Beeks Market Edge Intelligence™ is built on a modular, extensible foundation that ensures firms can scale their analytics efforts without sacrificing flexibility or performance. Each component of the solution has been designed to address a critical requirement of high-speed, AI-enabled analytics in trading environments—whether that’s processing at the wire, visualising anomalies, or integrating your own models (or your own edge timeseries data) into the data pipeline.

Featurised Data

The core of Edge Intelligence is its access to rich, granular telemetry data from financial networks. This includes metrics from market data gateways, trading algorithms, and order execution paths. These data streams are “featurised” through Beeks’ decoding layer, allowing users to extract precise, structured signals from raw packet flows. The resulting data is optimised for time-series analysis, anomaly detection, and root cause investigation.

Exogenous Metadata

To enhance model accuracy and contextual awareness, Beeks Market Edge Intelligence™ incorporates external metadata such as economic calendars, trading hours, weather data, and regulatory holidays. These inputs enable correlation with external events, improving forecasting models and supporting more intelligent resource allocation (e.g., scaling gateways in anticipation of high-impact announcements).

Time-Series Database

At the heart of the platform is QuestDB, an open-source, high-performance time-series database. QuestDB provides a columnar, SQL-native interface for ultra-fast querying and analytics, whether you're accessing wire telemetry or decoded trading messages. QuestDB provides real-time ingestion and historical queries, enabling low-latency dashboards and in-depth forensic analysis on the same infrastructure. The open nature of QuestDB makes combining Beeks Analytics and your own timeseries data easy, if you want to perform this close to the source of the data.

AI Middleware

The AI middleware layer orchestrates machine learning workflows by integrating with frameworks such as MindsDB, AutoTS, and Keras. This layer automates training and deployment pipelines, supports BYOM (Bring Your Own Model), and bridges high-speed data from QuestDB into actionable ML insights. Whether you're running anomaly detection, clustering, or predictive maintenance models, the middleware layer ensures consistent model deployment and observability.

Scalable Compute

Edge Intelligence supports distributed AI execution through GPU-enabled infrastructure deployed directly in colocation environments. This makes it possible to train and execute complex models—such as deep learning forecasting models—at the edge, without needing to send all of the data to central locations.

Data Visualisation and Exploration

The decoded and modelled outputs from Edge Intelligence can be explored using Beeks’ custom dashboards via VMX-Explorer (Grafana), or through hosted Jupyter Notebooks for more advanced, code-driven exploration. These visual interfaces make complex analytics accessible to traders, operational teams, and engineers alike — supporting everything from alert investigation to hypothesis testing.

The system provides full data transparency—whether you’re extracting PCAPs for low-level inspection or visualising streaming metrics for infrastructure tuning.

Alerting

Edge Intelligence includes a flexible event notification system that can push alerts based on thresholds, anomalies, or custom-defined patterns. This is critical in high-frequency trading environments, where immediate awareness of performance degradation or unexpected latency can protect revenue and ensure compliance.