Hiring Assessment Platform
Rebuilt and integrated an adaptive quiz service to qualify software engineers at scale
Introduction
After acquiring Triplebyte for its predictive quizzes, Karat expanded beyond live technical interviews by bringing quiz-based qualification into its platform.
In collaboration with a small team, Ferrer Labs helped build what became Karat Qualify — an on-demand, 15-question multiple-choice assessment that leverages adaptive technology and assessment theory to evaluate knowledge across technologies, languages, and "real-world" engineering topics.
The work centered on designing the service boundaries and integration approach, implementing the majority of the Qualify backend, contributing significantly to the platform integration, and shipping a small amount of dashboard UI.
The Challenge
The product goal was clear: deliver a candidate experience that feels instant, produces signals that hiring teams can trust, and supports a growing catalog of quiz topics — frontend, backend, algorithms, Python, and more.
The engineering reality was more complex. This effort bridged two systems with very different histories: Karat's existing platform (authentication, candidate lifecycle, reporting surfaces) and a quiz service that already existed but carried legacy behavior, older data models, and long-tail edge cases. The timeline was tight — beta was targeted within two quarters and reached within one — so correctness and stability had to be achieved early.
On top of that, the system needed to operate under SOC 2 and GDPR expectations while staying low-latency for interactive question serving and resilient under high usage assumptions — without introducing fragility into the broader platform.
Approach
Rebuilding Qualify into a clean, service-first backend
We rewrote the service to remove legacy complexity, cutting ~75% of the code while making it safer to evolve under a tight schedule.
Adaptive question selection with continuous scoring
The quiz adapted in real-time — picking the next question based on how the candidate was performing, giving a more accurate signal in fewer questions.
Designing reliable integration with Karat's platform
We integrated with Karat's existing systems for authentication, candidate progress tracking, and results delivery — designed so nothing got lost or duplicated.
Operational readiness under strict standards
The system was designed with production constraints as a baseline: low-latency paths relied on caching (authentication and question serving), while background work handled retries and synchronization. Observability supported day-to-day reliability via Sentry and CloudWatch, and the data pipeline supported both operational analytics and model training by persisting answers for downstream processing.
Results
A beta version shipped within one quarter, despite the complexity of integrating an acquired quiz system into an existing platform with strict compliance expectations.
From an engineering standpoint, the rewrite reduced legacy risk and created a cleaner foundation for iteration: fewer edge cases, clearer service boundaries, and a data model that was easier to reason about. That simplicity mattered because it stabilized the integration surface — progress and grading signals could flow reliably between services without repeated regressions.
On the product side, the integration enabled capabilities requested in customer conversations, including configurable quiz topics and digest-style visibility into candidate activity and results — supporting sales motions without requiring another major rebuild.
Tech Stack
Want to build something similar?
Let's discuss how we can help integrate an acquired product, rebuild a legacy service, or ship a production-ready microservice.