Teal

Teal is a blazing-fast AI career suite that turbocharges every step of your job hunt. In seconds, its GPT-4 engine tailors résumés with ATS-crushing keywords, auto-generates cover letters in the company’s voice, and tracks all opportunities in a Kanban-style CRM. A single Chrome click saves jobs from 40+ boards, while an AI Interview Agent preps you with STAR-scored mock sessions. Trusted by 2 million professionals, Teal cuts application time in half and doubles interview callbacks, turning weeks of stress into days of decisive action.

Natural Language Processing Stack

Teal’s core competitive edge lies in its proprietary NLP pipeline. The system ingests raw job descriptions from more than forty boards, tokenizes them with a RoBERTa-large model fine-tuned on 1.4 million U.S. job postings, and extracts hard skills, soft skills, certifications, and seniority indicators. A second transformer layer cross-references the user’s existing résumé and LinkedIn profile, generating a gap-analysis matrix that scores each missing keyword on relevance and frequency. This semantic matching is not simple keyword stuffing; the model uses cosine-similarity thresholds to ensure contextual fit, reducing false positives by 38 percent compared with traditional TF-IDF approaches.

Dynamic Résumé Builder

The résumé builder employs a conditional text-generation engine built on GPT-4-turbo with retrieval-augmented generation (RAG). When a user clicks “Tailor résumé,” the engine calls the top 20 most relevant bullet-point snippets from a vector database of 2,000+ ATS-approved examples. These snippets are then re-written on the fly to mirror the user’s actual experience, tone, and metrics, ensuring that each résumé is both unique and keyword-optimized. The entire process completes in under four seconds, a latency benchmark Teal achieved through quantized inference on NVIDIA A100 GPUs hosted on AWS us-east-1.

Interview Simulation Layer

Premium subscribers unlock an AI Interview Agent that uses few-shot prompting to simulate behavioral, situational, and technical questions. The agent analyzes the résumé and job description to predict likely interview topics, then scores user responses on the STAR (Situation, Task, Action, Result) rubric. Speech-to-text via Whisper provides real-time transcription, while a sentiment-analysis micro-service flags filler words and confidence markers.