AI Analytics Startup: Working AI data analysis prototype delivered in 8 weeks. 80%+ SQL query accuracy achieved.

The Challenge

Before committing to full product development, the company needed to answer a fundamental question: was the technical approach viable? Two specific challenges stood between the concept and a buildable product. The first was data model integration. ERP systems, which are the data source the agent would need to query, contain large volumes of data organized in highly varied schemas depending on vendor and implementation. Building an AI agent that could operate reliably across these schemas required a model schema design that was simultaneously structured enough for the AI to reason over and flexible enough to accommodate real-world ERP variation. Table descriptions had to be precise enough to guide the agent while remaining modifiable as the product evolved. The second challenge was accuracy. LLMs operating as natural language to SQL translators introduce hallucination risk by default, as they can generate syntactically plausible SQL that returns incorrect results, or misinterpret business terminology in ways that compound quietly. For a product whose core value proposition is reliable data analysis, accuracy was not a secondary concern, it was the primary gate. Producing inconsistent or incorrect SQL results would make the product unusable, regardless of how intuitive the interface was. The client needed a prototype that demonstrated the approach was sound before investing in full product development.

The Approach

Gradion engaged with a three-person team, including one Technical Advisor, one PM/PO, and one AI Engineer, over an eight-week discovery and prototype sprint. The team began with an IS-Status Analysis, assessing the current data environment, understanding the ERP schema structure, and identifying where the accuracy and consistency risks were most acute. The data architecture combined a primary SQL database with a secondary Vector Database. Anonymized data was migrated into the prototype SQL DB. The Vector DB served a specific purpose, which was storing examples that the agent could retrieve during query generation to improve accuracy through context, rather than relying on the LLM’s generalized reasoning alone. Accuracy was addressed through a multi-layered fine-tuning approach: vector examples provided contextual grounding, table descriptions gave the agent a precise vocabulary for the specific schema, and structured prompting guided the agent’s step-by-step reasoning through query construction. The combination was designed to reduce hallucination in SQL generation and to handle the semantic distinctions that business users care about, for example, correctly distinguishing between “orders” and “purchases” as distinct concepts in the underlying data model. The resulting prototype featured a chat interface that could accept natural language queries, build accurate SQL against the underlying schema, and present results, with result redaction where appropriate, in an accessible format for non-technical users.