Querying Data and Insights

Once data is ingested and cleaned, then connected into knowledge maps, the next step is understanding it. Querying is how users turn structured data into clear, actionable insights.

In traditional systems, querying requires technical skill and a precise knowledge of where data lives. At DataLinks, this process is transformed. The platform combines the power of structured data with the reasoning ability of large language models (LLMs) so that anyone can ask questions and get accurate, meaningful answers.

From data to questions

Most enterprise data is too complex for manual exploration. Each table might contain thousands of rows, dozens of columns, and connections across multiple datasets. Writing SQL to join and filter all of that is time-consuming and error-prone.

DataLinks bridges that gap by using LLMs to interpret questions and generate the right structure automatically. When a user types a question, for example, “Show me all suppliers who serve both our European and Asian operations” , the system translates that intent into an optimized SQL query that runs across the connected datasets.

This is not just keyword matching. The model understands context, recognizes relevant entities, and uses the knowledge map to locate the necessary tables and fields. It can reason about relationships such as companies → shareholders → regions and construct multi-step queries that would otherwise require a data analyst.

How querying works in DataLinks

Querying is built into both the web platform and the API, giving flexibility to business users, analysts, and developers alike.

1. Web Platform

   Through the Preview & Query interface, users can type natural language prompts or manually write SQL. The platform runs the query, displays results, and highlights the underlying datasets and links used.

2. API

   Developers can submit queries directly using:

   • POST Query data

   This endpoint accepts structured query requests and returns results as JSON, which can be integrated into dashboards, reports, or applications.

The role of the LLM in structuring queries

At the core of DataLinks’ querying is the LLM-powered reasoning engine. Its role is not to replace databases but to make them more accessible and intelligent.

Unlike generic chat-based LLM systems that generate freeform answers, DataLinks uses its models to reason within the boundaries of verified enterprise data. Every answer is grounded in facts that come directly from your connected datasets.

Here is how the process works:

• Interpretation: The model reads your natural language question and identifies the relevant entities and metrics.
• Translation: It converts that intent into query syntax, joining the correct datasets within the namespace.
• Optimization: It selects the most efficient path through the knowledge map, prioritizing the strongest or most relevant links.

How hallucinations are mitigated

Because the LLM operates on top of DataLinks’ structured semantic layer, it cannot produce results outside of what the data actually contains. The model does not “guess” or fill in gaps. Instead, it constructs queries that are executed against real, verified data.

This design eliminates the most common cause of hallucination in AI-driven analytics: freeform generation without grounding.

The system ensures reliability in several ways:

• Grounded reasoning: The model only references information that exists in connected datasets.
• Schema awareness: It understands dataset structure and field names, which significantly minimizes the risk of it inventing nonexistent columns or relationships.
• Query validation: Every LLM-generated query is checked against the schema and executed through the database, guaranteeing factual output.
• Traceability: Users can always view the generated SQL query, giving full transparency into how the result was produced.

By grounding the model in enterprise data and maintaining full transparency, DataLinks gives organizations the power of natural language querying without the risk of misinformation or hallucinated results.

From queries to business insights

Once data can be queried naturally, insight becomes immediate. Executives and analysts no longer need to wait for data teams to prepare reports. Instead, they can explore information directly, following their questions where they lead.

Some examples of the insights that can be unlocked include:

• Compliance and risk: Identify indirect links between entities and sanctioned organizations.
• Procurement optimization: Discover overlapping suppliers across business units to consolidate spend.
• Financial intelligence: Correlate company performance metrics with supply chain dependencies.
• Operational monitoring: Surface anomalies, trends, and relationships across regions or product lines.

These insights come from structured connections between datasets, not isolated tables. The LLM simply makes navigating those connections fast and intuitive.

Why this matters

Enterprises spend millions each year trying to extract value from data, but the bottleneck often lies between knowing what to ask and how to ask it. DataLinks removes that barrier by allowing anyone, not just SQL experts, to query their data through plain language.

Because the platform understands both the structure and the meaning of the data, it can generate high-quality queries that reflect real business logic. This combination of semantic structure and LLM interpretation turns querying into an intelligent conversation with your data.

Example: Deep search across connected datasets

You can use query language to traverse multiple degrees of connections in the knowledge map to uncover indirect relationships that matter for compliance and risk. For example, the following query starts from a specific company in the registry, explores its network, and then looks for any links to sanctions data:

Ontology("registry/companies")
  .filter("full_name" == "Qualcomm Technologies, Inc.")
  .searchAround(5)
  .find("sanctions/companies")

This query works in several steps:

• Ontology("registry/companies") selects the companies registry dataset as the starting point.
• .filter("full_name" == "Qualcomm Technologies, Inc.") narrows the focus to a single legal entity based on its full registered name.
• .searchAround(5) walks the knowledge map up to five degrees of connections away from that company, following the defined links between datasets such as ownership, subsidiaries, and related entities.
• .find("sanctions/companies") returns the companies in the sanctions dataset that are discovered within that five-degree reach.

This kind of deep search can reveal whether a company has direct or indirect exposure to sanctioned entities, without requiring the user to know ahead of time how those relationships are structured.

The foundation of data-driven decision making

When querying works at this level, AI becomes more than a tool for analysis; it becomes a partner in reasoning. Clean, connected, and well-structured data combined with LLM-driven querying gives organizations the ability to move from static reports to continuous discovery.

With DataLinks, insight is no longer locked behind technical barriers. It becomes a shared capability across the enterprise, available to every team and every question.