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Discover how Geektrust reduced interview setup time by 66% and achieved 90% cost savings with an autonomous AI Interviewer Agent built for scale and domain precision.
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Discover how Geektrust reduced interview setup time by 66% and achieved 90% cost savings with an autonomous AI Interviewer Agent built for scale and domain precision.
The expert: Krishnan Nair, CEO and Co-Founder at Geektrust.
The organization: Geektrust, a Bengaluru-based tech-hiring startup helping companies evaluate software engineering talent through real-world coding challenges and AI-powered assessments.
The problem: Geektrust’s existing ML-driven code assessment tool, Codu.ai, was limited in functionality and expensive to maintain—hindering scalability and requiring intensive development resources.
The solution: A scalable, context-aware AI Technical Interviewer Agent that automates initial technical interviews, delivering reliable and customizable assessments.
The limitations and challenges of Geektrust's initial ML-driven code assessment platform, Codu.ai.
How OpenAI's technologies reshaped Geektrust's product strategy and operational efficiency.
The AI-powered Interviewer Agent, its capabilities and measurable outcomes.
A problem-solution-outcome mapping.
Provides an overview of the technologies powering the agent, from frontend to infrastructure.
The phased approach used to design, test, and roll out the AI Technical Interviewer Agent.
The key technologies: asynchronous architecture, dynamic IDEs, and LLM engineering.
Strategies for resolving technical issues, ensuring resilience and continuity during interviews.
How Geektrust's system integrates with ATS platforms and client workflows to ensure seamless adoption.
Specific challenges of designing prompts for autonomy, reliability, and dynamism in agents.
Lessons learned and actionable takeaways.
Case study insights in a nutshell with closing thoughts.
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