Alkera AI, Inc.
Alkera provides an agentic platform for data engineering, analysis, and data science in collaborative workspaces shared by humans and AI agents. Its goal is to make data work reliable and safe by combining unified data-stack context, lineage, governance, and reproducible computation.
At a Glance
- Data engineering teams
- Data analysts and analytics teams
- Data science and machine-learning teams
- Enterprise organizations with governed data stacks
- +2 more
AI Tools by Alkera AI, Inc.
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Alkera
Agentic Data Engineering Workspace
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Latest News
Databench by Alkera launched on Product Hunt as an open-source collaborative agentic workspace for data teams.
Alkera published its DataAgentBench research and July leaderboard submission; its launch materials report a #1 result on UC Berkeley's DataAgentBench.
Y Combinator published 'Alkera - The data agent that you can trust,' describing the company's agent for reliable data engineering, analysis, and science.
Alkera joined Y Combinator's Summer 2026 batch.
Products & Services
Commercial agentic data platform that works across a company's data stack. It provides collaborative workspaces containing chats, SQL/Python notebooks, files, and managed or customer-controlled compute, with lineage, knowledge, governance, permissions, and enterprise deployment controls.
Open-source, Apache-2.0-licensed, self-hostable multiplayer workspace where people and agents collaborate on data through notebooks, chats, shared files, and local or remote compute.
Market Position
Alkera positions itself as an agent-native layer across the entire data stack rather than a code-generation assistant or isolated notebook. It competes with end-user data-work platforms such as Hex and Sigma, while its broader data-engineering and integration set overlaps with Matillion, Fivetran, Airbyte, Ascend, Prefect, and related pipeline platforms. Alkera differentiates on column-level lineage, impact checks, reproducibility, governance, collaborative human-agent workspaces, and parallel remote compute.
Leadership
Founders
Rick Gao
Founder and CEO. Studied computer science at Yale; previously worked at Verition Fund Management building financial models, treasury-financing optimizations, and ingestion pipelines, and conducted computational-biology research at Yale's Gerstein Laboratory.
Andrew Tran
Co-founder and CTO. Studied computer science at Yale and conducted machine-learning research at Yale's Gerstein Laboratory on biology-reasoning models; previously worked closely with data teams at Ramp and Hudson River Trading, including software-engineering work and data tooling.
Tony Li
Founder and COO. Holds a Yale BS in computer science and mathematics; previously was an agentic software engineer at ByteDance, a quantitative developer at Carthage Capital, and built multi-agent workflows, data pipelines, analysis, and production bots.
Executive Team
Rick Gao
Founder and CEO
Yale computer-science background; former Verition Fund Management financial-modeling and data-pipeline contributor; former Yale Gerstein Laboratory computational-biology researcher.
Andrew Tran
Co-founder and CTO
Yale computer-science background; former Ramp and Hudson River Trading data-tooling contributor; former Yale Gerstein Laboratory machine-learning researcher.
Founding Story
The founders saw that coding agents could produce data code that runs but could not reliably understand the consequences of changes across pipelines, dashboards, and business definitions. They built Alkera around the need for column-level lineage, impact checks, collaborative notebooks, remote compute, and guardrails so data teams could work with agents without losing reproducibility or control.
Business Model
Revenue Model
Subscription SaaS with plan-based usage and storage limits, plus enterprise contracts and usage-based fees. The commercial hosted service is complemented by the self-hostable open-source Databench repository.
Pricing Tiers
Free forever; 10 GB storage, limited usage, and native integrations.
Includes Free features, 1 TB storage, extra usage, and access to all models.
Includes Plus features, 5 TB storage, significantly increased usage, dedicated support, and early feature access.
Pooled usage and storage, SSO/SAML, SCIM, IAM, audit logs, VPC and on-premises deployments, zero data retention by default, data residency controls, dedicated support, and available SLAs.
Target Markets
- Data engineering teams
- Data analysts and analytics teams
- Data science and machine-learning teams
- Enterprise organizations with governed data stacks
- Teams using cloud data warehouses, dbt, BI, notebooks, and remote compute
- Organizations needing self-hosted, VPC, on-premises, or data-residency deployments
- Production data engineering and pipeline work with dbt and Apache Airflow
- SQL and Python data analysis in notebooks
- Data science and long-running modeling or GPU experiments
- Creating analytics pipelines and insights without requiring every task to be hand-built by data engineers
- Safe agent-assisted changes to transformations, queries, models, and dashboards
- Collaborative research by data engineers, analysts, scientists, and AI agents