Deep Analyst

deep analyst

Introducing
Deep Analysis Agent

Traditional BI stops at “what happened.” Your real questions are “why did it happen?” and “what should we do next?” which call for deeper investigation.

Business intelligence has barely evolved. After years of investment in dashboards and BI platforms, most enterprise analysis still follows the same slow loop: a stakeholder asks a question, a data analyst writes SQL, builds dashboards, and eventually shares a report. When that report sparks new questions — as it always does — the entire process starts over.

At the same time, AI has made extraordinary progress. Autonomous research agents, like OpenAI’s Deep Research or Google’s Gemini, can now plan multi-step investigations, collect information from diverse sources, and synthesize detailed, high-quality reports with minimal human input.

Yet, these breakthroughs haven’t transformed how businesses understand their own data — the internal metrics that actually drive decisions: customer activity, supply chain performance, financial results, and operational trends. Text-to-SQL tools can generate queries, but they can’t replicate the curiosity, adaptability, and contextual reasoning of an experienced analyst. They don’t ask follow-up questions, refine their hypotheses, or connect findings back to real business goals.

That’s why we built OpenAnalyst’s Deep Analysis Engine — to bring the autonomous reasoning power of modern AI research systems directly into the world of business intelligence. It thinks like a true analyst: exploring, questioning, and uncovering the “why” behind your data.

The Real-World Obstacles to Using Generative AI in Business Data Analysis

Traditional text-to-SQL systems have achieved remarkable results on academic benchmarks — some models even surpassing 90% execution accuracy on standardized datasets. But these numbers come with an important disclaimer: they’re achieved in controlled, simplified environments that look nothing like real enterprise data systems.

Academic testbeds usually involve compact schemas (often fewer than 50 columns per database), a single SQL dialect such as SQLite, limited data types, and short, straightforward queries averaging around 30 tokens.

Real-world business data, however, tells a very different story:

Production warehouses can include hundreds of tables and more than 500 columns.
Companies use multiple SQL dialects — BigQuery, Snowflake, Redshift — each with unique syntax and capabilities.
True understanding requires business context, domain expertise, and organizational definitions that extend far beyond table structures.
Meaningful questions demand multi-step reasoning, hypothesis testing, and iterative exploration.

This disconnect between academic performance and real-world deployment persists because effective business intelligence demands more than query generation. It requires reimagining how AI approaches analytical reasoning itself — moving beyond syntax to genuine understanding, exploration, and insight.

Our Approach

At OpenAnalyst, we set out to build what we call a Deep Analysis Engine for business intelligence. The concept is simple but powerful: instead of limiting AI to generating SQL queries, we designed a system capable of executing the entire analytical workflow that a skilled human analyst would perform.

That means going far beyond querying data. OpenAnalyst can generate visualizations, manage multiple lines of analysis simultaneously, and synthesize its findings into clear, actionable reports.

In practice, experienced analysts don’t follow a single, linear path — they explore multiple hypotheses at once. They test different assumptions, compare results, visualize patterns, and look for deeper connections before forming a conclusion. OpenAnalyst mirrors that same behavior, leveraging parallel tool execution to investigate several dimensions of a question concurrently. The result is faster, richer, and more comprehensive analysis — all with minimal manual effort.