The recent acquisition of Carbon AI by Perplexity validates the growing demand for solutions that connect organizational data with AI systems. While this signals exciting future developments, growing companies need solutions today. Let's explore what Carbon AI offers and what practical options exist for mid-market organizations seeking immediate enterprise search capabilities.
What is Carbon AI?
Carbon AI is a platform designed to facilitate the integration of external data sources with Large Language Models (LLMs). It is a universal retrieval engine, enabling LLMs to access unstructured data from any source. Carbon AI simplifies the Retrieval Augmented Generation (RAG) process, allowing users to focus more on using their data rather than the complexities of data ingestion.
The platform supports over 25 data connectors and more than 20 file formats, including text, audio, and visual data, making it highly versatile for various applications. Additionally, Carbon AI offers features like hybrid search, embedding generation, and secure data handling, ensuring optimal performance and security for AI applications.
What's Carbon's technical approach?
According to Carbon CEO Derek Tu, the company takes a pragmatic approach to data integration, focusing on leveraging advanced embedding models for semantic understanding rather than over-optimizing chunking strategies. While Carbon focuses on building sophisticated technical infrastructure for future enterprise implementation, companies like Qatalog have taken a different approach - offering immediate, no-index RAG technology that's ready to deploy today. Let's explore both approaches to understand what makes sense for different organizational needs.
Key technical considerations in Carbon's approach include:
- Data normalization and preprocessing from multiple sources
- Metadata and tagging for better organization
- Advanced embedding models for semantic understanding
- Cost optimization through quantization
- Privacy and ethical compliance built-in
How does Carbon AI work?
Carbon simplifies the process of Retrieval Augmented Generation (RAG), allowing users to focus more on using their data rather than ingesting it. Here's how Carbon works:
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Data integration: Carbon supports native integrations with over 20 data connectors and more than 20 file formats, including text, audio, and visual data. Users can sync data sources using Carbon's REST API or SDKs.
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Data retrieval: Depending on the use case, data can be retrieved in various formats such as original files (PDF, CSV, etc.), parsed plain text, or as chunks and embeddings for storage in a vector store.
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Search capabilities: Carbon enables semantic, keyword, and hybrid searches on a managed or self-hosted vector database, enhancing data retrieval efficiency.
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Storage Options: Users can choose between Carbon's managed vector database hosted on Qdrant Cloud or their own custom vector store. The system automatically updates embeddings and chunks as users modify connected sources.
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Universal API: Carbon's Universal API allows access and management of data from any source, including documents, chunks, vectors, and other metadata. This API powers all functionalities, including the Carbon Connect UI.
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Connect module: Carbon Connect provides a client-side UI for users to authenticate and upload content from various data sources like Notion, Google Drive, Dropbox, and more. It automates data synchronization to keep applications updated with connected sources.
What products does Carbon offer?
Carbon AI offers several products designed for enterprise implementation:
- Carbon Connect: A client-side UI for authenticating and uploading content from various data sources
- Store: Flexible storage options with managed or custom vector database
- Universal API: For accessing and managing data from any source
What's Carbon AI pricing?
Carbon pricing structure reflects an enterprise and developer-focused approach. The platform starts with a pay-as-you-go model charging $0.02 per 3,125 characters, moving up to an $85/month Professional tier with 25M characters included. Their Enterprise tier requires annual commitments and includes implementation services. Additional features like white labeling come with extra costs (+$250/month).
What does this mean for mid-market companies?
Carbon's sophisticated approach showcases the future of enterprise search but comes with significant technical demands. This complexity often creates barriers to entry for smaller and mid-market companies that typically face more immediate challenges:
- Need for quick deployment without technical complexity
- Limited resources for data preprocessing and optimization
- Immediate ROI requirements
- Growing data volumes across multiple platforms
- Budget constraints for complex implementations
What's Carbon alternative for mind-market companies?
The good news is that production-ready alternatives like Qatalog already offer secure search specifically designed for mid-market needs, delivering immediate value without the technical overhead:
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Zero engineering required: Deploy across your organization without technical expertise or dedicated IT resources
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Instant time-to-value: Start searching across all your tools immediately with no indexing or setup delays
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Enterprise security, mid-market simplicity: Bank-grade security and compliance without the complexity
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Native integrations: Seamlessly connect with tools you already use: Google Workspace, Microsoft 365 suite, Salesforce, BigQuerry, Snowflake, and more
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Scalable pricing: Cost-effective solution that grows with your business, not against it
How to get started with connected AI search?
While Perplexity plans to roll out Carbon's features in 2025, mid-market companies can implement AI-powered search today. Qatalog offers immediate deployment with the following:
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No-index RAG technology for real-time search
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Pre-built integrations with common business tools
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Enterprise-grade security
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Quick implementation without engineering resources