What happened before?
Charts and forecasts rarely show the historical context behind the signal.
Equity research, connected
One stock.
Three intelligence engines.
Far less noise.
AIforMarkets connects 12 million historical patterns, a proprietary Equity Rating built from company news and fundamentals, and an AI Assistant grounded in proprietary analysis and connected market data.
Spend less time collecting information. Spend more time judging what matters.
Pattern Recognition
Compare similar historical market setups.
Equity Rating
Assess company evidence across factors.
AI Assistant
Ask questions across connected research.
The investor problem
The market leaves patterns.
Companies leave evidence.
You are left to connect it.
What is changing now?
Financials, guidance, product news, and industry conditions live across scattered sources.
What deserves attention?
Thousands of rows make comparison possible—but finding the important ones still takes work.
AIforMarkets brings all three questions into one research workflow.
Three proprietary engines
Not another signal.
A connected point of view.
Each pillar answers a different part of the investment question. Together, they make uncertainty more visible and research more efficient.
01
Market memory
Pattern Recognition
See how similar historical setups unfolded—and the three paths the evidence supports today.
Explore market context → 02
Company potential
Equity Rating
Turn fragmented company news and fundamental evidence into one consistent, explainable rating.
Understand the company → 03
Research partner
AI Assistant
Question, compare, rank, and explain the entire research universe—not just one preselected stock.
Investigate the universe →01
Pattern Recognition
See what the market remembers
What can 12 million historical patterns reveal about today?
AIforMarkets compares current market behavior with more than 12 million historical precedents, then organizes the closest outcomes into three evidence-based paths.
Instead of betting everything on a single forecast, investors see a range of plausible outcomes, the relative weight behind each path, and where uncertainty remains.
- 01Replace anecdotes with precedent
Search a broad historical memory, not a handful of hand-picked examples.
- 02See three outcomes—not one promise
Explore distinct possibilities instead of a false sense of certainty.
- 03Make uncertainty visible
Compare paths, probability, expected return, and risk in context.
Historical pattern library12,054,049precedents searched
Pattern 1Pattern 2Pattern 3
03
AI Assistant
Intelligence grounded in proprietary analysis
Ask across your research universe.
Not a generic chatbot.
The AI Assistant works from the platform’s proprietary analysis across the complete research universe. It understands pattern scenarios, Equity Ratings, company context, sectors, markets, and the relationships between them.
Use natural language to compare companies, rank opportunities, group sectors, find outliers, or surface where Pattern Recognition and company evidence disagree.
Compare stocksRank the universeGroup sectorsExplain results
AI
Market AssistantProprietary analysis context
Grounded researchEXAMPLE QUESTION
Where do strong Equity Ratings and weaker pattern evidence disagree?
01
Interpret the question
02
Filter every eligible row
03
Compare both evidence layers
04
Return inspectable results
What the Assistant can returnStructured analysis
Ranked companiesSide-by-side comparisonsGrouped insightsUnderlying result rows
Questions worth asking
How does NVDA compare with semiconductor peers?Which sectors have the strongest combined evidence?Where do patterns and fundamentals disagree?Which companies deserve a closer look?
The team behind AIforMarkets
Two disciplines.
One research platform.
Quantitative finance, hands-on engineering, and technical guidance come together to make complex market research more usable, transparent, and efficient.
Chief Executive Officer
Mihai-Andrei Mitrache
Quantitative developer with more than 10 years in financial services and a Ph.D. in Economics from the School of Advanced Studies of the Romanian Academy, focused on pattern recognition and financial-bubble detection.
Quantitative financePattern recognition
LinkedIn
Head of Engineering
Catalin Sava
Specializes in React and Node.js to build responsive web applications. A graduate of Politehnica University of Bucharest in automation and computer science, he also works across C, C++, Python, databases, and object-oriented programming. Driven by curiosity, he enjoys turning technical challenges into practical digital experiences.
React & Node.jsFull-stack engineering
Tech Advisor
Adrian Pirvu
Software engineer with extensive experience in IT systems design and architecture across financial markets, banking, and business intelligence. He contributed to Romania’s National Artificial Intelligence Strategy and works on AI algorithms for market trends informed by global and local events.
Systems architectureApplied AI
Built for how investors grow
Clear enough to begin.
Powerful enough to investigate.
Understand before you decide.
Plain-language context, visible uncertainty, and concise company briefings make complex research easier to navigate.
- Guided scenario interpretation
- Company evidence in one place
- Natural-language exploration
Go deeper without slowing down.
Compare evidence layers, screen a global universe, rank opportunities, and inspect the records behind every result.
- Three-path scenario distributions
- Cross-company and sector analysis
- Inspectable AI result logic
Research with less friction
Understand the pattern.
Understand the company.
Ask the next question.
Discover a more connected way to research global equities.
Open the app