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1. Perplexity + Gemini gather (with citations)
Current sources and long documents are captured.
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.
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.
The research stack
The stack for sourced reports with a citation requirement. Prices as a ballpark, as of July 2026, vendor page authoritative.
| Task | Tool (recommended) | Why | Price |
|---|---|---|---|
| Live web research | Perplexity | Current answers with live source citations | € |
| Synthesis / report | Claude | Long context, synthesis of many sources | €€ |
| Source grounding | NotebookLM | Notes strictly on uploaded sources | Free/€ |
| Multimodal / long docs | Gemini | Very long context, multimodal | € |
| Data analysis | Claude / code interpreter | Computes and checks data instead of guessing | €€ |
How it works together
From gathering with citations to the final, sourced report.
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2. NotebookLM grounds on the uploaded sources
Answers stay strictly tied to the source material.
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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.
SME 'adopt AI'
The mid-market starts GDPR-aware with Claude/Copilot (everyday), Notion (knowledge/processes), n8n self-hosted (automation on your own server) and Mistral as an EU-native LLM for sensitive data — AI in the company without handing customer data to unclear third countries.
Marketing agency
The strongest AI marketing stack in 2026 combines Claude (strategy, copy, orchestration), Higgsfield/Seedance (cinematic video), HeyGen + fal OmniHuman (talking avatars), ElevenLabs (voiceover) + Suno (music), Canva (design) and n8n (automation + publishing) — full campaigns with no shoot, studio or agency overhead.
All AI stacks at a glance
Back to the hub with the stack overview and all 7 role-based stacks.
We build and operate the stack
We build you a research workflow with a citation requirement.