Build and maintain data workflows with lineage-aware AI agents
Alkera is an AI platform for data engineering, data science and analytics teams. It works across a company's data tools to help build pipelines, investigate problems, maintain documentation and answer questions. Its central approach is to give the agent context about the data stack, including how columns and transformations connect, before it proposes or carries out work.
Column-level lineage helps trace a field from its source through models and into a dashboard. That context can support migration planning and investigation of downstream effects. Alkera also describes pipeline generation and issue-triage workflows, with changes built around the team's conventions rather than isolated scripts. A living knowledge base keeps table and column documentation connected to the tools the team already uses.
For analysis, the platform aims to connect a question with the relevant business definition in the data code. This is useful when two departments use different meanings for a metric such as revenue. Its examples show an agent tracing the data and attaching the query behind an insight, giving a team something concrete to inspect.
Data science workflows use a Jupyter-compatible notebook shared by people and agents. Cells can run on different compute resources, including remote environments, with results brought back into the notebook. This supports workloads that require different processing resources within one investigation.
Alkera offers a limited free plan and paid tiers with greater usage. Enterprise arrangements include deployment and identity-management controls, with some features restricted to eligible plans. The product describes approval requirements for destructive work, audit trails and cost visibility. Teams should configure those controls around their environment and review generated pipelines, queries and changes before relying on them for production data operations.
The platform's controls distinguish understanding a dataset from changing it. A lineage trace or an analytical query can inform a decision, while a generated pipeline may affect downstream models and reports. Alkera describes SQL-aware permissions, cost visibility and approval requirements for destructive operations to help govern that transition. Enterprise deployment options are also separate from general feature availability. Teams should confirm the controls included in their agreement and avoid treating announced or pending compliance work as a completed certification when assessing the service for their environment.
