Sold by: Upriver
Deployed on AWS
AWS Free Tier
Upriver is the AI Data Engineering Platform. Its agent connects to your warehouse, orchestrator, and code to build a living understanding of how your environment actually works, and turning that understanding into resolved incidents, shipped builds, and trusted data your team can stand behind.
Overview
Upriver is the AI Data Engineering Platform built to live inside the way your team already works. Its agent instantly connects to your warehouse, orchestrator, and code to build a continuously updated, cross stack understanding of how your data environment actually functions. Its AI agent operates across the full data engineering lifecycle: resolving incidents, building new pipelines, accelerating migrations, and maintaining pipeline health.
No context switching. No new tool to learn. A Living Map is prebuilt within hours of connecting your environment, and the agent works for you. Hand the agent a task, review the work, ship through your normal flow. Every fix, validation, and explanation compounds into the Living Map permanently, so knowledge accumulates inside the platform instead of walking out the door. Customers report 3 day investigations resolved in 30 minutes, 3 month JIRA tickets closed on the first agent run, and 10x data engineering productivity.
Highlights
- Resolves incidents end-to-end, not just findings: Upriver traces root cause across your warehouse, orchestrator, and code - then opens the PR with the validated fix. Engineers stop firefighting, review the agent's work, and ship through their normal workflow.
- The Living Map, built for you: Connect Upriver to your environment and the cross-stack map is live within hours. No MCPs to write, no skills to configure, no data harness to maintain. The agent operates on real environmental understanding from day one.
- Compounds institutional knowledge: Every fix, validation, and explanation from your team feeds the Living Map permanently. New engineers ramp in days instead of months. The second incident is faster than the first. Knowledge stops walking out the door.
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By Upriver
By Monte Carlo Data
By Collibra
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Cross-Stack Environment Mapping
Connects to warehouse, orchestrator, and code to build a continuously updated understanding of the data environment across all components
Incident Resolution with Root Cause Analysis
Traces root cause across warehouse, orchestrator, and code infrastructure and automatically opens pull requests with validated fixes
Automated Pipeline Development and Maintenance
Operates across the full data engineering lifecycle including incident resolution, pipeline building, migration acceleration, and pipeline health maintenance
Rapid Environment Integration
Establishes cross-stack map within hours of connection without requiring MCPs, skill configuration, or data harness maintenance
Persistent Knowledge Accumulation
Stores every fix, validation, and explanation in the Living Map to enable institutional knowledge retention and accelerate resolution of subsequent incidents
Data Quality Monitoring
Machine learning-based monitoring and alerting for data quality issues across data warehouses, data lakes, ETL pipelines, business intelligence, and AI tools
Root Cause Analysis
Automatic root cause identification and impact assessment with end-to-end field-level lineage for data issues
Proactive Issue Detection
Proactive identification of data issues across the data stack before stakeholder notification
Data Lineage and Cataloging
Automatic field-level lineage tracking and centralized data cataloging for data asset accessibility, location, health, and ownership
Multi-Stack Integration
End-to-end observability platform supporting data warehouses, data lakes, ETL systems, business intelligence tools, and AI applications
AI Governance Framework
Active metadata-based governance with rules, processes and responsibilities to ensure ethical AI practices, mitigate risk, adhere to legal requirements, and protect privacy
Automated Data Lineage
End-to-end lineage tracking providing transparency into data transformation and flow across systems, including both summary-level business lineage and detailed technical lineage
Unified Data Catalog
Multi-cloud and hybrid environment data discovery with business context including data origin, ownership, usage patterns, and access to reports, AI models and data products
Data Quality Automation
Automated monitoring and rule management system for enterprise-wide data quality management replacing manual processes
Privacy and Compliance Workflow
Centralized automation of privacy workflows to operationalize privacy requirements and address global regulatory compliance
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