Reduce Your Time-to-Hire with Custom AI Recruiting Automation
AI reduces time-to-hire by instantly screening resumes against job requirements. It also automates scheduling and personalized outreach to qualified candidates.
Syntora designs and engineers AI solutions to reduce time-to-hire for service firms. By automating resume screening and candidate outreach, Syntora's approach aims to significantly cut manual recruiter time and improve candidate quality.
The scope and complexity of an AI recruiting system depend on your applicant volume and existing Applicant Tracking System (ATS). Integrating with a standard platform like Greenhouse or Lever is a common first step. A business processing 500 applicants a month across five distinct roles requires a more detailed data model and architectural consideration than one hiring for a single position.
Syntora has deep experience building secure, scalable document processing pipelines using Claude API for financial and legal documents. These same patterns and technical capabilities are directly applicable to analyzing and scoring candidate resumes and profiles, ensuring robust and accurate candidate assessment.
What Problem Does This Solve?
Most small recruiting teams rely on their ATS's built-in keyword filters, using tools like Breezy HR or Workable. This approach is brittle. A search for "Python developer" misses the candidate who lists "Django expert" or includes a GitHub portfolio full of Python projects. These filters rank resumes based on simple word counts, not semantic understanding, creating high rates of both false positives and false negatives.
A 15-person firm trying to fill a "Senior Backend Engineer" role gets 250 applications. Their ATS keyword filter for "Go" and "PostgreSQL" surfaces 30 resumes. Recruiters spend a full day screening them, only to discover most are junior developers who simply listed the terms. The best candidate, who wrote "Led a team building a high-throughput data pipeline with Golang and Postgres", was missed entirely because she did not use the exact keywords.
Generic resume parsing APIs are no better. They can extract text from a PDF into a structured format, but often fail on resumes with columns or graphics. They provide raw data without context, meaning a human still has to manually evaluate whether the extracted experience actually matches the specific requirements of the role and the company's culture.
How Would Syntora Approach This?
Syntora would begin an engagement by thoroughly auditing your current hiring workflow, ATS configuration, and historical hiring data. This discovery phase is crucial for defining the specific attributes of your ideal candidate and tailoring the system to your firm's unique needs.
The technical approach would involve connecting to your ATS API (e.g., Greenhouse, Lever) to retrieve job descriptions and relevant applicant data. Syntora would then use the Claude API to analyze your successful historical hires and collaboratively generate a structured job profile with weighted attributes. This profile, including inferred skills and required experience levels, would be stored in a Supabase Postgres database, offering flexibility and scalability.
When a new application arrives, a webhook would trigger an AWS Lambda function, typically written in Python. This function would download the resume, parse its content using PyMuPDF, and send the extracted text to the Claude API. A carefully engineered prompt, refined through iterative testing, would instruct the model to score the candidate against the defined attributes. The goal would be for this entire process to return a 0-100 match score and a concise 3-sentence summary, usually within 15 seconds.
The generated match score and summary would be written back to a custom field within your ATS via its API, allowing your team to operate within their familiar tools. Syntora could also develop a simple Vercel-hosted dashboard for hiring managers to review the ranked candidate list. For top-tier candidates, the Claude API could be used to draft personalized outreach emails, referencing specific projects or experiences from their resume, which could then be staged as drafts in the recruiter's Gmail.
Monitoring and observability are built into the system. Structured logging with structlog would track performance and system health. Automated alerts, such as Slack notifications for processing times exceeding 30 seconds or API error rates above 2%, would ensure prompt issue detection. A typical engagement for a system of this complexity, including discovery, design, development, and deployment, would span 8-12 weeks. Clients would provide ATS API access, historical data, and active participation in defining candidate profiles. This architecture is designed for cost efficiency, with typical AWS hosting costs remaining under $50 per month, depending on applicant volume.
What Are the Key Benefits?
Rank 200 Applicants in Under an Hour
Stop manual screening. The system processes resumes in seconds, allowing your team to engage the best candidates before your competitors do.
A One-Time Build, Not a Per-Seat Subscription
After a single scoped engagement, you pay only for minimal monthly hosting. No recurring SaaS fees that increase as your team grows.
You Own the Code in Your GitHub Repo
You receive the complete Python source code and deployment scripts. The system is yours to modify or extend as your business needs change.
Alerts When Performance Changes
Integrated monitoring via Slack notifies us if processing times lag or error rates increase, allowing for fixes before your recruiters notice a problem.
Works Natively Inside Your Existing ATS
Scores and summaries appear in custom fields within Greenhouse, Lever, or your current system. No new platform for your team to learn.
What Does the Process Look Like?
Scoping and ATS Connection (Week 1)
You provide read-only API keys for your ATS and example job descriptions. We deliver a data audit and a finalized project plan.
Core Ranking Engine Build (Week 2)
We build the resume parsing and candidate matching logic. You receive access to a staging environment to test rankings on historical candidates.
Integration and Automation (Week 3)
We connect the engine to your live ATS and build the outreach automation. You receive a live demo of the end-to-end workflow.
Launch and Handoff (Week 4)
The system goes live. We monitor performance for 30 days and then deliver the full source code, deployment scripts, and a maintenance runbook.
Frequently Asked Questions
- How does project scope affect cost and timeline?
- The primary factors are the number of ATS integrations and the complexity of your roles. Integrating a standard platform like Greenhouse is faster than a custom-built system. A 4-week timeline is typical for screening and ranking. Adding automated scheduling and personalized outreach can add 1-2 weeks. We provide a fixed quote after the discovery call.
- What happens if the AI misinterprets a resume?
- The system is built with human review gates. AI provides a score and a summary, but recruiters make the final hiring decision. The summary explains why a candidate scored high, allowing recruiters to quickly verify the AI's logic. This design ensures you maintain control and can easily override the system's suggestions based on your expertise.
- How is this different from an AI platform like Eightfold AI?
- Platforms like Eightfold AI are designed for large enterprises and have high subscription fees and long implementation times. Syntora builds a system for your specific workflow, using your data, without features you do not need. You own the code and pay a minimal monthly hosting fee instead of a costly per-user license. It's a bespoke tool, not a bundled platform.
- How do you address potential AI bias in hiring?
- We focus the AI on objective skills and experience listed in the resume, mapping them directly to your job description's requirements. We explicitly instruct the model to ignore demographic information. Most importantly, the human review gate ensures a person always makes the final decision, using the AI as an assistant, not an arbiter.
- How is our sensitive candidate data handled?
- All data is processed within a secure AWS environment and is never used to train models for other clients. We connect to your ATS using official, revocable API keys with read-only permissions where possible. The final system can be deployed to your own cloud account if you prefer, giving you complete control over your data and infrastructure.
- What if we use a less common or custom-built ATS?
- If your ATS has a documented REST API, we can integrate with it. The first week of the project is dedicated to reviewing your API documentation and establishing a connection. For systems without an API, we can sometimes use email parsing as an alternative, but this can affect the project timeline, which we would confirm upfront.
Ready to Automate Your Professional Services Operations?
Book a call to discuss how we can implement ai automation for your professional services business.
Book a Call