A leading global financial data and analytics firm sits on some of the most valuable fixed income data in the market — curated third-party bond datasets, a modern data infrastructure, and years of investment in data tooling.
But when a bond analyst wants to answer a question about a security, the data isn't readily accessible. It's trapped behind tooling built for SQL experts, not analysts.
PromptQL connects directly to the firm's existing data infrastructure and surfaces answers in plain English. No SQL, no downloads, no data team in the loop. Analysts can now query the bond data they already have in seconds, with fully auditable outputs that never leave the firm's environment.
The Problem: 2 tools to get 1 answer
The firm's data marketplace requires analysts to navigate between two separate systems to answer a single question.
First, a catalog tool to find the right dataset — a process that involves slow page renders, non-intuitive search results, and no semantic guidance on what fields mean or how to join them.
Then, a SQL virtualization layer to actually run the query.
Then back to the catalog to check a field. Then back again.
Most analysts give up and use Excel. The data team becomes a ticket queue. Exploratory analysis (surface something I didn't know to ask for) essentially doesn't happen.
The harder constraint: the firm's most valuable datasets are strict data compliance regulation. Any solution that moves data outside the firm's environment is a legal non-starter. The tool has to work inside existing infrastructure, against data that never moves.
What They Tried
The firm has already invested in a robust data virtualization layer. In theory, analysts could query any data source across the organization from a single point. In practice, the barrier to entry (knowing the schema, writing complex joins, navigating an unintuitive catalog) meant adoption never followed the infrastructure investment.
Previous attempts to add natural language interfaces ran into the same wall: tools either fetched the wrong columns, failed on aggregations, or returned results that couldn't be trusted or traced.
In financial services, an answer you can't explain is the same as no answer.
How PromptQL Solves It
PromptQL connects to the firm's existing data infrastructure. No rearchitecting, no new data pipelines, no egress. It exposes what's already in place through a natural language interface that any analyst can use.
Where other tools return plausible-sounding answers that can't be verified, PromptQL returns answers that can be. Every response is traceable to its source, and when the system detects ambiguity, it flags it explicitly rather than papering over it with confidence it hasn't earned.
"There are a core set of questions that analysts ask of this dataset constantly. If a tool can answer those accurately, every time — that's the proof of concept."
That trust translates directly into how analysts work.
- Analysts get answers directly: no SQL, no system-hopping, no waiting on a data team
- Data never leaves the environment: fully compliant with third-party licensing restrictions, by design
- Auditable outputs every time: every answer is traceable to its source, with confidence scores surfaced when the system detects ambiguity
- No rearchitecting required: PromptQL connects to existing data infrastructure
This Isn't a One-Off Problem with Financial Data
This story repeats across the credit and research side of financial services. Firms invest in modern data infrastructure. They license high-value third-party data. They build virtualization layers, cataloging systems, and BI dashboards. Adoption plateaus because the last mile (turning infrastructure into answers) remains too hard for anyone who isn't a SQL expert.
The constraint isn't the data. It isn't the infrastructure. It's that the interface between the analyst and the data was never built for non-technical analysts.
PromptQL solves the last mile. It connects to what exists, keeps data where it lives, and makes it queryable, by anyone, in plain English, with auditable outputs every time.
See how PromptQL supports a variety of Financial Service use cases. → Learn more
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