01 — Collaboration matrix
Each cycle makes the next one faster.
Not a linear pipeline, but a living loop: evidence sharpens reasoning, decisions meet reality, and outcomes inform what comes next.
FinTec AI Ecosystem
Illustrative collaboration map
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FinTec AI Ecosystem
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FinTec AI Ecosystem
Illustrative collaboration map
External world · Input
The financial world in motion
↓
Perception Data Horizon
Cognition FinBayes
Decision & execution AI Trading Matrix
Signals become reasoning, reasoning meets reality, and outcomes begin the next cycle.
Capability foundation · Staged RLE · FEFM
Explore the connections
→
Perception
Data Horizon
Takes in multi-source, multilingual financial information, around the clock
Building
Cognition
FinBayes
Turns a financial question into a sourced, time-stamped reference that states its risks
Preview
Decision & execution
AI Trading Matrix
Carries tested judgment into governed trading workflows
Building
Live & historical data
Data Horizon → FinBayes
Live and historical context, with its sources intact.
Market data & signal candidates
Data Horizon → AI Trading Matrix
Events become evidence; evidence becomes signal candidates.
User-mediated decision reference
FinBayes → User → AI Trading Matrix
The user weighs it, then decides whether to pass it to a trading workflow.
Attributed feedback
Every outcome becomes a new signal
Outcomes return to Data Horizon and FinBayes with their context, as evidence for the next cycle rather than a bare right-or-wrong verdict.
External world · Input
The financial world in motion
Many markets, many languages, public and licensed sources, around the clock.
Ecosystem product · Building
FinPrisMe
FinPrisMe: a global financial news platform built on Data Horizon, adding localisation, editing and interpretation.
Outside the ecosystem · Use
A reference where the question is asked
During research and analysis: sourced, time-stamped references you can keep questioning. Whether to use them is your call.
Outside the ecosystem · Use
From individual conviction to institutional discipline
Exploring verifiable strategies and virtual traders for individuals, and governed system partnerships with institutions.
Capability foundation · Staged
Where outcomes flow back to
Learning is designed into the loop.
RLE
Turns feedback from sensing, reasoning, and trading into material for the next iteration.
Staged
FEFM
Collects qualified financial corpora, tasks and feedback as candidate assets for future training and evaluation.
Staged
RLE and FEFM are shaped by real tasks, outcomes, and evaluation—not ambition in the abstract.
Selected pathways and intended use cases. Not every connection shown is live. Dashed lines mark staged capabilities that are not yet connected.