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AI Resume Parser & Candidate Search with Natural Language | FutureSmart Agent Platform

Pradip Nichite16 June 2026Watch on YouTube

Part of series

Ep. 1 · Agent Futuresmart Actually

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What you'll learn

  • You learn how to configure document-level processing so each resume is treated as a single unit instead of per-page extraction.
  • You discover how to define custom extraction fields via JSON schema or the UI to capture exactly the data you need from resumes.
  • You see how to properly set up and handle complex nested fields such as work experience (lists of dictionaries).
  • You understand how to search for candidates using natural language queries and multi-condition filters without manually reviewing resumes.
  • You learn how to inspect and validate extracted structured data after resumes are uploaded to the platform.

Frequently asked questions

How does document-level processing differ from page-by-page processing?
With document-level processing, each resume is treated as a single complete unit, which is better suited for recruitment purposes. Page-by-page processing splits resumes into separate pages, which is less relevant when you want to extract candidate information in a structured format.
Can I define complex fields like work experience as nested structures?
Yes, the FutureSmart platform supports nested fields such as lists of dictionaries for work experience. You define these via JSON schema or the UI so each job entry with all its details is extracted correctly.
How do natural language queries work for candidate searches?
You can search for candidates using natural language questions and multi-condition filters without manually reviewing resumes. The system interprets your query and finds matches in the extracted structured data.
What do I need to do to set up my own extraction fields?
You can define custom extraction fields via JSON schema or directly in the UI depending on what you want to extract from resumes. Then you upload the resumes and the platform automatically extracts the data according to your schema.

Topics

nlpdocument-extractionapplicationrecruitment-aistructured-data

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Description from the channel

Learn how to build a resume parsing and candidate search system using the FutureSmart Agent platform — extract structured data from resumes and search candidates using natural language queries. In this video, you'll see how to: → Configure document-level processing to treat each resume as a single unit → Define custom extraction fields using JSON schema or the UI → Handle complex nested fields like experience (list of dictionaries) → Upload resumes and inspect extracted structured data → Search candidates using natural language with multi-condition queries This approach gives you full control over what gets extracted and how candidates are matched. Want a production-ready resume parsing system with custom engineering? Email us at contact@futuresmart.ai #ResumeParsing #CandidateSearch #AIAgent #FutureSmartAI #HRTech #StructuredExtraction