From Raw Text to Relational Intelligence: How Fisor Builder™ Automates Structured Data Creation

Executive Summary

Most valuable business data—emails, reports, meeting notes, documents—comes in unstructured text. But decision systems, dashboards, and predictive models require structured data. Fisor Builder™ bridges this gap by using LLMs and schema inference to turn raw text into fully relational datasets, automatically creating tables, keys, and relationships.

1. The Problem with Unstructured Data

SMEs often have:

• Client communication logs
• Manually entered notes or purchase records
• Scraped data or PDFs

…but no clean way to extract structured entities, dates, quantities, or relationships without a dedicated data team.

2. How Builder™ Converts Raw Text to Tables

Builder™ breaks the process into modular steps:

• Text Ingestion: Accepts plain text, PDFs, emails, or HTML
• LLM-Powered Extraction: Identifies key fields using NER and prompt-based classification
• Schema Generation: Constructs normalized table definitions (YAML/SQL)
• Relationship Mapping: Detects foreign keys and joins based on context (e.g., customer-to-order)
• Data Output: Publishes to Iceberg, CSV, or PostgreSQL

3. Technical Architecture

Stage	Technology
NLP Core	GPT-4, LangChain
Schema Inference	Custom YAML/SQL template generator
Relational Mapping	Contextual rule engine + embeddings
Storage Layer	Apache Iceberg, PostgreSQL
Output Formats	JSON, CSV, SQL, Insight YAML

4. Real-World Applications

Use Case	Result
Invoice Parsing from Emails	Auto-generated Customer, Invoice, Item tables
Historical Report Digitization	Extracted event logs into normalized datasets
Order Info from Chat Logs	Reconstructed product + quantity relationships

5. Why It Matters for SMEs

• Eliminates need for manual data entry
• Creates insight-ready pipelines from everyday language
• Builds reusability—each schema becomes a module

This brings relational intelligence within reach of companies with zero in-house data science capacity.

6. Fisor Builder™ Advantage

Unlike generic LLM tools or spreadsheet parsing hacks, Builder™:

• Supports YAML-driven insight engine creation
• Aligns outputs directly with Fisor Insights™ and Fisor Radar™
• Ensures reproducibility and traceability of how data is structured

Conclusion

Fisor Builder™ transforms a messy problem into a modular solution. By turning raw language into normalized tables, it enables SMEs to extract insight, power forecasts, and automate analytics—without needing a data warehouse team. With Builder™, words become structure, and structure becomes intelligence.