Monitors incoming resumes, ingests and cleans content with Thordata, parses to JSON Resume Schema via GPT-4.1-mini, stores results locally and in Google Sheets, and notifies recruiters when complete.
The AI agent ingests unstructured resumes from multiple formats. It cleans and normalizes content using Thordata Universal API, converts HTML to Markdown for AI parsing. The output is a JSON Resume conforming to the JSON Resume Schema and is saved to local storage and Google Sheets for easy access and analytics.
Performs end-to-end resume parsing and data delivery.
Ingests resumes from PDFs, DOCX, HTML, or hosted links.
Cleans and normalizes content using Thordata Universal API.
Converts HTML to Markdown for reliable parsing.
Extracts fields into a JSON Resume Schema via GPT-4.1-mini.
Validates and formats the output for schema compliance.
Saves results locally and appends to Google Sheets for analytics.
This AI agent replaces manual data extraction with an automated pipeline that handles varied formats and ensures consistent outputs. It creates auditable, schema-aligned results that integrate with existing tools.
A simple 3-step flow that non-technical users can follow.
Receives a resume URL or file and passes it to Thordata Universal API for ingestion.
Thordata cleans HTML/CSS, extracts text and metadata, and converts to Markdown for AI parsing.
GPT-4.1-mini extracts structured fields and saves the JSON to local disk and Google Sheets.
One realistic scenario showing task, time, and outcome.
Scenario: An HR assistant uploads five resumes (PDFs) to the AI agent. The agent processes each resume in parallel, producing a JSON resume conforming to the JSON Resume Schema and appending a row to Google Sheets. The local disk stores a separate JSON file per resume. Expected outcome: structured candidate data ready for quick review and ATS/CRM import.
Roles that gain structured resume data for faster decisioning.
Access rapid, structured candidate data for screening.
Consolidate candidate data across multiple sources.
Maintain auditable data logs and reproducible parses.
Feed JSON resumes directly into pipelines.
Validate JSON Resume data for analytics.
Prototype and test new resume parsing flows.
Tools wired into the AI agent workflow.
Ingests and cleans resume content for extraction.
Extracts structured fields into JSON Resume Schema.
Appends parsed resumes for analytics.
Saves final JSON resumes for archival.
Six practical scenarios where the AI agent adds value.
Common questions and practical answers.
The AI agent accepts resumes from PDFs, DOCX, HTML, and hosted links. It uses the Thordata Universal API to ingest and normalize content, then employs a language model to extract structured data. Outputs conform to the JSON Resume Schema and can be retained locally or pushed to Google Sheets. If a format is not directly supported, you can convert it to a compatible format before ingestion. For best results, ensure the input contains clear text data rather than scanned images or unrendered content.
JSON Resume Schema provides a standard structure for candidate data, including basics, work history, education, skills, and more. The AI agent targets this schema to maximize interoperability with ATS, CRM, and analytics tools. This consistency reduces manual data shaping and improves searchability. You can validate the output against the schema and adjust inputs to improve coverage, such as ensuring complete contact info. Using a standard schema makes downstream integrations straightforward.
Parsed resumes are saved locally as JSON files and appended as rows in a Google Sheet for analytics. You can access the local files directly from the host machine or shared storage. The Google Sheet holds an indexed view of candidate data for quick filtering and collaboration. If you enable webhook or additional integrations, outputs can also be pushed to other apps. Access controls and credentials govern who can view or edit the results.
Data security depends on your deployment and credentials handling. Use secure API keys, encrypted storage for local files, and strict access controls for Google Sheets. The AI agent processes data in memory and stores results in designated locations only. If your organization requires, you can enable additional security measures, such as data masking in logs and restricted network access. Regular audits and credential rotation help maintain compliance.
Yes. You can adjust the model version used for extraction and tailor which fields are parsed. The workflow supports configuring the parsing prompts to emphasize specific fields like email, phone, location, and skills. You can add or remove schema attributes as needed and re-run parsing on existing inputs. Customization helps align outputs with your ATS or data warehouse requirements.
Yes. The agent can be wired to webhooks or trigger from file uploads in cloud storage, enabling near real-time parsing. You can configure triggers to initiate parsing when resumes arrive, on a scheduled batch, or via manual initiation. Notifications can be sent upon completion to recruiters or channels of your choice. This makes the flow proactive rather than manual.
Processing time scales with resume length and format complexity. Shorter, well-structured resumes parse quickly, while longer or highly formatted documents may take longer. If you run many resumes in parallel, you may hit rate limits of external APIs and incur higher usage costs. You can optimize by batching inputs, caching results, and adjusting model settings. Monitoring usage helps balance speed and cost.
Monitors incoming resumes, ingests and cleans content with Thordata, parses to JSON Resume Schema via GPT-4.1-mini, stores results locally and in Google Sheets, and notifies recruiters when complete.
