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Komo’s Data Room transforms how professionals work with documents. Upload contracts, reports, transcripts, or any business documents, then ask questions in natural language. Unlike traditional document chatbots, Komo shows you detailed quotes and exact page references for every claim—ensuring you can verify and trust every answer.

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What is Data Room & Document Research?

Data Room is your AI-powered document assistant that reads, analyzes, and answers questions about your uploaded files. Whether you’re reviewing a single contract or analyzing hundreds of documents, Data Room provides accurate answers with complete transparency into the source material. Core Capabilities:
  • Large Document Support - Handle documents of any size, from single pages to 1000+ page reports
  • Natural Language Q&A - Ask complex questions in plain English
  • Detailed Quote Citations - See exact text excerpts that support each answer
  • Page-Level References - Jump directly to relevant sections in source documents
  • Multi-Document Analysis - Ask questions across multiple documents simultaneously
  • Context-Aware Responses - AI understands document structure and relationships

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The Komo Difference: Verifiable Document Citations

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The Document Chatbot Problem

Most AI document tools (including ChatGPT with document upload) provide answers but make verification difficult: Limitations:
  • ❌ Generic citations like “according to the document”
  • ❌ No easy way to see exact quotes
  • ❌ Difficult to verify AI didn’t hallucinate
  • ❌ Can’t quickly jump to source location
  • ❌ Answers feel like black boxes

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The Data Room Solution

Komo’s unique split-screen interface provides complete transparency: Left Side: AI-Generated Answer
  • Comprehensive response to your question
  • Inline citation markers [1], [2], [3]
  • Structured formatting (bullet points, summaries)
  • Natural language explanations
Right Side: Document Citation Panel
  • Full document viewer with highlighting
  • Exact quotes extracted from source
  • Page numbers for each reference
  • Relevance scoring for each section
  • Jump directly to highlighted text in PDF

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How Document Citations Work

Example Query: “What is the doc about?” AI Answer (Left Side): 
The document is a transcript of an interview [1] conducted on June 23, 2025,
with a former Director of Global Clients & Partnerships at Meta Platforms Inc.

The main topic is the digital advertising industry, focusing on Alphabet Inc.
(Google) [1], its evolution, market competition, and future risks.

Key Content and Discussion Points:

- Expert Background: The expert has over 19 years of experience in digital
  advertising, primarily spending 15 years at Google... [1]

- Google's Evolution and Challenges: The discussion covers Google's shift
  from a search-only company to a diverse platform including YouTube... [1]
  A major challenge discussed is the ultimate risk to search from Google's
  own LLMs and GPTs (zero-click searches) [1].

- Competition: The expert highlights Amazon as a strong competitor, calling
  it a "necessary evil" for advertisers... [1]
Document Citation Panel (Right Side): 
┌─────────────────────────────────────────────────────────┐
│ alphabet_inc_139251_Former_Director_Global_Clients...  │
│                                                         │
│ [PDF Viewer with Highlighting]                         │
│                                                         │
│ Page 1 of 12                            Relevance: 33% │
│                                                         │
│ ───────────────────────────────────────────────────    │
│                                                         │
│ Highlighted Section:                                   │
│                                                         │
│ "Interview conducted on June 23, 2025"                 │
│                                                         │
│ "Topics: Digital Advertising, AI Integration,          │
│  Market Competition, Privacy Regulations..."           │
│                                                         │
│ "The Tegus Client engaged in a discussion with an      │
│  expert who highlighted Google's evolution in the      │
│  advertising space, from a search-only company to      │
│  a diverse platform including YouTube and Google       │
│  Shopping..."                                          │
│                                                         │
│ [Click to navigate to page with full context]         │
└─────────────────────────────────────────────────────────┘
Why This Matters:
  • ✅ Instant Verification - Click citation [1] to see exact source text
  • ✅ No Hallucinations - Every claim backed by document excerpt
  • ✅ Legal Defensibility - Page-level references for audits and compliance
  • ✅ Build Trust - See AI’s reasoning and evidence simultaneously
  • ✅ Professional Work - Suitable for contracts, due diligence, legal review

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How It Works

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1. Upload Documents

Navigate to komo.ai/data-room → Upload Documents Supported Formats:
  • PDF (including scanned/image PDFs with OCR)
  • Microsoft Word (.docx, .doc)
  • PowerPoint (.pptx, .ppt)
  • Excel (.xlsx, .xls)
  • Plain text (.txt)
  • Images (JPG, PNG - with OCR)
  • Rich text formats
Document Size:
  • Single documents: Up to 10,000 pages
  • Total storage: Based on plan (Standard: 10GB, Enterprise: unlimited)
Organization:
  • Create folders for different projects
  • Tag documents by type (contract, report, transcript)
  • Search uploaded documents
  • Share document sets with team

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2. Select Documents for Chat

Single Document Analysis:
  • Click document in Data Room
  • Click “Chat with Document”
  • Start asking questions
Multi-Document Analysis:
  • Select multiple documents (checkbox)
  • Click “Chat with Selected” (e.g., “Chat with 12 documents”)
  • Ask questions that span all selected files
Example Multi-Document Query: 
[Selected: 5 customer contracts]
Query: "Which contracts have termination clauses with less than
       90 days notice? List contract names and specific terms."

