The AI stack for research & analysis 2026

For solid research you combine Perplexity (current, cited web answers), Claude (synthesis of long sources + long context), NotebookLM (source-grounded notes) and Gemini (multimodal, very long context) — facts with sources instead of hallucination.

July 5, 20265 min
ResearchAnalysisAI stack 2026

In short

For solid research you combine Perplexity (current, cited web answers), Claude (synthesis of long sources + long context), NotebookLM (source-grounded notes) and Gemini (multimodal, very long context).

The result is facts with sources instead of hallucination. The ground rule: never trust a single AI blindly — always ≥2 sources, and for drift-prone facts (prices, model IDs, numbers) check the primary source.

Las sesiones semanales de IA ya están integradas en toda la web.

Cada jueves a las 23:00 Asia/Ho_Chi_Minh, el formato combina filtro de mercado, casos prácticos, preguntas y siguientes pasos claros.

jueves, 9 de julio de 2026, 23:00 · hora de Vietnam1 vez por semanaQ&A en vivo
  • para founders, equipos y responsables operativos
  • centrado en casos reales de negocio y no en teatro de IA
  • con calendario inicial y serie de arranque fija

Próxima sesión: jueves, 9 de julio de 2026, 23:00 · hora de Vietnam. Después la serie sigue con ritmo semanal.

Escena de sesión en vivo y enablement de equipo

The research stack

The stack for sourced reports with a citation requirement. Prices as a ballpark, as of July 2026, vendor page authoritative.

TaskTool (recommended)WhyPrice
Live web researchPerplexityCurrent answers with live source citations
Synthesis / reportClaudeLong context, synthesis of many sources€€
Source groundingNotebookLMNotes strictly on uploaded sourcesFree/€
Multimodal / long docsGeminiVery long context, multimodal
Data analysisClaude / code interpreterComputes and checks data instead of guessing€€

How it works together

From gathering with citations to the final, sourced report.

1

1. Perplexity + Gemini gather (with citations)

Current sources and long documents are captured.

2

2. NotebookLM grounds on the uploaded sources

Answers stay strictly tied to the source material.

3

3. Claude synthesizes the final, cited report

Many sources become a solid, sourced write-up.

Common mistakes

What makes research results unusable.

  • Trusting a single AI blindly — always pull ≥2 independent sources.
  • Quoting drift-prone facts (prices, model IDs, APIs, platform numbers) from memory instead of checking the primary source.
  • Not documenting sources — without a citation the report isn't verifiable.

Frequently asked questions

Why not just use ChatGPT for research?

A single LLM working from memory hallucinates on current and drift-prone facts. The stack deliberately separates: Perplexity/Gemini fetch current sources with citations, NotebookLM grounds on uploaded material, Claude synthesizes. The result is a sourced report instead of a plausible but unverified answer.

What is NotebookLM and what for?

NotebookLM answers questions strictly based on the sources you upload — rather than from general model knowledge. That's ideal when statements must come only from defined documents (e.g. contracts, studies, internal reports).

More AI stacks

Matching stacks for other roles — each with a stack table, workflow and common mistakes.

We build and operate the stack

We build you a research workflow with a citation requirement.

Iniciar análisis de potencial

Si quiere priorizar un proceso real, unas pocas entradas claras bastan para una primera evaluación sólida.

WhatsApp con Kai