Data Intelligence Platform
Data Intelligence for Decisive Action
Fair Suretendance runs real-time predictive models tuned to Canadian market volatility, giving investment teams a statistically grounded view of risk before capital moves.
Data Intelligence Platform
Fair Suretendance runs real-time predictive models tuned to Canadian market volatility, giving investment teams a statistically grounded view of risk before capital moves.
How the Engine Works
Rather than promising outcomes, Fair Suretendance shows its work. The platform is built on three operating principles that together turn raw market data into a recommendation an analyst can defend.
Models are trained to flag early indicators of shifting volatility, so portfolio adjustments can happen ahead of a drawdown rather than in reaction to one.
The system ingests and processes millions of data points continuously across markets and asset classes, without requiring manual re-analysis for each new signal.
Outputs are filtered through B2B-specific logic, accounting for mandate constraints, liquidity needs, and sector exposure rather than generic retail assumptions.
Methodology
Every model that reaches production has already been tested against historical volatility. The process below describes how data moves from raw feed to validated recommendation.
Structured and alternative data feeds are normalized and time-stamped, so signals from different sources can be compared on equal footing.
Candidate models are run against multi-year historical scenarios, including known periods of elevated volatility, to measure how they would have performed.
Only models that clear defined accuracy and drawdown thresholds move forward, and each is re-tested on a rolling basis as new data arrives.
Representative chart shape used to illustrate model reporting; not a performance guarantee.
Applied Scenarios
The same underlying engine supports two distinct decision types common among our clients: adjusting an existing portfolio and evaluating a new allocation.
Portfolio Optimization
When a monitored asset's behaviour deviates from its historical correlation pattern, the system generates a rebalancing note with the detected anomaly and its statistical confidence level.
Use Case 01
An institutional holder uses Fair Suretendance to monitor a multi-asset portfolio for correlation breakdowns that often precede volatility spikes. Instead of reacting after a move, the desk receives a flag with the specific positions affected and a suggested rebalancing range.
Market Entry Analysis
Capital allocation scenarios are run against historical analogues to estimate a probability-weighted return range before commitment, rather than a single forecast figure.
Use Case 02
A business evaluating entry into a new segment uses the platform to model expected returns under several macroeconomic scenarios. The output is a range, not a single number, which lets the finance team size the decision against its own risk tolerance.
Data Integrity
Cautious investors tend to ask about sourcing and compliance before they ask about returns. Here is how Fair Suretendance answers both.
Models draw on global index data, exchange-level pricing feeds, and select alternative data sets, all logged with source and timestamp for later audit.
Client and portfolio data is handled in accordance with PIPEDA requirements for personal information protection under Canadian federal law.
Detailed backtesting results for your specific mandate are shared during onboarding once data-sharing terms are agreed.
Common Questions
Models are backtested against known historical shock periods, so their behaviour under stress is documented rather than assumed. When live volatility exceeds the range seen in training data, the system flags reduced confidence on its output instead of issuing a recommendation with false certainty.
Yes. Risk tolerance parameters, such as maximum acceptable drawdown or sector concentration limits, are configured per mandate before the engine generates recommendations, so output reflects your constraints rather than a generic default.
Data ingestion and initial analysis run continuously, with most anomaly flags surfacing within minutes of the underlying data becoming available. Formal recommendation reports, which include supporting statistics, are typically compiled on a daily cycle.
Recommendations and data exports are delivered in standard formats compatible with common portfolio management and reporting tools. Integration specifics are scoped during onboarding based on your current stack.
Join the waitlist for our next cohort of institutional-grade analysis, and receive onboarding details as capacity becomes available.