Build AI Recruiting Automation That Plugs Into Your HR Stack
Yes, custom AI solutions integrate with small business HR software using APIs. This connects AI-powered resume screening and ranking directly into your existing ATS.
Syntora specializes in custom AI integrations for small business HR software. Our approach focuses on developing tailored solutions that leverage advanced LLMs like Claude 3 Sonnet API for resume screening and ranking, providing a detailed technical proposal rather than a pre-packaged product.
The feasibility and complexity of such an integration largely depend on your HR software's existing API capabilities. A modern applicant tracking system (ATS) with a well-documented REST API, like Greenhouse or Lever, generally allows for a more straightforward integration. Conversely, a legacy system with no public API would require Syntora to build a custom connector, which adds significant development time and complexity to the project. Syntora begins each engagement by auditing your current HR system's architecture and API documentation to determine the most effective integration strategy and scope.
What Problem Does This Solve?
Recruiting teams often hit a wall with their existing HR software's filtering capabilities. Most ATS platforms rely on simple keyword matching. A search for "Python" will miss a great candidate whose resume says "FastAPI expert" because the system cannot infer the relationship between the two skills. This rigid logic surfaces unqualified candidates and hides qualified ones.
A common next step is trying to connect the ATS to an AI tool with a no-code platform. The central challenge is parsing resumes, which are unstructured PDFs. A no-code workflow can trigger when a new resume is added, but its PDF parsing block fails to extract structured data like "years of experience with AWS" from a two-column resume layout. The workflow breaks down before the AI even sees the data.
Off-the-shelf AI recruiting platforms solve the parsing problem but introduce new ones. They are often full-suite replacements that force you to abandon your existing ATS and change your entire workflow. For a small firm that just needs to solve the initial screening problem, migrating their entire operation to a new platform is too disruptive and expensive.
How Would Syntora Approach This?
Syntora's approach to integrating custom AI with your HR software would typically begin with a discovery phase to understand your specific workflow and the technical architecture of your existing Applicant Tracking System. The first step in building a resume screening system involves establishing a robust connection to your ATS. This would commonly be achieved by developing a Python service utilizing libraries like httpx to retrieve new candidate applications and their associated resume files as they are submitted. To ensure data integrity and provide a complete audit trail, every transaction – from receiving a resume to generating its final score – would be logged in a Supabase Postgres database.
For extracting structured data from resumes, the system would leverage the Claude 3 Sonnet API. Syntora would craft a highly specific prompt, instructing the large language model to emulate an expert technical recruiter, identifying and extracting a comprehensive set of features relevant to your hiring needs. This could include specific programming languages, years of experience with cloud platforms, and management history, producing a structured JSON output that offers greater reliability than basic text parsing. This parallels our experience building document processing pipelines using Claude API for sensitive financial documents, where accuracy and structured output are paramount.
Following data extraction, a custom ranking algorithm, typically implemented as a Python script, would score the identified features against the ideal profile for the open role. For example, a candidate matching 8 out of 10 required skills might receive a score of 80. The core processing, from resume receipt to score generation, is designed to be highly efficient, usually completing within seconds.
The entire service would be packaged in a Docker container for consistent deployment and then deployed as an AWS Lambda function. Syntora would configure a webhook within your ATS to trigger this Lambda function upon the arrival of each new applicant. Within seconds of processing, the function would write the computed score and a concise summary of the candidate's strengths back into a designated custom field on the candidate's profile within your ATS. This allows recruiters to access crucial insights directly within their familiar software environment without needing to navigate external tools. Syntora aims for a typical project timeline of 6-10 weeks for such a system, with the client providing access to their ATS documentation, example resumes, and actively participating in defining desired candidate features. The deliverables would include the deployed, production-ready AI integration, full source code, and comprehensive documentation.
What Are the Key Benefits?
Get Candidate Scores in Seconds, Not Hours
Automated screening delivers a candidate score and summary in under 8 seconds. Your recruiters can immediately focus on the top 10% of applicants instead of manually reading every resume.
Stop Paying Per Recruiter Seat
This is a one-time build engagement, not a recurring SaaS subscription. After launch, you only pay for cloud hosting, which is typically under $20 per month.
You Own the Ranking Algorithm
We deliver the complete Python source code in your private GitHub repository. You have full control to modify the scoring logic as your hiring needs change.
Alerts Before Your Team Notices a Problem
We configure AWS CloudWatch monitoring to send a Slack alert if the API error rate exceeds 1% or latency increases. We know about issues before they impact your workflow.
Works Inside Your Current ATS
The solution integrates directly with tools like Greenhouse, Lever, and Breezy HR. No new software for your team to learn; scores appear in the system they already use every day.
What Does the Process Look Like?
API Access and Role Definition (Week 1)
You provide read/write API credentials for your ATS. We work together to define the ideal candidate profile, which becomes the basis for the AI's scoring logic.
Core Engine Build (Weeks 2-3)
We build and test the Python service that extracts data from resumes and scores candidates. You receive a demo showing scores for 10 of your past applicants.
ATS Integration and Deployment (Week 4)
We deploy the system on AWS Lambda and configure the webhook in your ATS. You receive the live system, with scores appearing on new candidates in real time.
Monitoring and Handoff (Weeks 5-8)
We monitor system performance and accuracy for 30 days post-launch. You receive the full source code and a technical runbook explaining the architecture and maintenance.
Frequently Asked Questions
- How much does a custom AI integration cost?
- Pricing is scoped based on the quality of your HR software's API. A modern ATS with a well-documented REST API is a standard project. An older system that lacks a public API may require a custom web scraper, which adds complexity. A typical build takes four weeks and is a fixed-price engagement. Book a discovery call at cal.com/syntora/discover for a quote.
- What happens if the AI model misreads a resume?
- The system is designed with human review gates. The AI provides a score and summary to guide recruiters, not replace their judgment. We also log every AI output. If a recruiter notices a consistent error pattern, such as misinterpreting a specific certification, we can update the AI's prompt to correct the behavior in under an hour.
- How is this different from an off-the-shelf AI recruiting tool like Eightfold.ai?
- Eightfold.ai is a complete talent platform designed to replace your core HR systems. Syntora builds a lightweight AI layer that enhances your existing ATS. We automate a single, high-value task (resume screening) without forcing you to migrate platforms or change your core workflow. We build a sharp tool, not a new toolbox.
- How do you handle sensitive candidate data and PII?
- We are a data processor, not a data store. Candidate data is processed in-transit and we do not store resumes or PII long-term. Resumes are passed from your ATS to our AWS Lambda function, sent to the Claude API, and the resulting score is written back to your ATS. All data is encrypted in transit using TLS 1.2.
- How accurate is the resume screening?
- We target over 90% accuracy in identifying key skills compared to your expert recruiters. During development, we test the AI's output against 50 resumes you have already screened manually. We tune the extraction prompts until the AI's analysis aligns with your team's evaluation, ensuring it reflects what your recruiters value most.
- Can the ranking logic be changed for different job roles?
- Yes. The scoring logic is controlled by a simple configuration file in the code. You can have different profiles for a 'Senior Engineer' versus a 'Product Manager' by adjusting the required skills and their weights. This allows you to use the same system to screen for completely different roles by simply creating a new configuration.
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