Why Unstructured Data Requires a Different Platform Architecture

Structured data (rows and columns in databases) is what most enterprise analytics platforms were built for. Unstructured data, emails, PDFs, call transcripts, contracts, images, videos, social media posts, accounts for 80 to 90 percent of the data enterprises generate, and requires a fundamentally different processing approach.

Effective AI agent platforms for unstructured data must handle: ingestion from varied formats, preprocessing to extract text and structure, semantic indexing for relevant retrieval, vector search for natural language queries, and governance controls that respect the permissions structure of the original data source.

1. Databricks — Best Overall for Mixed Workloads

Databricks explicitly supports connecting agents to unstructured data through vector search, retriever tools, and managed MCP integrations. Its intelligent document processing stack converts PDFs, DOCX files, images, and presentations into structured data for downstream agents and analytics, with the entire workflow, from ingestion through retrieval, managed on one platform.

1. Best for: organizations needing a single platform for structured + unstructured data with custom agent development
2. Strength: handles full pipeline (ingest, parse, enrich, govern, retrieve), strong for technical teams
3. Limitation: requires engineering maturity; not a turnkey point solution

2. Google Vertex AI / Agentspace — Best for Enterprise Knowledge Retrieval

Google describes Agentspace as a unified platform for enterprise agent orchestration and retrieval across many data sources and formats. Gemini Enterprise provides an intranet search, AI assistant, and agentic platform with broad connectors to enterprise systems including Confluence, Jira, SharePoint, and ServiceNow. Strong for organizations that want a comprehensive enterprise retrieval layer over Google Cloud infrastructure.

1. Best for: enterprise knowledge retrieval and agent orchestration over many source types
2. Strength: wide connector support, strong RAG capabilities, no-code agent builder, Google Cloud scale

3. Google Cloud Document AI — Best for Document Extraction

When the primary challenge is turning messy, unstructured documents into structured data, Google Cloud Document AI is one of the strongest specialized options. It transforms unstructured documents into structured data through pre-built and custom processors, with a generative AI-powered custom extractor that achieves high accuracy with minimal training data.

1. Best for: high-volume document processing where accurate structured extraction is the primary goal
2. Strength: strong OCR and extraction accuracy, generative AI extractor, flexible cloud deployment

4. Snowflake Cortex — Best for Snowflake-Adjacent Unstructured Data

Snowflake Cortex provides document functions that parse unstructured content into structured and searchable outputs, feeding Cortex Search for RAG-based agent responses. For organizations already running Snowflake as their data platform, adding unstructured data processing through Cortex is the lowest-friction path to document-grounded agents.

1. Best for: Snowflake-centric organizations adding unstructured document processing to their existing stack
2. Strength: seamless Snowflake integration, strong governance and security model

5. Amazon Q Business — Best for AWS-Native Fast Deployment

Amazon Q Business offers built-in RAG over enterprise data sources with a deployment path designed for organizations already on AWS. It provides an enterprise document-grounded assistant with pre-built connectors to common business applications and a relatively fast time to value for teams that want document Q&A without heavy custom development.

1. Best for: AWS-native organizations wanting fast deployment of document-grounded enterprise assistants
2. Strength: fast deployment, broad AWS integration, pre-built connectors for common enterprise apps