The Sovereign Ontology for Enterprise AI.
Joined November 2022
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Yesterday, we launched Data Apps to all customers. Today, Aaron Zhu walks you through how to build a working Data App from a single prompt.
He shows how to build a data reconciliation workflow: a leasing intake portal that takes messy broker reports and uses an agent to validate them. The agent surfaces anomalies, brings in human input where needed, and writes clean, validated rows back to the warehouse.
This is just one example. In the blog post below, we're rounding up a few more data apps that impressed us, for inspiration.
Meet Data Apps.
We took what makes low-code and no-code app builders great: speed, no engineering bottleneck. And applied it directly to enterprise data.
The result: fully operational apps built in one or two prompts. Accurate. And fully enterprise-ready out of the box, with version control and RBAC built in, not bolted on afterwards.
We're offering free credits to Team and Enterprise organizations building their first three data apps. Try it today by describing what you want to build in a new chat. That's it.
Learn more about customer examples in our blog post below 👇
Enterprise data teams are still running modern data stacks like 1910s Detroit assembly lines.
@TheEthanDing, CEO, sat down with @petesoder of @ZeroPrimeVC to talk about why that model is breaking now, and what TextQL does differently: working directly on messy enterprise data instead of demanding clean inputs first.
The dirtier the data, the more this approach pays off. And because TextQL builds context over time, it gets cheaper with use instead of more expensive.
They also covered the question every data leader is quietly panicking about: AI is spiking warehouse bills exponentially. The old playbook does not survive contact with that math.
Thanks to Pete and the Zero Prime Ventures team for having us.
Blackstone Credit & Insurance runs on 1,000s of tables, each with 100s of fields and 1,000,000,000s of rows. Until recently, knowing which tables to join, and how to join them without double-counting, was tribal knowledge.
Rob Wisniewski, CTO of Credit & Insurance Technology at Blackstone, deployed TextQL's Ana on top of that data environment.
→ thousands of tables, billions of rows, joined accurately without analyst hand-holding
→ foreign key matching and entity de-duping handled natively
→ "time to theory and where to look" compressed from days to minutes
→ every answer comes with the work shown (non-negotiable in credit and insurance)
"I asked Ana for a playbook on how to solve a problem... it created a scheduled playbook, configured it, and to this day every Monday I get the results in my email. That's the kind of thing you don't see in significantly more mature products."
Most enterprises think faster analytics = faster queries. The bottleneck isn't query execution; it's time to theory: the gap between "I have a question" and "I know which tables to look at and how to join them without breaking the math."
Ana closes that gap while showing its work for every answer. At Blackstone scale, that's not just a productivity gain; it's the difference between acting on a thesis this week and acting on it next quarter.
Your business context originates in APIs, and it's scattered across every tool that your team uses.
In this walkthrough, Matt Abate, Head of AI Research at TextQL, shows how TextQL can connect to API-based sources like Salesforce, Slack, and Notion, and turn that scattered context into governed ontology inputs for Ana.
No duplication, no data modeling. Ana can just reason across your business tools, as they are.
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Open roles:
→ People Operations Lead
→ Chief of Staff to the CEO
→ Member of Technical Staff (Platforms)
→ Member of Technical Staff (Design Engineering)
→ Member of Technical Staff (Distributed Data Systems)
→ Member of Technical Staff (Fullstack / Generalist)
→ Lead Forward Deployed Engineer
→ Lead Solutions Engineer
→ Director of Business Development
→ UI Designer
→ Partner Manager
→ Events Marketing Manager
& more.
Every answer in TextQL now shows its work, inline.
Each citation traces a number back through its entire provenance chain — a visual map of exactly how a number was derived, end to end. This includes which tables were read, the SQL that aggregated them, the Python that reshaped the result, and the answer that came out.
The insights you surface in a Thread can now be e-mailed to yourself or others in your organization.
Ask Ana to share a result and she writes it up as clean HTML with charts and tables in place, tags the right colleagues, and delivers it inside your organization. That way, you no longer have to search through threads to resurface what you found.
According to Dropbox, TextQL's multi-source agent lets their FP&A team query Databricks, Oracle, and Tableau as if they were a single system
read more here: textql.com/customers/dropbox