Exnet.ai
Exnet.ai provides a decision infrastructure platform that converts continuous financial data into execution-ready actions for wealth managers and advisory firms. The system uses quant-driven agents and machine learning to detect signals, score risks, and route actions like rebalancing or transfers directly into existing workflows. It can be embedded via API into current systems or deployed as a white-labeled platform.
- Financial Technology
- Artificial Intelligence
- AI Agents
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Most financial platforms stop at signals—they display data, generate alerts, and produce reports, but execution remains external and manual. Financial signals are continuous while decisions are periodic, creating a latency gap where risk accumulates, and this systematic decision infrastructure has traditionally been available only to large portfolios with quant teams.
Solution
Exnet.ai provides a decision infrastructure layer that converts multi-domain financial data into structured, execution-ready decisions in real time. The platform ingests market, portfolio, client, macro, ESG, and news data continuously, then applies quantitative models and machine learning to score signals by probability, urgency, and confidence. A decision engine constructs optimal actions through optimization—not selection—combining multiple inputs into a single recommended action. These decisions are routed to advisor queues, automated transfers, or compliance holds, and a feedback loop captures execution outcomes to recalibrate models and improve future decision quality. Exnet can be embedded via API into existing advisor systems or deployed as a fully white-labeled platform.
Target Audience
Primary customers are financial advisory firms, wealth managers, pension providers, and financial institutions that need continuous, systematic decision intelligence embedded into their existing advisor workflows or client-facing platforms.
Features
- Seven specialized agents covering pension accumulation, retirement decumulation, portfolio building, portfolio health, tax-impacting events, ESG health, and mortgage fit, each converting ranked signals into execution-ready actions
- Signal engine that detects statistically significant deviations across market, portfolio, and behavior data, filtering inputs through severity, confidence, policy, ESG, and client suitability constraints
- Decision engine that constructs optimized actions from multiple inputs (e.g., portfolio drift + client mandate + risk signal + ESG constraint) rather than selecting from predefined outputs
- ML/quant scoring validated on out-of-sample historical data with rolling time splits, no lookahead bias, multi-horizon testing, and per-signal evaluation
- Continuous real-time monitoring with automatic action triggers when thresholds are breached, demonstrated in live portfolio examples showing drift detection within 12 minutes
- Deployment options including an embeddable decision API for CRMs, rebalancers, and advisor workstations, or a fully white-labeled platform with workflow and reporting layers
- Feedback loop capturing execution outcomes, client responses, and market context to recalibrate models and improve signal accuracy and confidence calibration