About Flenumek
Where machine intelligence meets market clarity
Flenumek was built to give analysts, portfolio managers, and independent researchers a sharper view of financial data — without the noise.
Our background
Started in 2016 from a specific frustration
The founding team had spent years working with financial datasets that were either too slow to process or too fragmented to act on. Existing tools either required expensive infrastructure or produced outputs that needed significant manual interpretation. Neither was acceptable.
Flenumek was built to close that gap. The platform applies pattern recognition models to equity, commodity, and index data — surfacing signals that a human analyst would take hours to identify manually. It does not predict outcomes. It organises information so that decisions can be made faster and with more context.
Today the platform serves clients across Canada, from independent advisors to institutional research desks.
What the platform prioritises
Speed
Signal detection runs continuously — results appear in seconds, not after a batch process.
Depth
Multi-timeframe data is layered so context is never stripped from a single reading.
Transparency
Every output shows its data source and the conditions that triggered the signal.
Adaptability
Parameters adjust to sector-specific behaviour rather than applying one universal model.
The people behind the platform
Small team, deep domain knowledge — each person works on a specific layer of the product.
Petra Vanlith
Lead Data Scientist
She designed the core signal classification layer and continues to refine model thresholds based on live market feedback.
Built by analysts, for analysts
The team includes backgrounds in quantitative research, software infrastructure, and financial compliance. Product decisions come from direct experience with the problems the platform solves — not from abstract user research alone.
Callum Dreyfus
Infrastructure Lead
Manages the data pipeline architecture that keeps latency low even during high-volume market sessions.
Roisin Baxter
Product & Compliance
Ensures the platform's outputs meet Canadian financial data standards and that the interface stays interpretable under pressure.
Real-time signal feed
Pattern alerts update as market conditions shift — no manual refresh required.
Multi-timeframe context
Daily, weekly, and intraday data layers are shown together so no signal is read in isolation.
Sector-adjusted models
Energy, financials, and tech behave differently — the platform's parameters reflect that.
Auditable outputs
Every signal includes the data window and model version that produced it — reviewable at any time.
Numbers that reflect how the platform is actually used
These figures come from platform telemetry and client reporting — not projections. The platform processes a significant volume of market data daily, and the team monitors model performance continuously to catch drift before it affects outputs.
8+
Years of continuous operation
14
Market sectors covered
4ms
Median signal latency
CA
Nationwide client coverage