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3. Ask Questions

Types of Questions: Extraction Questions:
  • “What is the contract termination clause?”
  • “What are the key risks mentioned in this report?”
  • “Who are the decision makers identified in this document?”
Summary Questions:
  • “Summarize the main findings of this research report”
  • “What are the 5 key takeaways from this transcript?”
  • “Give me an executive summary of this proposal”
Analysis Questions:
  • “What are the differences between Contract A and Contract B?”
  • “Are there any red flags in these agreements?”
  • “How does this company’s financial performance compare to last year?”
Comparison Questions (Multi-Document):
  • “Which vendor offers the best pricing?”
  • “Compare the liability caps across all 10 contracts”
  • “What common themes appear across all interview transcripts?“

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4. Review Answers with Citations

Every answer includes:
  • Inline Citations: Click [1], [2], [3] to jump to source
  • Citation Panel: See exact quotes and page numbers
  • Document Viewer: Full PDF with highlighted relevant sections
  • Relevance Score: AI confidence in each cited section
Verification Workflow:
  1. Read AI’s answer in left panel
  2. Check citations in right panel
  3. Click citation number to see document highlight
  4. Verify quote matches AI’s interpretation
  5. Read surrounding context if needed

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5. Continue Conversation

Follow-Up Questions:
  • “Tell me more about the liability section”
  • “What page discusses termination fees?”
  • “Are there any exceptions to this clause?”
Refinement:
  • “Focus only on financial terms”
  • “Ignore the appendices”
  • “Just look at sections 5-8”
Multi-Step Analysis:
  • Start broad: “What is this document about?”
  • Drill down: “Tell me more about the competitive analysis section”
  • Extract specific: “What exact market share numbers are mentioned?”

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Real-World Use Cases

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Use Case 1: Contract Review & Red Flag Identification

Scenario: Legal team reviewing 20 vendor contracts for non-standard terms Workflow:
  1. Upload 20 vendor contracts to Data Room
  2. Select all 20 contracts
  3. Ask: “What are the key terms in these contracts? Flag any clauses that are non-standard or risky.”
AI Response: 
Analysis of 20 vendor contracts:

Standard Terms Found Across Most Contracts:
- Payment terms: Net 30 [Contracts 1-15, 17-20]
- Liability caps: 12 months of fees [Contracts 1, 3-7, 9-14, 16-20]
- Termination: 90 days notice [Contracts 2-8, 10-20]

⚠️ Non-Standard/Risky Terms Flagged:

1. VendorTech_Agreement.pdf [2]:
   - Liability capped at $50K (well below 12-month fees)
   - Auto-renewal with 180-day cancellation notice
   - IP assignment clause (we assign all IP to vendor)
   Page 7, Section 8.1-8.3

2. DataCorp_MSA.pdf [3]:
   - Unlimited liability for data breaches
   - No termination for convenience clause
   - Vendor can change pricing with 30-day notice
   Page 12, Section 11.2

3. CloudServices_Contract.pdf [5]:
   - Exclusive vendor lock-in for 5 years
   - Automatic price increases tied to CPI +5%
   Page 4, Section 3.4
Citation Panel shows:
  • Exact text: “Vendor’s total liability shall not exceed $50,000…”
  • Page reference: Page 7, Section 8.1
  • Full context available in highlighted PDF
Result:
  • 20 contracts reviewed in 10 minutes (vs. 20+ hours manual)
  • 3 high-risk contracts flagged for negotiation
  • Every finding verifiable with page numbers
  • Legal team focuses on exceptions, not reading every page

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Use Case 2: Due Diligence Document Analysis

Scenario: M&A team conducting due diligence on acquisition target Documents Uploaded (45 files):
  • Financial statements (last 3 years)
  • Customer contracts (top 10 customers)
  • Employee agreements
  • Board minutes
  • IP/patent documents
  • Regulatory filings
Workflow: Query 1: “What are the company’s main revenue sources based on these financial statements and customer contracts?” Query 2: “Are there any material risks disclosed in board minutes or regulatory filings?” Query 3: “Do any customer contracts have termination clauses exercisable in the next 12 months?” Query 4: “What IP does the company own vs. license from third parties?” Sample Response (Query 2): 
Material Risks Identified:

1. Revenue Concentration Risk [BoardMinutes_Q3_2024.pdf, Page 4]:
   "Top 3 customers represent 68% of total revenue. Loss of any
    single customer would materially impact financial performance."

