Engineering an Agentic AI mesh to execute sub-second portfolio rebalancing and continuous tax-loss harvesting—eliminating slippage and capturing 18% more Tax-Alpha.
Trusted by Leading Fortune 500 Innovators
High-net-worth (HNW) asset management platform requiring continuous, non-custodial portfolio oversight.
AI Architect + Quant Engineer + DevSecOps Lead embedded within the Investment Strategy unit.
Transitioning from batch-based rebalancing to autonomous, signal-driven asset optimization.
Event-driven market feeds, Python-native Quant-nodes, and K8s-orchestrated execution engines.
The client relied on legacy 'End-of-Day' batch processing to identify portfolio drift. During periods of high market volatility, portfolios remained outside of target allocation for up to 18 hours, resulting in significant tracking error and missed tax-optimization windows.
The friction was technical: rebalancing 50,000+ custom portfolios manually or via basic scripts created a 'Rebalancing Lag' that eroded net performance. The firm required a transition to an Agentic model where individual 'Portfolio Agents' monitor and execute trades autonomously within strict risk guardrails.
Portfolios were drift-corrected once per 24 hours, leading to significant intraday variance.
Agents monitor drift in real-time, executing corrections within milliseconds of a threshold breach.
Tax-loss harvesting was a manual administrative task performed 4 times a year.
Agents continuously harvest losses at the optimal moment, maximizing the tax-shield for HNW clients.
System slowed down as the number of clients grew, increasing rebalancing delay.
K8s-native nodes spin up dynamically to handle market surges across millions of portfolios.
Direct integration with custodian APIs using pre-warmed connections to eliminate execution lag during volatile windows.
Autonomous decisions are traceable back to the exact market signal, ensuring 100% regulatory defensibility.
The AI calculates complex wash-sale scenarios across multi-custodial accounts to ensure no tax credits are invalidated.
Pre-built logic for managing stateful AI agents in high-concurrency financial environments.
Production-ready templates for wash-sale detection and real-time loss-harvesting logic.
Real-time dashboards for monitoring agent health, trade efficacy, and portfolio drift across the entire platform.
Automated compute right-sizing to maintain latency targets during high-volatility spikes without overspending on cloud.
Autonomous harvesting captured intraday volatility opportunities that were previously missed by batch systems.
Continuous monitoring reduced the time portfolios spent outside of target allocation by 92%.
Event-driven architecture ensures trades are placed before market conditions shift against the client.
Client Testimonial
Coretus transformed our platform from a management tool into a mathematical engine. Our agents harvest losses 24/7 with a level of precision that humans simply can't match. We’ve effectively eliminated the rebalancing lag for our entire client base.
CTO // Tier-1 Wealth Management