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Lucas Augusto Kepler
Lucas Augusto Kepler

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TechAssist: AI-Powered Assistant for IT Support Technicians

#devchallenge #algoliachallenge #ai #agents

This is a submission for the Algolia Agent Studio Challenge: Consumer-Facing Conversational Experiences

What I Built

TechAssist is an intelligent conversational assistant designed for IT support technicians during live customer calls. The system helps helpdesk analysts quickly diagnose issues and find solutions by searching through indexed documentation, historical tickets, and system information.

The problem it solves: IT support teams often struggle with long resolution times because they need to manually search through multiple knowledge bases, past tickets, and documentation while keeping the customer on the line. TechAssist acts as a real-time copilot that understands the context of the issue and retrieves relevant information instantly.

Key features:

  • Contextual chat interface where technicians can describe the problem in natural language
  • Context panel to input system details (system name, version, environment, error message)
  • Structured diagnostic responses with actionable steps
  • Similar tickets retrieval to show how past issues were resolved
  • Sub-50ms search latency for real-time assistance during calls

Demo

Live Demo: teki-kappa.vercel.app

GitHub Repository: github.com/ScryLk/teki

How I Used Algolia Agent Studio

Algolia Agent Studio is the core intelligence layer of TechAssist. I chose it because support technicians need instant, accurate responses grounded in real data, not hallucinated answers from a generic LLM.

Data Indexed

I created four Algolia indices to serve as the knowledge base:

Index Records Description
documentacoes 10 SOPs, technical manuals, and operational procedures
tickets 12 Historical support tickets with resolutions
sistemas 8 System information, versions, and known bugs
solucoes 1 Validated solutions uploaded by the team

Each record is structured with searchable attributes optimized for technical queries:

{
  "objectID": "ticket_001",
  "titulo": "Erro 500 no Fluig após atualização",
  "descricao": "Servidor retorna HTTP 500 ao acessar processos",
  "sistema": "Fluig",
  "causa_raiz": "Cache do Wildfly corrompido após deploy",
  "solucao": "1. Parar o Wildfly 2. Limpar pasta standalone/tmp 3. Reiniciar",
  "tags": ["fluig", "wildfly", "erro-500", "cache"],
  "data_resolucao": "2025-12-15"
}
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Agent Configuration

I configured the TechAssist agent in Algolia Agent Studio with:

System Prompt: A specialized prompt that instructs the agent to act as a senior IT support specialist, always grounding responses in the indexed data and providing structured diagnostics.

Tools: Connected all four Algolia indices as search tools, allowing the agent to query documentation, tickets, systems, and solutions based on the conversation context.

LLM: OpenAI GPT-4 via Agent Studio for natural language understanding and response generation.

Integration

The frontend calls Algolia Agent Studio through a single API endpoint:

const response = await fetch(
  `https://${ALGOLIA_APP_ID}.algolia.net/agent-studio/1/agents/${AGENT_ID}/completions`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-algolia-application-id': ALGOLIA_APP_ID,
      'x-algolia-api-key': ALGOLIA_API_KEY,
    },
    body: JSON.stringify({ messages }),
  }
);
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Why Algolia Was the Right Choice

Requirement Algolia Solution
Speed during live calls Sub-50ms search latency
No hallucinations RAG grounded in indexed data
Relevance control Custom ranking and facets
Simple integration Single /completions endpoint

The combination of fast retrieval and LLM orchestration in one API made it possible to build a responsive assistant that technicians can actually rely on during high-pressure support calls.

Tech Stack

  • Frontend: Next.js 14, React, Tailwind CSS
  • AI/Search: Algolia Agent Studio
  • Indices: 4 Algolia indices (documentacoes, tickets, sistemas, solucoes)
  • LLM: Google Gemini 2.5 Fast (via Agent Studio)
  • Deployment: Vercel

Conclusion

TechAssist demonstrates how Algolia Agent Studio can power domain-specific conversational experiences where accuracy and speed are critical. By grounding the AI in structured, indexed data, the assistant provides reliable diagnostics that support teams can trust during real customer interactions.

The project is open source and can be adapted for any organization that needs to empower their support teams with AI-assisted knowledge retrieval.

Built with Algolia Agent Studio for the Algolia Agent Studio Challenge 2026.

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