Jessica Egan connects to your data sources and runs a full multi-variate risk and return analysis the moment you confirm setup. No spreadsheets, no manual modelling — the AI does the heavy lifting, and you review the output.
Built for professionals who want data-driven decisions without a background in data science.
Most portfolio tools stop at reporting. Jessica Egan's engine ingests live market and operational data, applies statistical and machine-learning models to identify patterns humans typically miss, and converts the output into a short list of recommended actions. You don't configure parameters or tune models — the system handles that layer automatically.
Real-time ingestion keeps the analysis current rather than relying on end-of-day snapshots. Automated rebalancing then adjusts weightings when the underlying data shifts beyond pre-set thresholds, without requiring you to log in and act manually.
Setup is deliberately short. Most of the time saved comes from removing the manual steps other platforms still require — connecting feeds by hand, choosing model parameters, or interpreting raw output yourself.
Link brokerage accounts, market feeds or internal business data in a single pass. The system maps fields automatically, so there's no manual tagging or formatting required before analysis can start.
A multi-variate model evaluates correlations, volatility and historical drawdown patterns across your full dataset within seconds, not hours.
You receive a decision-ready summary with suggested allocations or actions, each with a stated confidence level, so you can accept, adjust or decline.
Passive does not mean unmonitored. Jessica Egan applies established quantitative techniques to flag exposure before it becomes a problem, and documents why a given recommendation was made so the reasoning is never hidden from you.
Every recommendation carries a risk score alongside the expected return, so upside is never presented without its corresponding downside context.
Identifies periods where price swings tend to group together, helping the model anticipate turbulence rather than react to it after the fact.
Sets dynamic thresholds that trigger a review or rebalance when a position's decline exceeds historical norms for its asset class.
Tracks how assets move relative to one another, reducing the chance of unintentional concentration across a portfolio.
Runs recommended allocations against historical stress periods before they're presented, so the output reflects more than a single market regime.
The same underlying engine adapts to different objectives depending on the data it's given and the outcome you're optimising for.
Connect brokerage or exchange accounts and receive rebalancing suggestions aimed at improving risk-adjusted return relative to your current allocation.
Feed in sales, inventory or cost data to surface predictive patterns that support decisions on staffing, stock levels or pricing before issues emerge.
Aggregate public market commentary and news flow into a sentiment score that contextualises price movement alongside the quantitative models.
Jessica Egan was built on the premise that institutional-grade analysis shouldn't require an institutional-sized team. The platform packages statistical and machine-learning methods that were previously confined to specialist desks into a setup process that takes under a minute.
We don't claim the models remove risk entirely — no analysis can. What they do is make the trade-offs visible, consistent and based on data rather than instinct, so your decisions have a documented rationale behind them.
Jessica Egan is not a single opaque algorithm. It's an ensemble of established statistical methods, each chosen for a specific part of the analysis, and each explainable in plain terms.
Market data is pulled from licensed price and volume feeds. Where you connect a brokerage or business system, data is read directly from that account via secure, read-only access — the platform never gains authority to move funds or alter your records.
Each data source is timestamped and logged, so any recommendation can be traced back to the inputs that produced it.
Regression analysis measures how strongly one variable (for example, interest rates) tends to move with another (for example, a sector's returns), producing a statistical relationship we can quantify and test.
Neural networks are used for pattern recognition across larger, less linear datasets — for example, spotting a volatility pattern that wouldn't show up in a simple correlation table. Both methods are validated against historical data before being used on live recommendations.
Each model is back-tested against historical periods it has not been trained on, to check that its logic holds outside the data it originally learned from. Models that underperform a defined benchmark during testing are not promoted to live recommendations.
Connections to external accounts use encrypted, read-only protocols. Data in transit is encrypted, and account credentials are never stored in plain text. Access to your account data is limited to what's required to run the analysis you've requested.
Connect your data and receive your first decision-ready analysis in under 60 seconds. No manual setup, no model tuning required on your end.
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