A configuration engine that anchors foundation models into a specialized evaluator, where static constraints shape a dynamic conversation.
Every Context Pack is built from 10 configurable fields that control how the AI interviewer behaves. Here's a closer look at 3 of them.
Collaborative, asks "why" more than "what". Creates psychological safety.
Probing, seeks edge cases & failure modes. High signal extraction.
Focus on impact and trade-offs. Values pragmatism over theory.
Theory-first, explores novel approaches. Deep technical rigor.
"You're building a GNN recommender. New users have only 2-3 interactions. Walk me through how you'd generate meaningful embeddings without enough neighborhood data."
"Your GNN was serving predictions in 50ms. After scaling to 100M nodes, latency jumped to 500ms. What's your diagnosis process?"
"Your GNN's performance drops 15% after 2 months. How would you detect this drift early and design an incremental retraining strategy?"
The same Context Pack architecture, configured for exactly the workflow you need.
"Scale this up…", "What broke when…"
Technical Detective
Production edge cases, system limits
"Trade-off between…", "Walk me through…"
Product-Minded Engineer
Shipping speed, tool familiarity
"How would you teach…", "Walk me through…"
Curious Senior Colleague
Fundamentals, conceptual clarity
The AI doesn't just "chat." It continuously checks responses against your defined Evidence Requirements.
If a candidate provides a vague answer when the Context Pack demands Metrics, the system detects the gap and triggers a probe to extract the missing data.
High-signal reports without human supervision.
AI receives and analyzes the answer in real-time.
Compares against required evidence types.
Identifies missing or vague information.
Triggers specific follow-up before proceeding.