Research & Development

Semantic Keyword Matching

Standard ATS optimization tools perform string-matching between resume text and job descriptions. TailorMeSwiftly implements a weighted semantic similarity approach: job description terms are decomposed into a weighted keyword graph where each term carries a relevance score derived from its position, frequency, and contextual role (hard skill, soft skill, domain term, action verb). Resume content is then mapped against this graph using semantic similarity rather than exact string matching, capturing synonyms, related terms, and contextual equivalents.

The practical difference: a resume that says "managed cross-functional teams" matches a job posting that asks for "led interdisciplinary collaboration" — because the system understands the semantic relationship, not just the surface text.

Recruiter Heatmap Simulation

Based on published eye-tracking research on recruiter resume scanning behavior (Ladders, 2018; Bogen & Rieke, 2018), TailorMeSwiftly simulates attention distribution across resume sections. The heatmap visualization surfaces which sections receive disproportionate or insufficient recruiter attention, allowing users to restructure content for maximum signal density in high-attention zones.

Recruiters spend an average of 7.4 seconds on initial resume screening. The heatmap model reflects this: it weights the top third of page one heavily, penalizes dense paragraph blocks, and flags when high-value content is buried in low-attention regions.

Ladders, Inc. (2018). "Eye Tracking Study." Bogen, M. & Rieke, A. (2018). "Help Wanted: An Examination of Hiring Algorithms, Equity, and Bias." Upturn.

No Fabrication, Ever

The system never generates experience, credentials, or metrics that aren't in your original resume. All output comes from restructuring and rephrasing what you actually wrote. The prompt architecture separates your factual content (untouchable) from the optimization operations (restructuring, rephrasing, keyword integration).

I built it this way because the alternative — an AI that invents bullet points — is worse than useless. It creates legal liability for you and erodes trust in the whole tool category. The system rewrites what you've done; it does not invent what you haven't.

Skill Gap Analysis

Given your self-reported skills and a target role, the system maps the gap between what you know and what the role requires. This mapping draws on structured skill taxonomies (O*NET, ESCO) and job market data to identify the specific knowledge gaps between you and your target position.

The output is not a generic course recommendation. It's a ranked list of specific competencies the user lacks, weighted by how frequently they appear in job postings for the target role and how trainable they are within a realistic timeframe.

Spaced Retrieval Practice

Retention scheduling follows established spaced repetition methodology (Ebbinghaus, 1885; Leitner, 1972; Pimsleur, 1967). The system tracks per-concept mastery states and schedules review at intervals calibrated to the user's demonstrated retention curve. Concepts answered correctly are scheduled at increasing intervals; concepts missed are returned to shorter intervals. This adaptive scheduling optimizes long-term retention while minimizing total study time.

The research on this is old and well-established. Ebbinghaus published his forgetting curve work in 1885. Leitner formalized the box system in 1972. What's new here is applying it to career skill acquisition specifically — calibrating the intervals and content granularity for professional knowledge domains rather than vocabulary or exam prep.

Ebbinghaus, H. (1885). "Über das Gedächtnis." Leitner, S. (1972). "So lernt man lernen." Pimsleur, P. (1967). "A Memory Schedule." Modern Language Journal, 51(2).

Mastery-Based Progression

Rather than time-based course completion, the system uses mastery-based progression: you advance when you demonstrate retained knowledge, not when you've watched a video. Each concept has a mastery level (1–5) that advances only through spaced retrieval testing, not passive consumption.

This is a deliberate design choice against the standard edtech model where "completion" means "clicked through all the slides." A user who can recall a concept unprompted three weeks later has learned it. A user who watched a 40-minute video yesterday probably hasn't.

How Briefings Are Curated

The briefing system aggregates content from technical and industry news sources, then applies multi-stage filtering: relevance scoring against your declared interests (industry, tech stack, career trajectory), recency weighting, source credibility assessment, and deduplication. The output is a personalized 5-minute briefing — not a feed optimized for engagement, but a digest optimized for professional relevance.

The distinction matters. Social media feeds are optimized to maximize time-on-platform. This system is optimized to minimize it. The goal is to get the user informed and back to work in under five minutes.

Podcast Generation

Text briefings are converted to audio using multi-voice speech synthesis, producing a podcast-format briefing with distinct host voices. The generation pipeline handles text segmentation, prosody optimization, and conversational formatting to produce listenable output rather than robotic text-to-speech.

The motivation here is simple: commute time is otherwise dead time for professional development. A five-minute audio briefing during a morning commute compounds into substantial domain awareness over months.

Why You Can See the Scoring

Unlike social media feeds, the curation logic is visible. You can see why each story was selected, adjust your interest weights, and understand the filtering criteria. This treats you as an informed adult, not an engagement target.

I built it this way because opaque recommendation systems have well-documented failure modes: filter bubbles, engagement-bait amplification, and user distrust. If the user can see the scoring function, they can correct it. That makes the system more useful over time, not less.