2. Key Person Dependency [BoardMinutes_Q2_2024.pdf, Page 7]:
   "CTO departure noted as critical risk. No succession plan
    documented. CTO personally responsible for core IP development."

3. Regulatory Compliance [10K_Filing_2024.pdf, Page 23]:
   "Company received inquiry from FTC regarding data privacy
    practices. Potential fine range: $500K - $5M."

4. Customer Churn [BoardMinutes_Q4_2024.pdf, Page 5]:
   "Churn rate increased from 12% to 19% annually. Management
    attributes to competitive pressure and pricing concerns."
Each risk shows:
  • Exact quote from source document
  • Document name and page number
  • Click to view full context in PDF
  • Relevance scoring
Result:
  • 45 documents analyzed systematically
  • Material risks surfaced automatically
  • Every claim verifiable with source
  • Due diligence time reduced from weeks to days
  • Investment committee has complete documentation trail

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Use Case 3: Expert Interview Analysis

Scenario: Consulting team analyzing 15 expert call transcripts Query: 
"What are the common themes across these expert interviews about
 digital advertising? What do experts agree on? Where do they disagree?"
AI Response: 
Analysis of 15 Expert Interviews:

STRONG CONSENSUS (14-15 experts agree):

1. AI Will Disrupt Search Advertising [1, 3, 4, 5, 7-15]
   - Quote from Interview #7: "Zero-click searches via LLMs are
     the ultimate risk to Google's search business"
   - 14/15 experts see AI-generated answers reducing ad impressions

2. Amazon Is Major Threat to Google [2, 3, 5-15]
   - Quote from Interview #3: "Amazon is a necessary evil for
     advertisers—ROI is too good to ignore"
   - 13/15 experts cite Amazon's retail media growth

SIGNIFICANT DISAGREEMENT:

1. Timeline for AI Impact on Search Revenue
   - Pessimistic (5 experts): 2-3 years [1, 4, 7, 11, 13]
   - Moderate (7 experts): 5-7 years [2, 3, 5, 6, 8, 9, 12]
   - Optimistic (3 experts): 10+ years [10, 14, 15]

2. Google's AI Moat
   - Strong moat (6 experts): Google's infrastructure advantage [2, 6, 9, 11, 14, 15]
   - Weak moat (9 experts): Commoditization of AI models [1, 3-5, 7, 8, 10, 12, 13]
Citation Panel:
  • Click [7] to see full context in Interview #7 transcript
  • Highlighted quote on page with surrounding conversation
  • Expert background and credentials visible
Result:
  • 15 transcripts synthesized into actionable insights
  • Consensus vs. debate areas clearly identified
  • Client-ready analysis with verifiable sources
  • Research time: 30 minutes vs. 2 days manual analysis

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Use Case 4: Regulatory Compliance Audit

Scenario: Compliance team auditing 200 customer contracts for GDPR compliance Workflow:
  1. Upload 200 customer contracts
  2. Select all contracts
  3. Ask: “Do these contracts comply with GDPR requirements? Specifically check for:
    • Data processing terms
    • Data retention periods
    • Right to deletion clauses
    • International data transfer provisions
    • Sub-processor consent”
AI Response (Sample): 
GDPR Compliance Analysis - 200 Contracts:

✅ COMPLIANT: 183 contracts (91.5%)

⚠️ NEEDS REVIEW: 12 contracts (6%)
- Missing explicit data retention periods
- Generic data processing language
- Recommend adding GDPR-specific addendums

❌ NON-COMPLIANT: 5 contracts (2.5%)

1. MegaCorp_Agreement_2019.pdf [Contract #47]:
   - No data processing agreement
   - No mention of GDPR or data subject rights
   - International transfer clause non-compliant
   Page 3-4, Data Protection section

2. TechVendor_MSA_2020.pdf [Contract #89]:
   - Data retention period exceeds GDPR limits (10 years)
   - No right to deletion clause
   - Sub-processor list not disclosed
   Page 7, Section 6.2

[Full analysis continues...]
For each non-compliant contract:
  • Exact problematic clauses quoted
  • Page numbers for legal team review
  • Specific GDPR articles violated
  • Recommended remediation
Result:
  • 200 contracts audited in 2 hours (vs. weeks)
  • 17 contracts requiring action identified
  • Every finding documented with evidence
  • Proactive compliance vs. reactive penalties

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Key Features & Capabilities

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1. Large Document Support

  • Komo Data Room: Documents of any size (tested up to 10,000 pages)
  • ChatGPT: ~300 pages practical limit, quality degrades
  • Google AI Studio: Context window limitations

