Automate Sabre EDI parsing to produce AR and Tax reports using RAG and GPT-4.
This AI agent reads Sabre EDI files, parses the data, and structures it into AR and tax reports. It uses RAG to reference the 154-page IUR document for precise term definitions and rules. Outputs can be exported as CSV/JSON and integrated with Google Sheets for audit-ready workflows.
Delivers structured financial reports from Sabre EDI data.
Ingests Sabre EDI files from Google Drive.
Parses raw EDI contents into structured financial fields.
Queries the IUR using vector search for clarification.
Generates AR Summary and Tax & Surcharges reports.
Exports outputs as CSV/JSON and saves to Drive.
Logs processing metadata for auditing.
This AI agent replaces manual Sabre EDI parsing and ad-hoc reporting with a structured, auditable flow. It ensures accurate interpretation of EDI terms using the IUR as a reference, and delivers ready-to-import reports.
A simple 3-step flow that makes reference data searchable and reports reliable.
Vectorize the Sabre IUR document using embeddings and store in Pinecone for semantic retrieval.
Trigger ingestion, fetch new EDI files from Drive, download content, and extract text.
Pass extracted data to AI Agents connected to GPT-4 and Pinecone to produce AR and Tax reports, exported as CSV/JSON.
A realistic run showing end-to-end processing and output.
Scenario: At 6:00 AM, 12 new Sabre EDI files arrive in Google Drive. The AI Agent ingests, parses, and references the IUR to generate an AR Summary and Tax & Surcharges report. Outputs are saved as CSV and JSON in Drive and linked to a Google Sheet for finance review. Total time: ~12–18 minutes depending on file size; reports are ready for import into BI dashboards.
Roles that gain clarity and speed from automated Sabre EDI reporting.
Needs timely AR summaries to monitor cash flow and aging.
Requires consistent tax and surcharge reporting for filings.
Wants structured data for reporting and forecasting.
Needs an auditable trail of processed files and reports.
Manages Sabre outputs and integration with Sheets/CSV.
Uses standardized reports for dashboards and insights.
Direct connections to data sources, embeddings, and storage.
Source of raw EDI text files and PDFs; outputs are stored here.
Automation & orchestration for the AI agent tasks and triggers.
LLM for parsing EDI data and generating reports.
Converts text into embeddings for semantic search.
Vector DB to store and retrieve IUR chunks for RAG.
Concrete scenarios where the AI agent adds value.
Practical, common questions with clear answers.
The AI agent supports Sabre EDI files and plain text extracts. It reads from Google Drive and can process new .edi and .txt files automatically. It parses data into structured fields for AR and tax reporting. You can extend to other report types with minimal prompts. It maintains an auditable log.
Yes, new versions of the IUR can be uploaded to Google Drive and reindexed in Pinecone. The AI agent can utilize the latest chunks when generating reports. If the IUR changes significantly, prompts can be adjusted to reflect new terms. Retrieval remains efficient due to semantic search.
Yes. The AI agent maintains an auditable log of every processed file and report type. Timestamps, file names, and outcomes are recorded in a Google Sheet or a designated log store. This enables traceability during audits and reviews. Logs can be filtered by date, report type, or status.
Reports can be exported as CSV or JSON and stored in Google Drive. Outputs can be linked to Google Sheets for easy sharing, or imported into BI tools. The agent formats data into consistent columns to reduce manual rework. You can schedule automatic export as part of the workflow.
Data remains within the configured cloud sources (Drive, Pinecone, and the selected LLM environment). Access is controlled via existing permissions, and exports are kept in designated Drive folders. If needed, data retention and export policies can be tightened with restricted sharing. No external links are created automatically without explicit configuration.
The system supports multi-model fallbacks; if OpenAI usage limits are reached, prompts can reroute to alternative models like Claude or Gemini. The fallback maintains report generation with slightly adjusted prompts. Outputs remain structured, though response latency might increase. This ensures continuity without manual intervention.
Yes. Report templates and extraction prompts can be adjusted to match your exact field mappings and formats. You can add new report types by extending prompts and reusing the same data pipeline. Changes apply without major architectural updates. This enables rapid expansion to AP, revenue, and margin reports.
Automate Sabre EDI parsing to produce AR and Tax reports using RAG and GPT-4.
