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Document Analysis Checklist
Before adding another tool to your stack, check whether your General AI Assistant already handles document analysis well enough. Every new tool is another license, another security review, and another training session. Reuse when you can.
Evaluation Checklist
Start from the assumption that you don't need a separate document analysis tool. These questions will tell you if that assumption is wrong.
1. What Your General AI Assistant Already Does
Test your existing tool first.
Upload 5 representative documents that your team processes regularly and ask your General AI Assistant to summarize, extract key points, compare, or answer questions about them. Score each test:
Sufficient -- Does what you need without issues
Partial -- Gets most of it right but misses details or formatting
Insufficient -- Can't handle the document type, length, or task
If most results are "Sufficient": You don't need a dedicated tool. Stop here.
If you're seeing "Partial" or "Insufficient": Continue through this checklist to identify what's falling short.
2. Document Types & Complexity
What types of documents does your team analyze?
Text-heavy documents (reports, contracts, policies, emails): Any General AI Assistant handles these well. Claude Team is particularly strong with long documents due to its 200K context window. No dedicated tool needed.
Structured data (spreadsheets, CSVs, databases): If the data is already in spreadsheet format, M365 Copilot (Excel) or Gemini (Sheets) may handle it better than a standalone tool. For complex analysis, consider whether your team actually needs a BI tool rather than an AI document tool.
Scanned documents / PDFs with images: OCR quality varies between tools. Claude and ChatGPT both handle PDF analysis well, including mixed text-and-image documents. If your PDFs are mostly scanned images with poor OCR, test carefully -- you may need a dedicated document processing platform like Adobe Acrobat AI or a specialized OCR pipeline.
Technical drawings, blueprints, diagrams: This is where general tools often fall short. If visual document interpretation is a primary need, you likely need a specialist tool. Test your specific document types before deciding.
How long are the documents you're analyzing?
Under 50 pages: All current General AI Assistants handle this comfortably.
50-200 pages: Claude Team (200K tokens) handles this in a single conversation. Other tools may require chunking or multi-turn approaches. Test with your actual documents.
200+ pages or multiple documents at once: You may benefit from a tool with document indexing -- NotebookLM (Google), or retrieval-augmented generation (RAG) pipelines. Perplexity Pro is also strong for research across multiple sources.
3. Compliance & Data Sensitivity
Do the documents contain sensitive data?
Refer to the Data Classification Policy from the governance toolkit.
Green data only: Any approved tool works. No additional concerns.
Yellow data: Use your enterprise-grade General AI Assistant on a business plan. No separate tool needed as long as it's properly licensed.
Red data (PII, financials, legal): Evaluate carefully. Your General AI Assistant's enterprise plan likely covers this, but verify the data processing agreement. For highly regulated document workflows (legal discovery, medical records), a specialized tool with industry-specific compliance certifications may be required.
Do you need audit trails for document processing?
Yes, for compliance: Most General AI Assistants on enterprise plans provide usage logs. If you need detailed audit trails showing exactly which documents were processed and what outputs were generated, verify this capability with your chosen tool. Specialized document platforms in regulated industries (legal tech, healthcare) typically have more robust audit features.
No: Not a differentiator. Move on.
4. Volume & Workflow Integration
How many documents does your team process per week?
Under 50 documents/week: Manual upload to your General AI Assistant is fine. No dedicated pipeline needed.
50-500 documents/week: You might benefit from batch processing capability. Check if your General AI Assistant's API supports batch uploads. If not, a lightweight automation (Zapier, Make, or a custom script) connecting your document source to the API may be enough.
500+ documents/week: At this scale, a dedicated document processing pipeline is likely warranted. Consider tools like Unstructured, LlamaIndex, or cloud provider solutions (AWS Textract, Google Document AI, Azure AI Document Intelligence). These integrate with your AI assistant rather than replacing it.
Do you need source citations in the output?
Yes, critical for trust/compliance: Perplexity Pro is strongest here -- it provides inline citations linking back to source material. NotebookLM (Google) also shows source grounding. Standard General AI Assistants can cite sources when asked but it's not their default behavior and accuracy varies.
Nice to have: Prompt your General AI Assistant to include page numbers and section references. It won't be as reliable as a citation-first tool, but may be sufficient.
Not needed: Not a factor. Your General AI Assistant is fine.
Decision Flow
Reuse your General AI Assistant if:
- - Documents are primarily text-based (reports, contracts, policies)
- - Volume is under 50 documents per week
- - Source citations are not a hard requirement
- - Your 5-document test scored mostly "Sufficient"
Add Perplexity Pro if:
- - Source citations are important for trust or compliance
- - Your team does heavy research across multiple sources
- - You want a lightweight addition without a major new tool rollout
Invest in a dedicated solution if:
- - Volume exceeds 500 documents per week
- - Documents include scanned images, blueprints, or technical drawings
- - Industry-specific compliance requires dedicated audit trails
- - You need automated document processing pipelines (not manual uploads)
The 80% rule applies here.
Most organizations can handle 80% of their document analysis needs with their General AI Assistant. The remaining 20% is often better served by targeted automation (scripts, Zapier) connecting documents to the same AI rather than adding an entirely new tool with its own learning curve and license cost.