Configure Your Perfect AI Interviewer. Scale Your Technical Screening.

AIVIA™ Context Pack: The Brain of Your Hiring Ecosystem
1 Pick AIVIA Project
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2 Configure Pack & Share
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3 Automated Evaluations

How It Works

5 min
Configure & Share
Set up your evaluation in minutes and share the link.
24/7
Candidates Self-Evaluate
All candidates get 2 free attempts. No scheduling overhead.
Auto
Get Results
Follow-up on prescreened talent with full context.
Engineer-led hiring   •   Direct Control   •   Own your pipeline

Each Context Pack is a customizable AI interviewer template that conducts dynamic QA screening 24/7.

git diff --traditional-evaluation --aivia-prescreening

// The patch that fixes technical hiring. Each engineer owns their pipeline with shareable links

- Traditional Approach

  • - Custom rigid prescreening systems
  • - Outsource to firms - zero customization
  • - Coordinate schedules across timezones
  • - Interview and prescreening stages not aligned
  • - Burn budget on unqualified candidates

+ AIVIA Context Packs

  • + Pick any project, configure, share link
  • + Your needs, Your evaluation criteria
  • + 24/7 async - candidates pick their time
  • + Prescreening data -> interview insights
  • + Unlimited evaluations to build pipeline

Unlimited Evaluations included with AIVIA Talent Core.
Early Adopter Bonus: Align™ (Ranking) and Scout™ (Verification) included free for 2026.

Example Context Pack: 1 of 50+ available project evaluations

LangChain RAG Systems Building Team Consensus React Performance ML Pipeline Design GNN Recommenders ✓ Multi-Agent Collaboration AWS Architecture +43 more…
Interview Context Pack: GNN Recommender System
This pack configures AIVIA's automated interviewer for dynamic GNN-based recommender system evaluations
💡 Demo Mode: Click info icons to learn what each setting drives in the interview

1 Probe Families — Style (Select up to 3)

Click info icons to see how they shape the interview

"Walk me through…"
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"What broke when…"
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"Trade-off between…"
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"Interesting, why not…"
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"How would you teach…"
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"Scale this up…"
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2 AI Persona

Click info icons to see how they shape the interview

Curious Senior Colleague
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Collaborative, asks "why" more than "what".
Technical Detective
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Probing, seeks edge cases & failure modes.
Product-Minded Engineer
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Focus on impact and trade-offs.
Academic Researcher
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Theory-first, novel approaches.

3 Scenario Cards

Click info icons to see how they shape the interview

Cold start with sparse user-item interactions
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AI may ask: "You're building a GNN recommender. New users have only 2-3 interactions. Walk me through how you'd generate meaningful embeddings for them without enough neighborhood data."
Graph grows to 100M nodes, inference slows
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AI may ask: "Your GNN was serving predictions in 50ms. After scaling to 100M nodes, latency jumped to 500ms. What's your diagnosis process and how would you optimize this?"
Temporal drift in user preferences
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AI may ask: "Your GNN's performance drops 15% after 2 months in production. How would you detect this drift early and design an incremental retraining strategy?"
A/B test shows CTR drop despite better offline metrics
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AI may ask: "Interesting situation - your new GNN model has 20% better recall@10 offline, but A/B test shows 5% CTR drop. What factors would you investigate?"

Demo Configuration — Full version available to AIVIA Talent Core members