Build an AI Recruiting Assistant for Your Email Inbox

Most teams start by creating Gmail filters to label or star emails containing the word 'resume'. This organizes the inbox but does nothing to extract the information from the attached PDF or DOCX files. The core bottleneck, manual review and data entry, remains unchanged. You still have to open every file, copy-paste key details, and create a card in Trello or Asana.

Then, teams try an email parsing tool. These services can pull text from attachments, but they are brittle. An uncommon file format like a .pages document or a password-protected PDF breaks the entire workflow, creating a silent failure. The extracted data is often just a wall of unstructured text, leaving you to manually find the candidate's phone number or years of experience. This simply trades one manual task for another.

A 4-person agency we worked with had a dedicated admin spending 10 hours a week processing applications into Trello. When a key client requested an urgent candidate search, their inbox received 40 applications in one day. The admin fell behind, two top candidates were not contacted for 72 hours, and one had already accepted another offer. The cost of their manual process was a lost placement fee.

Syntora would approach this problem by first understanding your exact hiring workflow, target roles, and the specific data points most valuable for your team. The first step in building the system would involve configuring an Amazon Simple Email Service (SES) rule to forward incoming applications from your jobs@ email address directly to an AWS Lambda function. This event-driven architecture would process each application as it arrives, avoiding delays associated with polling an inbox. A Python function, utilizing libraries like pypdf2 and python-docx, would extract raw text from resume attachments, covering common document formats.

The extracted text would then be sent to a language model API, such as Claude 3 Sonnet, with a prompt engineered to act as an expert recruiter. This prompt would guide the model to identify specific fields like contact information, work history, education, and skills relevant to your open positions. The model would return this data as a structured JSON object, along with a brief summary of the candidate's qualifications. Syntora has experience engineering and optimizing prompts for information extraction from diverse documents.

This structured JSON data would then be used to integrate with your chosen applicant tracking system or a tool like Trello. Syntora would develop an API integration to create new records or cards, populating them with the candidate's name, the AI-generated summary, extracted skills as tags or labels, and the original resume as an attachment.

To keep your team informed, the system could post a notification to a dedicated Slack channel via an incoming webhook. This alert would include key candidate details and a direct link to the newly created record. Syntora would configure monitoring with CloudWatch Alarms to alert your team of any system errors, ensuring ongoing reliability.

A typical engagement for a system of this complexity usually takes 4-8 weeks to build and deploy. For this, your team would need to provide access to your email service, Trello (or equivalent), and Slack for integration, along with example resumes and job descriptions for prompt engineering and testing. The deliverables would include the deployed cloud infrastructure, source code, and documentation.