Focus Area
Data
Platforms that help organizations collect, process, and derive insights from data at unprecedented scale. Data is the foundation of the AI era.
30%
Vector DB Adoption
Companies by 2026 (Gartner)
$14.3B
Scale AI Investment
Meta investment in 2025
64%
Platform Consolidation
Companies seeking fewer tools
40%
AI-Assisted Pipelines
New pipeline development
Our Investment Thesis
In the AI era, data is more valuable than ever. But most organizations are drowning in data while starving for insights. The winners will be companies that help organizations turn raw data into actionable intelligence.
We're seeing a fundamental shift in how data infrastructure works. The rise of AI workloads, real-time processing requirements, and the explosion of unstructured data are creating demand for entirely new categories of data tools.
Data infrastructure is once again in flux and evolving faster than at any point in recent memory. Organizations should expect the pace of acquisitions to continue as big vendors realize the foundational importance of data to the success of agentic AI.
We invest in companies building the next generation of data infrastructure: from vector databases and streaming platforms to data quality tools and AI-native analytics.
What We Look For
- AI-native architecture designed for modern workloads
- Clear differentiation from legacy data tools
- Strong integration with the modern data stack
- Demonstrated value in production environments
- Path to becoming essential infrastructure
- Teams with deep data engineering expertise
Market Insight
The long-held belief that bigger data leads to better AI is being challenged
With research suggesting high-quality public text data could be depleted as early as 2026, the focus is shifting from data quantity to data quality and freshness. In this new paradigm, stale data is a liability.
Key Trends Shaping the Market
The forces driving innovation and creating new opportunities in this space.
Vector Database Integration
Purpose-built vector databases are becoming core infrastructure, while traditional databases are absorbing vector capabilities for hybrid workloads.
Platform Consolidation
Organizations are moving from 8-12 different vendors to unified platforms. The modern data platform must provide SQL analytics, vector search, and real-time processing as integrated capabilities.
AI-Ready Data
Data products, lakehouse architecture, observability, and augmented management are becoming baseline requirements for organizations building with AI.
Contextual Memory over RAG
For agentic AI, contextual memory is surpassing traditional RAG, enabling LLMs to store and access pertinent information over extended periods.
Data Quality Focus
The shift from data quantity to data quality and freshness. Organizations adopting data quality tools early report faster insights and lower costs.
AI-Assisted Pipeline Development
40% of new data pipeline development efforts in 2025 involve AI assistance, drastically reducing the time and expertise needed for pipeline creation.
Where We See Opportunity
Specific segments and categories where we're actively seeking investments.
Vector Databases
Purpose-built databases for storing and querying embeddings, enabling semantic search and RAG applications. Essential infrastructure for AI-native applications.
Real-Time Data
Streaming platforms and tools for processing and analyzing data as it arrives. Critical for responsive AI applications and operational intelligence.
Data Quality
Tools for ensuring data accuracy, completeness, and reliability across pipelines. The foundation for trustworthy AI systems.
Data Governance
Platforms for managing data access, lineage, and compliance at enterprise scale. Essential for regulated industries and AI compliance.
AI-Native Analytics
Analytics tools that use AI to surface insights and enable natural language querying. Making data accessible to non-technical users.
Data Integration
Modern ETL/ELT tools and data pipelines built for the cloud-native era. Connecting disparate data sources for unified analysis.
Market Landscape
Notable companies and categories shaping this market.
Vector Databases
Pinecone, Weaviate, Milvus, Qdrant, Chroma, pgvector
Data Platforms
Databricks, Snowflake, Confluent, Fivetran, dbt
Data Quality
Monte Carlo, Atlan, Great Expectations, Soda, Bigeye
Real-Time Data
Confluent, Redpanda, Materialize, Rockset, ClickHouse
Data Governance
Collibra, Alation, Immuta, BigID, OneTrust
AI-Native Analytics
ThoughtSpot, Tableau (Salesforce), Sigma, Mode, Hex
Portfolio Companies
Companies in our portfolio building in this space.
OrbioCloud
AI-powered asset and fleet management platform that makes operations simple, efficient, and affordable.
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Buffy
AI-powered fitness companion and social platform transforming how people train and connect.
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We're actively investing in this space and would love to hear about what you're building.