Monitors CSV/XLSX uploads, fetches LinkedIn data via Apify, scores each candidate with GPT-4.1, generates Hebrew explanations, formats sheets, and alerts on errors.
The AI agent converts your candidate list into a decision-ready evaluation. It enriches profiles with LinkedIn data and context, scores candidates on a 0-100 scale, and explains each score in Hebrew. It outputs a neatly formatted sheet with filters, sorting, and clear decision signals for recruiters.
Performs end-to-end candidate evaluation in one workflow.
Convert CSV/XLSX to Google Sheet with RTL formatting
Fetch up to 3 recent LinkedIn posts per candidate via Apify
Evaluate each candidate against job requirements using GPT-4.1 and a transparent rubric
Generate Hebrew explanations for each score
Sort results by score and apply professional formatting
Monitor for errors and send alerts when issues occur
Before adopting this AI agent, HR teams faced manual data handling, inconsistent scoring, and delays in decision-making. After adoption, you get standardized scoring, LinkedIn-enriched candidate context, Hebrew explanations, batch processing, and proactive error alerts.
A simple 3-step flow from upload to decision-ready results.
Submit a CSV/XLSX; the AI agent stores it in Google Drive and creates a new Sheet with RTL formatting.
For each candidate, fetch LinkedIn posts via Apify, compare against job requirements, and compute a 0-100 score using GPT-4.1.
Format the sheet, apply filters and sorting, generate Hebrew explanations, and alert admins of any errors.
One realistic scenario demonstrates task, time, and outcome.
Task: evaluate 15 candidates for a data analyst role from a CSV. Time to process: approximately 5–7 minutes. Outcome: a Google Sheet sorted by score (0-100) with Hebrew explanations for each candidate, ready for review.
One supporting sentence.
Standardize evaluation and accelerate shortlisting across teams.
Scale consistent client scoring for multiple roles.
Access data-driven insights to speed decisions.
Maintain auditable scoring with LinkedIn context.
Handle higher volumes without increasing headcount.
Provide Hebrew explanations for stakeholders preparing offers.
One supporting sentence.
Stores inputs, creates a new Sheet, and applies RTL formatting to match Hebrew data.
Fetches up to 3 recent LinkedIn posts per candidate to inform evaluation.
Calculates scores, generates Hebrew explanations, and enforces the scoring rubric.
Sends error alerts and notification emails to admins.
Six practical scenarios where the AI agent adds value.
Common questions and practical answers.
The AI agent accepts CSV and XLSX inputs. It converts them into a Google Sheet with RTL formatting to preserve Hebrew data and layout. The workflow handles both formats automatically, so you can upload whichever you already have. If a file contains extra columns, you can map them during setup. The system processes candidates sequentially, but it scales to larger batches by batching the work.
No coding skills are required. The solution is template-based and config-driven: you provide the CSV/XLSX, connect Google Drive/Sheets, Apify, and OpenAI, and you’ll manage credentials. The platform handles the orchestration and error alerts. You can adjust the scoring rubric and language settings through the UI or configuration files. Advanced users can customize integration points if needed.
LinkedIn data is scraped through Apify within rate limits and usage policies. The AI agent uses a robust scoring rubric but treats LinkedIn data as contextual input rather than a singular decision factor. You get a score plus a Hebrew explanation that reflects the data quality. You should review flagged items and update URLs when needed.
Yes. The scoring rubric is configurable (weights like 50/25/15/10) and can be adjusted to align with job requirements. You can tune which signals matter most (experience, activity signals, or relevance to roles). Any changes apply consistently across all candidates in a batch. You can also save multiple rubric presets for different roles.
The agent supports Hebrew by default and can switch to other languages. Hebrew explanations are generated to provide context about why a score was assigned. You can enable bilingual reporting if needed. Translation settings can be adjusted per job or per client.
Errors are logged and surfaced via email alerts to admins. The agent continues processing other candidates where possible and retries failed steps where appropriate. You’ll see error details in the sheet and can trigger a quick remediation flow. The system also sends a summary of issues after each batch run.
Cost is approximately $0.05 per candidate, depending on the exact data pull and post count used. The pricing is designed for batch processing of typical recruitment pipelines. You’ll see the per-candidate cost in your usage report. This makes it easy to budget for high-volume hiring. The system optimizes prompts and post fetch to maximize value.
Monitors CSV/XLSX uploads, fetches LinkedIn data via Apify, scores each candidate with GPT-4.1, generates Hebrew explanations, formats sheets, and alerts on errors.
