforms mentioned

Adjacent

ChatGPT

1.Jira

2.Atlassian

3.Prefect

4.Apache Airflow

5.Redis

+26 more

Claude

1.Slack

2.Jira

3.Google Drive

4.LlamaIndex

5.Pinecone

+10 more

Gemini

1.Carbon

2.Airbyte

3.Unstructured.io

4.LlamaIndex

5.LlamaHub

+10 more

AI Overviews

1.Ragie

2.Vertex AI RAG Engine

3.n8n

4.Pinecone

5.Omni

+4 more

“best architecture for an AI agent that needs real-time access to company documents”

0/3 platforms mentioned

Adjacent

The Enterprise AI Architect · Principal AI Architect

Claude

1.Snowflake

2.Unstructured.io

3.Apache Airflow

4.Pinecone

5.Weaviate

+5 more

Gemini

1.Snowflake

2.Confluent Cloud

3.Kafka

4.Slack

5.Jira

+20 more

AI Overviews

1.NVIDIA Developer

2.Pinecone

3.Weaviate

4.pgvector

5.PostgreSQL

+9 more

“what is a context retrieval layer and do i need one for my LLM app”

0/3 platforms mentioned

Core

The Enterprise AI Architect · Principal AI Architect

Claude

1.Snowflake

2.Cohere

3.Pinecone

4.Weaviate

5.BGE models

+4 more

Gemini

1.Snowflake

2.Cohere Rerank

3.Pinecone

4.Weaviate

5.Snowflake Cortex

+9 more

AI Overviews

1.Red Hat

2.Pluralsight

Source Intelligence

Citations

The sources AI platforms cite when recommending this brand. Pendium reverse-engineers what's already proven to be catnip to AI agents, then engineers content that fills gaps and helps agents do their job — which means more citations for you.

Use data ingestion with Vertex AI RAG Engine

docs.cloud.google.com

Airweave

Key Takeaways

Visibility Landscape

Insights & Recommended Actions

Content Ideas

Sample Conversations

Citations

Brand Voice & Style

Investors

Engineer content that makes AI agents recommend you

Explore Software

Start gettingrecommended by AI.

Frequently asked questions

Airweave

Key Takeaways

Visibility Landscape

Insights & Recommended Actions

Content Ideas

Sample Conversations

Citations

Brand Voice & Style

Investors

Engineer content that makes AI agents recommend you

Explore Software

Start gettingrecommended by AI.

Frequently asked questions

Start getting
recommended by AI.

Start getting
recommended by AI.