v2.0 Agentic Framework

Automate Your Technical Interviews with AI

KP Agentic utilizes autonomous agents and FAISS vector databases to conduct dynamic, context-aware technical screenings. Evaluate engineers instantly with zero bias and enterprise-grade accuracy.

Core Capabilities

Intelligent Candidate Screening

Move beyond multiple-choice tests. Our Agentic AI conducts real conversational evaluations.

Dynamic Question Generation

The AI adapts to candidate responses in real-time. If a candidate struggles with Machine Learning concepts, the agent pivots to assess their core Python backend skills.

Bias-Free Evaluation Matrix

Every answer is strictly scored against an objective FAISS knowledge base. The Llama-3.1 agent grades purely on concept correctness, technical depth, and clarity.

Actionable Analytics

Instantly generate performance dashboards for every interview session. Identify a candidate's specific weak points before passing them to your senior engineering team.

Intelligence Hub

Latest Technical Insights

The Rise of Agentic RAG: Moving Beyond Simple Chatbots to Autonomous Intelligence in 2026.
Artificial Intelligence
1 months ago
The Rise of Agentic RAG: Moving Beyond Simple Chatbots to Autonomous Intelligence in 2026.

Explore how Agentic RAG and Neural Evaluation are redefining technical infrastructure. Learn why context-aware autonomy is the next frontier for Enterprise AI.

By Koustubh 41 reads
Architecting Context-Aware Reranking (CAR) for High-Dimensional RAG Systems
AI Engineering
1 months ago
Architecting Context-Aware Reranking (CAR) for High-Dimensional RAG Systems

Stop relying on basic vector search. Explore the NAI Formula and Context-Aware Reranking (CAR) at KP Agentic. Optimize RAG systems with <120ms p99 latency and semantic clustering.

By Koustubh 73 reads
Advanced Retrieval: Optimizing FAISS for Agentic RAG
RAG
1 months ago
Advanced Retrieval: Optimizing FAISS for Agentic RAG

Learn how KP Agentic leverages FAISS vector search to reduce AI reasoning latency in RAG-based technical interview pipelines.

By Koustubh 39 reads

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Interviews Conducted

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Hiring Workspaces

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Evaluation Accuracy

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Agent Response Time

Agentic Interview Architecture
Agentic Evaluator Active
System Architecture

How the AI Interviewer Works

  • 1
    Knowledge Grounding (FAISS)

    We ingest standard computer science curricula, specific job descriptions, and corporate style guides into a high-speed FAISS vector database.

  • 2
    Candidate Interaction

    The AI issues domain-specific questions (e.g., Python Backend or ML). As the candidate answers, the agent actively parses their logic and code structure.

  • 3
    Algorithmic Scoring

    The agent cross-references the candidate's answer with the embedded vector truth, returning a strict 1-10 score and professional feedback matrix to the hiring manager.

Deployment Options

Scalable Interview Licensing

Choose a provisioning tier that aligns with your engineering candidate volume.

Candidate Simulator

₹0/mo

  • Pre-loaded FAISS interview datasets
  • Standard RAG agent feedback
  • Personal performance dashboard
Start Practicing Free
Enterprise HR
Corporate Evaluator

₹499/mo

  • Inject custom job descriptions
  • Candidate vs. Candidate comparison
  • Unlimited API calls & evaluations
  • Priority Flask integration support
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Technical FAQ

Because the Llama-3.1 agent dynamically assesses the conversational context rather than just looking for exact keywords, copy-pasted Wikipedia answers often score low on "clarity" and "depth." The AI specifically looks for logical reasoning and real-world application, not just textbook definitions.

Standard LLMs hallucinate. By using a RAG architecture with FAISS (Facebook AI Similarity Search), we strictly ground the agent. The AI is forced to compare the candidate's answer directly against verified, high-dimensional vector embeddings of actual computer science documentation.

Yes. The Corporate Evaluator tier allows HR teams to inject custom JSON datasets containing domain-specific questions (e.g., React Native, DevOps, CyberSecurity). The agent instantly adapts its evaluation matrix to the new data.

Ready to Transform Your Engineering Hiring?

Stop relying on subjective interviews. Deploy an objective, scalable, and fully autonomous technical assessment pipeline today.

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