Loving Work

A video-first candidate experience introduced complexity that traditional job boards do not face. 

The platform needed stable recording, compression, playback, and uploads across browsers and devices, while still feeling approachable for candidates recording themselves for the first time. 

Job, application, and profile data also had to stay consistently up to date without creating unnecessary load, especially in flows where users wait for AI analysis results or status changes.

TechMagic focused on reliability, performance, and user confidence. We designed recording and upload flows with clear progress feedback, thumbnail and orientation handling, and browser-specific behavior that prevents silent failures. Video handling was engineered to remain stable across environments, with explicit user guidance when limitations exist.

To keep data fresh without overloading the app, we implemented a data strategy that relies on background updates and conditional polling only when required, such as during AI analysis. 

We used Next.js hybrid rendering so first-load and public pages prioritize speed, while interactive video and form components rely on client-side behavior. Forms validate at natural interaction points and save intelligently, reducing friction for users and unnecessary requests for the system. URL-based state preserves search, filters, and pagination so navigation feels predictable and shareable.

Employers needed a workflow that makes job publishing and early screening faster and more consistent. Traditional hiring creates bottlenecks around writing job descriptions, reviewing large volumes of applications, coordinating early interviews, and communicating employer brand in an authentic way. 

The platform also had to support collaboration, structured hiring stages, flexible candidate views, and automation that stays understandable and under human control.

TechMagic designed employer workflows that combine video-led hiring with AI support, while keeping decision-making firmly in human hands. The platform enables video job ads and company pages that communicate culture more clearly than text-only listings. Structured hiring stages, flexible views, filtering, and bulk actions help teams manage volume efficiently.

AI features were implemented as decision support rather than black-box automation. Job descriptions can be generated through a guided Q&A flow that reflects company values. Screening produces fit scores with clear explanations, and threshold-based automation can shortlist or reject candidates while preserving a review path for edge cases. The AI interviewer enables conversational video interviews that adapt through follow-up questions and produce recordings and transcripts for team review.

The core challenge was to automate polished video production from raw footage without losing timing precision or user clarity.

There was a need to simplify how employers create branded videos that showcase culture and values for company pages and job posts, as a step that strengthens the Employer Platform before publishing. It was decided to create X10 Studio, a platform designed to reliably turn raw storytelling footage into finished, branded videos. The challenge was doing this without losing timing precision or user clarity. That required transcription with millisecond-level timing, optional B-roll understanding and selection, structured story hooks, and automated rendering.

Because these steps take time, the platform also had to provide clear progress visibility and remain stable during long-running operations such as transcription and rendering.

TechMagic focused on reliability, performance, and user confidence. We designed recording and upload flows with clear progress feedback, thumbnail and orientation handling, and browser-specific behavior that prevents silent failures. Video handling was engineered to remain stable across environments, with explicit user guidance when limitations exist.

To keep data fresh without overloading the app, we implemented a data strategy that relies on background updates and conditional polling only when required, such as during AI analysis. 

We used Next.js hybrid rendering so first-load and public pages prioritize speed, while interactive video and form components rely on client-side behavior. Forms validate at natural interaction points and save intelligently, reducing friction for users and unnecessary requests for the system. URL-based state preserves search, filters, and pagination so navigation feels predictable and shareable.

LovingWork now runs as one connected ecosystem that supports candidates, employers, and content workflows across three integrated products.

Candidates can discover jobs and apply faster through guided video creation, reusable video assets, training tools, and AI feedback that supports improvement.

Employers can publish video job posts, manage structured hiring pipelines, and use AI-assisted screening and interviews with transparent outputs and team-friendly review.

Employers can generate polished, branded videos from raw footage, with clear progress tracking and an iteration flow that supports multiple story variations.