AI that connects current Brazilian market news to lessons from financial history
Investors and the public repeatedly misunderstand the necessary role of speculation in markets, leading to fear-driven reactions and regulations after crashes like 1929 and 2008
SpecLens uses AI to scan current financial news and regulatory proposals, then overlays relevant lessons from past speculation-driven events like 1929, 2008, and Brazilian crises. It helps users understand if current fears are justified or if they're repeating historical misunderstandings about speculation's role.
Brazilian investors, financial professionals, and policymakers following economic history
Hyper-localized to Brazilian economic history and current regulatory environment with accurate Portuguese language explanations using a curated vector database of local economic texts.
professional and reassuring
Pulls latest news from Brazilian and global financial sources focused on speculation topics
Uses RAG to link news to relevant historical events and lessons
Visual timeline showing current event vs historical parallels
Users can save, tag and organize important historical insights
Notifies when new bills or CVM proposals target speculation
Custom feed based on user role (investor vs policymaker)
Generate PDF with annotations for sharing with teams
Share annotated articles with team members
Searchable database of 100+ historical Brazilian economic documents
Forecast potential outcomes based on history
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| role | text | No |
| preferences | text | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| title | text | No |
| url | text | No |
| content | text | No |
| embedding | text | Yes |
| published_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| title | text | No |
| content | text | No |
| embedding | text | Yes |
| event_date | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| article_id | uuid | Yes |
| historical_event_id | uuid | Yes |
| note | text | Yes |
| created_at | timestamp | No |
Relationships:
/api/feedGet personalized news feed with annotations
/api/analyzeSubmit article for AI historical annotation
/api/insightsRetrieve user's saved insights
/api/alertsGet regulatory alerts for speculation topics
/api/webhook/newsIngest new articles from RSS feeds
3 annotations/month
None
Custom volume
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 220 | 7% | $340 | $4,080 |
| Month 6 | 1,650 | 13% | $4,720 | $56,640 |
AI instantly shows you the historical parallels behind today's Brazilian market headlines and regulatory proposals.
Offer free Pro access to 30 popular Brazilian investment newsletter writers in exchange for a review and mention. Present the tool at virtual FGV/IBRE economic seminars. Use Twitter advanced search for people discussing 'especulação' or 'bolha' in financial context and send personalized demos of the tool analyzing recent articles they shared.
Large community of financial analysis
No automated historical parallel mapping or regulatory focus
Instant AI-driven historical context specifically for Brazil
Strong general research capabilities
Lacks curated Brazilian economic history dataset and regulatory tracking
Specialized vector database of Brazilian financial crises
Curated Brazilian economic history vector database that improves with user feedback on annotation accuracy, creating a data moat competitors cannot easily replicate.
LLM and RAG technology is now capable of accurately handling complex economic texts, while Brazil faces renewed debates on regulating crypto speculation and stock market leverage.
AI hallucinating historical facts
Heavy use of RAG with human-curated base documents plus user feedback mechanism to downrank bad annotations
News sources blocking scraping
Use official RSS feeds and partner APIs where possible
Copyright concerns with news content
Only show snippets and link back to originals, clear fair-use educational disclaimers
Success: 150 signups and 12 interviews completed
Success: 65% report they would pay $22/mo
Success: 400 total users and $800 MRR in first 45 days
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