AI that reads every document so you don't miss critical terms
Private market firms waste significant time and lose visibility managing deals through fragmented spreadsheets, disconnected CRMs, standalone VDRs, and scattered email threads instead of a unified system.
SageDeal ingests all your deal documents, emails, and data room files using specialized RAG trained on private market language. It extracts key terms, flags risks, compares against your previous deals, and surfaces insights automatically. Replaces hours of manual spreadsheet population and review.
Private equity funds, family offices, fund managers, accelerators, and institutional investors in the GCC, Singapore, and Europe handling high-volume deal flow
Domain-specific vector database of GCC, Singapore, and European private market precedent (term sheets, LPAs, NDAs) that improves with every deal uploaded (with permission).
supportive
Drag-and-drop or email-forward any PDF/Word document for instant parsing
Automatically identifies and structures key commercial terms into a deal card
Flags unusual terms, missing clauses, or deviations from market standards
Compares current deal against your firm's historical closed deals
Single view combining extracted data, documents, and AI insights
Ask questions about any deal in plain English ('What is the liquidation preference?')
Auto-generates customized diligence request lists
Email summary of all portfolio and pipeline activity with insights
Structured data export for portfolio reporting systems
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| org_id | uuid | No |
| title | text | No |
| stage | text | No |
| vector_id | text | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| deal_id | uuid | No |
| content | text | No |
| embedding | vector | No |
| metadata | text | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| deal_id | uuid | No |
| type | text | No |
| content | text | No |
| confidence | int | No |
Relationships:
/api/ingestUpload document, chunk it, generate embeddings and extract terms
/api/queryRAG query over deal documents and precedents
/api/deals/:id/insightsGet all AI-generated insights for a deal
Single user
Up to 8 users
Unlimited
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 65 | 18% | $292 | $3,504 |
| Month 6 | 480 | 26% | $3,120 | $37,440 |
AI trained on private equity documents that reads everything and tells you what matters.
Offer free AI diligence audits to 10 mid-sized European and GCC funds via LinkedIn outreach, using their public term sheets as demonstration. Partner with two law firms in Dubai and Singapore who can refer deals for AI review during early diligence. Create viral 'AI vs Associate' comparison content showing time saved.
Strong company database
Not focused on document understanding within your own deals
Deep understanding of your own historical documents and proprietary terms
Broad legal capabilities
Not specialized for private markets or investor side workflows
Built by and for PE/VC investors with relevant training data
Flywheel of proprietary deal data — the more documents funds upload, the smarter the system becomes at identifying market-standard terms in those specific jurisdictions
GPT-4 class models combined with pgvector finally make accurate private-market RAG economically viable for a solo developer to build and maintain.
Hallucinations in high-stakes legal/financial analysis
Always cite source chunks, implement human-in-the-loop approval before insights are marked final, conservative prompting
Liability if AI misses a critical term
Clear disclaimers, insurance, position product as 'augmentation' not replacement
Success: Minimum 88% accuracy on key term extraction
Success: At least 9 funds report finding material issues the AI caught
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