Automate resume intake, parsing, scoring, and routing across Gmail, HubSpot, Slack, Google Drive, and PostgreSQL.
Monitors Gmail for new resume submissions and validates attachments. Parses PDFs into structured data with 45+ skill mappings. Scores candidates on a 100-point scale, routes qualified leads to HubSpot, archives in Drive, notifies Slack, and logs analytics in PostgreSQL.
Converts unstructured resumes into actionable candidate profiles.
Ingests Gmail submissions and validates PDF size and type.
Parses PDFs into structured JSON data.
Extracts contact details, LinkedIn URLs, and 45+ skills across 7 categories.
Calculates total years of experience by analyzing date ranges in resumes.
Scores candidates on a 100-point scale and assigns A+, Qualified, or Below Threshold.
Routes qualified candidates to HubSpot, archives in Google Drive, notifies via Slack, and logs analytics in PostgreSQL; sends personalized feedback for unqualified candidates.
Before: manual screening is slow and inconsistent; PDFs arrive unstructured; data is scattered across Gmail, Drive, and spreadsheets; feedback to applicants is delayed; funnel metrics are opaque. After: screening is faster and consistent; data is centralized; qualified leads are routed automatically; candidates receive timely feedback; analytics are visible for optimization.
A simple 3-step flow for non-technical users.
Monitors Gmail for new resume submissions, validates PDF size under 10 MB, and filters out invalid attachments.
Parses PDFs into structured JSON data and maps 45+ skills across seven categories to a candidate profile.
Scores candidates on a 100-point scale, routes qualified profiles to HubSpot, archives in Google Drive, notifies Slack, and logs metrics in PostgreSQL.
One realistic scenario.
Scenario: 120 resumes arrive via Gmail in a day. The AI agent parses PDFs into structured candidate profiles, scores them on a 100-point scale, and routes 30 top candidates to HubSpot as Qualified; rest are archived and a Slack notification is sent for the top matches. All actions are logged in PostgreSQL for analytics and process improvement.
Profiles that benefit most from automated resume screening.
Standardizes intake, validation, and archival; reduces manual handoffs.
Ensures a repeatable, auditable screening process across teams.
Receives real-time top-match alerts and consistent candidate data.
Accesses structured candidate profiles with clear skill match.
Maintains auditable decision records and data lineage.
Monitors funnel performance with centralized analytics.
Seamlessly connects to your existing tools.
Monitors new resume submissions and triggers intake.
Parses resumes into structured data.
Creates/updates candidate records and lifecycle stages.
Sends real-time notifications to recruiting channels.
Archives qualified profiles and related data.
Logs data points and computes funnel metrics.
Concrete scenarios where the AI agent shines.
Common questions about this AI agent.
The AI agent ingests Gmail submissions, parses PDFs to JSON, and stores structured data in PostgreSQL. It routes data to HubSpot and Slack according to the configured routing rules. All data processing happens within the defined consent and retention settings. You can adjust which fields are extracted and how they map to your candidate records.
Data privacy is enforced through role-based access, secure storage, and audit trails. Personal data is processed only for screening and routing purposes as defined by your policy. Retention is configurable, and you can set automatic deletion after a defined period or export data for compliance review. The agent supports data minimization and purpose limitation practices.
The current design focuses on PDFs, which are common for formal applications. If non-PDF formats are received, the agent can be extended to handle them after confirming format support and extraction methods. You can configure a pre-validation rule to reject unsupported formats or route them for manual review. Future enhancements can include OCR for image-based documents.
Yes. Scoring thresholds, weights, and the skill categories are configurable to fit your hiring requirements. You can adjust the 100-point scale, define what constitutes A+, Qualified, and Below Threshold, and map different skill sets to categories. Changes apply immediately to new submissions and are version-controlled.
Processing is designed to occur in near real-time. Intake, parsing, scoring, and routing typically complete within minutes of a submission, depending on attachment size and system load. The logging in PostgreSQL provides visibility into latency and throughput for continuous improvement. You can configure batching or prioritization if needed.
Unqualified candidates receive a personalized, constructive Gmail rejection that cites missing skills or experiences. The system logs the rejection reason to PostgreSQL for feedback analytics. This preserves candidate respect and brand integrity while keeping the pipeline clean. You can adjust rejection messaging to align with your policy.
The core design centers on Gmail intake, but the agent can be extended to ingest data from other email or application sources with minimal changes. Extensions would map fields to existing candidate profiles and ensure consistent routing. This keeps the assessment process uniform across channels. You can add additional sources while preserving the same scoring and routing logic.
Automate resume intake, parsing, scoring, and routing across Gmail, HubSpot, Slack, Google Drive, and PostgreSQL.
