Executive Summary
A concise overview of how RightSourceAI leverages next-gen RAG techniques; specifically Graph RAG and multi-agent architectures; to solve one of the most pressing challenges in recruitment: matching accuracy. With a sharp focus on speed, precision, and cost-efficiency, the platform delivers a powerful tool for engineering teams and recruiters in fast-scaling SaaS environments.
Industry
Initial Go-to-
Market Timeline
Tech Stack
Team
Composition
Client Background
RightSourceAI was developed to address one of the most persistent and costly challenges in technical hiring: slow screening cycles driven by inaccurate candidate matching.
Most traditional applicant tracking and sourcing systems rely on keyword matching or vector similarity. While these approaches can surface large volumes of candidates, they introduce two critical problems:
These issues compound into longer hiring cycles, higher recruiter costs, and excessive time spent by engineering leaders in interviews that should never have happened.
Originally built to support Athenaworks’ internal recruiting operations, RightSourceAI was designed to scale across both SaaS and enterprise organizations hiring for complex engineering, data, and product roles.
“RightSource uses Graph RAG and multi-agent AI to understand job descriptions and resumes the way a senior recruiter would—semantically, not by keywords. The result is fewer candidates to review, better matches surfaced faster, and significantly less time wasted by recruiters and engineering teams.”
Value Delivered
From over 2 minutes to just 30–40 seconds, enabling recruiters and hiring managers to work more efficiently and review more profiles in less time.
Significantly enhances candidate-job fit, reducing false positives and negatives, and improving interview-to-hire ratios, by using Graph RAG for parsing and matching JD.
The platform can handle larger talent pools of tens of millions of candidates without degrading performance, thanks to being built on modular, cloud-native microservices infrastructure with multi-agent orchestration.
Recruiters can access and review matched candidates for free, eliminating barriers to trial and encouraging organic product adoption.
The platform benefits both recruiting teams (faster screening, better matches) and engineering leads (higher-quality pipelines, less screening burden); accelerating hiring while maintaining technical quality.
The Challenge
Recruiting and engineering teams face two compounding issues:
Even modern AI-powered recruiting tools struggle to deliver context-rich matches at speed. For time-sensitive technical hiring, this leads to:
RightSource introduces a new candidate-matching architecture built on:
Together, this enables deep semantic matching at production scale.
Optional integration with Athenaworks’ Gatekeeper service for curated, pre-interviewed candidates
Why Graph RAG Over Traditional RAG?
Feature
Traditional RAG (Vector DB)
Graph RAG (Graph DB – AWS Neptune)
Flat embeddings
Rich relationship graphs
Similarity-based
Context-aware, connection-based
Accuracy
Often noisy results
Higher semantic relevance
Example Use Case
Keyword match on “DevOps”
Maps job responsibilities to candidate experiences & certifications
By introducing Graph RAG, RightSourceAI tackles one of enterprise AI’s biggest blockers: accuracy at scale.
Why Graph RAG Over Traditional RAG?
Data Structure
Traditional RAG (Vector DB)
Flat embeddings
Graph RAG (Graph DB – AWS Neptune)
Rich relationship graphs
Retrieval Logic
Traditional RAG (Vector DB)
Similarity-based
Graph RAG (Graph DB – AWS Neptune)
Context-aware, connection-based
Accuracy
Traditional RAG (Vector DB)
Often noisy results
Graph RAG (Graph DB – AWS Neptune)
Higher semantic relevance
Example Use Case
Traditional RAG (Vector DB)
Keyword match on “DevOps”
Graph RAG (Graph DB – AWS Neptune)
Maps job responsibilities to candidate experiences & certifications
By introducing Graph RAG, RightSourceAI tackles one of enterprise AI’s biggest blockers: accuracy at scale.
To meet user expectations of “real-time” results, RightSourceAI incorporates multi-agent orchestration:
This mirrors how modern search engines and distributed systems optimize latency; bringing consumer-grade responsiveness to enterprise-grade AI.