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2. Detailed Quote Citations

  • Komo: Click any citation → see exact quote + page number + highlighted PDF
  • Competitors: Generic “according to document” or response-level citations
  • Traditional Tools: Manual search through documents

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3. Multi-Document Analysis

  • Komo: Ask questions across unlimited documents simultaneously
  • ChatGPT: Upload multiple files but challenging to compare systematically
  • Manual: Open dozens of PDFs, use Ctrl+F, compile manually

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4. Professional Document Types

Contracts & Legal:
  • NDAs, MSAs, vendor agreements
  • Employment contracts
  • License agreements
  • Terms of service
Financial:
  • 10-Ks, 10-Qs, 8-Ks
  • Earnings transcripts
  • Investor presentations
  • Audit reports
Business:
  • RFP responses
  • Market research reports
  • Board meeting minutes
  • Strategic plans
Research:
  • Academic papers
  • Expert interview transcripts
  • Analyst reports
  • Whitepapers

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5. Context-Aware Understanding

Document Structure Recognition:
  • Understands sections, headers, footnotes
  • Tracks table data and extracts systematically
  • Recognizes legal clause numbering
  • Interprets financial tables
Relationship Awareness:
  • Links related sections across document
  • Understands cross-references
  • Tracks definitions and uses throughout

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Best Practices

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Uploading Documents

Organize by Project:
  • Create folders: “Q4 Contracts”, “Due Diligence - CompanyX”, “Expert Calls”
  • Tag consistently: “Contract - Vendor”, “Financial - 10K”, “Research - Industry”
OCR Quality:
  • For scanned PDFs, ensure high resolution (300+ DPI)
  • Clean scans produce better OCR results
  • Test with sample question after upload
File Naming:
  • Use descriptive names: “Vendor_Acme_MSA_2024.pdf”
  • Include dates: “BoardMinutes_Q3_2024.pdf”
  • Avoid generic: “contract1.pdf”, “doc.pdf”

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Asking Questions

Start Broad, Then Narrow:
  1. “What is this document about?” (overview)
  2. “Tell me more about the liability section” (drill down)
  3. “What is the exact liability cap in dollars?” (specific extraction)
Be Specific with Multi-Document Queries:
  • ✅ “Which of these 10 contracts have auto-renewal clauses?”
  • ✅ “Compare pricing across all vendor proposals”
  • ❌ “Tell me about the contracts” (too vague)
Request Structured Outputs:
  • “Create a table comparing key terms across all contracts”
  • “List all red flags in bullet points with document names”
  • “Summarize each interview in 3 sentences”

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Verifying Answers

Always Check Citations for:
  • Legal decisions and contract reviews
  • Financial data and metrics
  • Compliance and regulatory matters
  • Any high-stakes business decisions
Quick Verification:
  • Scan AI answer for key claims
  • Click citation numbers
  • Verify quote matches claim
  • Check surrounding context in PDF
When to Manual Review:
  • First time using AI on document type
  • Mission-critical decisions
  • Contradictory information appears
  • Low confidence/ambiguous answers

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Common Questions

Q: What’s the maximum document size? A: Up to 10,000 pages per document. Most users work with 10-500 page documents comfortably. Q: Can it read scanned PDFs? A: Yes. Komo uses OCR (Optical Character Recognition) to extract text from scanned documents and images. Q: How many documents can I chat with at once? A: No hard limit. Users regularly chat with 50-100 documents simultaneously. Performance depends on total pages. Q: What if the AI misunderstands my question? A: Rephrase your question. Try being more specific. Example: Instead of “What’s the price?”, ask “What is the total contract value in USD?” Q: Can I share Data Room access with my team? A: Yes. Share specific folders or entire Data Room. Set permissions: view-only or edit access. Q: Does this work with non-English documents? A: Yes. Supports major languages including Spanish, French, German, Chinese, Japanese, and more. Q: How is this different from ChatGPT with documents? A: Komo provides detailed quote citations with page numbers in a split-screen interface. Every claim is instantly verifiable with exact source text highlighted in PDF. Q: Can I export chat conversations? A: Yes. Export to PDF or markdown with all citations and sources included. Q: What happens to my uploaded documents? A: Documents are encrypted and stored securely. Enterprise: SOC 2 Type II certified, GDPR compliant. Delete anytime. Q: Can I use this for privileged legal documents? A: Yes. Enterprise plans include attorney-client privilege protection and air-gapped deployment options. Contact [email protected]. Q: How accurate is the OCR? A: 95%+ accuracy for good quality scans. Lower quality scans may have errors—always verify critical information with citation panel. Q: Can I search within my Data Room? A: Yes. Search all uploaded documents by filename, content, or tags. Find specific documents quickly.
Data Room & Document Research transforms how professionals work with documents—enabling instant answers to complex questions with complete transparency and verifiability, turning hours of manual document review into minutes of AI-assisted analysis you can trust.