Agentic data platform that brings data engineering, analysis and science into collaborative workspaces shared by humans and AI agents.

Databench by Alkera works by combining Knowledge Base: Agents read context from connected sources such as Notion, Confluence and Google Docs so business definitions stay consistent, with each entry showing its sources and whether it is human-verified., Column-Level Lineage: Traces lineage across warehouse, transformation and analysis tools so agents check downstream impact before making a change., Automated Maintenance: Detects job failures, stale data and correctness issues, then proposes fixes and tests them in isolated sandboxes with CI/CD quality gates., Notebooks and Dashboards: Build analyses in SQL and Python notebooks with the Alkera agent and share them as interactive dashboards that recalculate only dependent charts., Dedicated GPU Kernels: Give a notebook kernel a dedicated GPU node to train and post-train models and chart results in the same notebook. to help users with Pipeline Monitoring: Automatically catch failed jobs and stale data and have agents propose tested fixes., Impact Analysis: Check which downstream models and dashboards a schema or logic change will affect before shipping it., Self-Serve Analytics: Let analysts ask an agent to chart metrics such as monthly revenue by segment in a notebook., Model Training: Run heavy training or post-training jobs on dedicated GPUs directly from a notebook., Shared Metric Definitions: Keep business definitions consistent across teams by grounding agents in verified knowledge sources..

Key features include Knowledge Base: Agents read context from connected sources such as Notion, Confluence and Google Docs so business definitions stay consistent, with each entry showing its sources and whether it is human-verified., Column-Level Lineage: Traces lineage across warehouse, transformation and analysis tools so agents check downstream impact before making a change., Automated Maintenance: Detects job failures, stale data and correctness issues, then proposes fixes and tests them in isolated sandboxes with CI/CD quality gates., Notebooks and Dashboards: Build analyses in SQL and Python notebooks with the Alkera agent and share them as interactive dashboards that recalculate only dependent charts., Dedicated GPU Kernels: Give a notebook kernel a dedicated GPU node to train and post-train models and chart results in the same notebook..

Databench by Alkera is useful for anyone interested in Pipeline Monitoring: Automatically catch failed jobs and stale data and have agents propose tested fixes., Impact Analysis: Check which downstream models and dashboards a schema or logic change will affect before shipping it., Self-Serve Analytics: Let analysts ask an agent to chart metrics such as monthly revenue by segment in a notebook., Model Training: Run heavy training or post-training jobs on dedicated GPUs directly from a notebook., Shared Metric Definitions: Keep business definitions consistent across teams by grounding agents in verified knowledge sources..

Databench by Alkera offers a free tier with paid plans for advanced features.

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