Uncovering Candidate Potential With Our LLM

We built our Model to prevent hallucinations by controlling the environment. Instead of parsing unregulated resumes our LLM uses our technology which structures candidates experience, projects, hyperlinks, and more into dicernable patterns that improves our LLM's effectiveness. We created the bridge so that everyone can easily pass through.

With this technology we have been able to create easily accessible Vectors in our FAISS which allows us to do more with less. This technology has allowed our LLM to read structured Candidate Profiles where information is easily transitioned and turned into realiable reasoning when employers search for their ideal candidates.

By doing this we cut the cost to run our LLM while also allowing employers and universities to deploy our technology with ease through a semi-traditional job-posting-like search feature. Here employers fill our a form, detailing who they want. Our LLM then pull top ranking candidates that match that search and shares its reasoning.

Through building and testing our LLM we've analyzed over 5,000 anonymized profiles, we found that LLM-based evaluation identifies contextual strengths such as problem solving, adaptability, and initiative more accurately than keyword-driven systems. This marks a major step toward transparent, human-centered hiring. And we will work to publish a paper detailing our findings and continual findings later on.

All of this allows our model to not only matches candidates to roles but also generate recruiter-friendly summaries, ensuring decision-makers see candidates in context — not as filtered data points. This shift enables faster, fairer, and more meaningful connections between employers and job seekers.