Databao: Shared context is the foundation of AI trust

Databao sees shared context as the foundation of AI trust

There’s a quiet problem sitting underneath most AI-powered analytics tools today: they can query your data, but they don’t truly understand what it means.

In many organizations, that gap shows up in familiar ways. The same metric gets defined differently across teams. Dashboards contradict each other. Employees spend as much time reconciling definitions as they do generating insights.

Now layer AI on top of that, and the cracks don’t disappear, but widen. A model can generate tables, summarize trends, or build a chart. But if the underlying definitions aren’t aligned, it’s just automating inconsistency at scale.

That’s what Databao is trying to solve. The premise is straightforward. Before AI can reliably answer questions about your business, it needs a shared, governed understanding of your data, or what Databao refers to as a semantic context layer. Without it, even the best models will struggle to produce results teams can trust. 

Gartner has long highlighted the financial impact of poor data quality, estimating that it costs organizations an average of $12.9 million per year. But beyond the money, there’s also a loss of confidence in data systems and slower, more fragmented decision-making.

A two-part approach: context first, then AI

The product is built around two main components that can be used together or separately.

The first is a context engine, a python library tool that connects to databases, BI tools, and documentation systems to extract schema and metadata. The goal is to capture business logic and definitions and encode that into a governed, reusable layer. 

In practice, that means defining terms such as what counts as “active users,” how revenue is calculated, and how different datasets relate to one another. Rather than forcing teams to rebuild a semantic layer from scratch, Databao can also build its context directly from existing dbt projects.

The second piece is a data agent, delivered as an open-source Python SDK. This is where AI comes in. The agent uses the semantic layer created by the context engine to generate SQL queries, clean and combine data, and produce interactive visualizations. It can also create the semantic layer based on end-user interactions and questions. The agent will then propose an update to the semantic layer that can be reviewed and approved by the team.

Because the agent is grounded in a shared definition layer, its outputs are intended to align more closely with the organization's existing data, reducing guesswork and contradictions.

The agent can work across multiple data sources and supports both cloud-hosted and local language models, which gives teams flexibility in deployment and reducing the risk of sensitive data leaving your environment.

From tools to a team platform

Databao now offers platform flexibility. It can provide SaaS for end-to-end self-service, while open-source libraries are also available if an engineering team wants to build its own custom data agents.

The goal is to bring the context layer and data agent into a shared workspace where teams can collaborate, manage definitions, and run production-grade workflows in one place.

The pitch is less about replacing existing data tools and more about operating across them and serving as a unifying layer that standardizes meaning while allowing companies to keep their current stack.

If that sounds abstract, it’s because the problem itself is abstract. Semantic alignment isn’t as visible as a dashboard or as flashy as a new model release. But it’s foundational and increasingly important as AI becomes embedded in everyday data workflows.

Databao offers the ability for companies to run a proof-of-concept where they define their needs and use cases and start defining a context process and connect data agents to it.

Why this approach is getting attention

We’re at a point in the AI cycle where model capabilities are no longer the primary constraint for many data teams. The harder challenge is ensuring that outputs are reliable enough to inform real business decisions.

That’s where Databao is trying to make its mark. Rather than building a better model, it's improving the conditions under which models operate.

If it works, the payoff is straightforward. Data teams spend less time redefining metrics and they stay focused on high-impact work instead of ad hoc business requests. Business users get answers that align with how their organization actually measures performance. And AI tools become something teams can rely on, rather than babysit.

The success of a semantic layer depends on adoption and governance, both as much cultural as technical. But the direction is clear. As AI moves deeper into enterprise workflows, the companies that can standardize the meaning of their data will have an advantage. Databao is building for that layer.

See why builders like you are loving Databao right here.