A technical walkthrough of the runtime, GPU acceleration layer, and pipeline architecture powering our enterprise intelligence platform — from raw event to scored prediction in under 12 milliseconds.
Every event that enters the platform passes through the same four checkpoints. Pick a stage below to see what's actually running underneath it.
Every write to a source database is captured the moment it happens using Kafka and Debezium change-data-capture — nothing is polled, nothing is missed. Continuous feeds move straight from legacy databases into Delta Lake storage, ready for processing.
Apache Flink and cuDF run transformations and time-window aggregations directly on GPU memory. Because the data never has to leave VRAM to be reshaped, the wait between "event happened" and "event is ready for scoring" stays under 200ms.
This is the layer most platforms skip. Our own CUDA C++ kernels handle matrix factorization and graph traversal directly on NVIDIA Tensor Cores, with no round-trip through the CPU to slow things down.
Multiple TensorRT-optimized models run side by side under one server, with requests batched dynamically as they arrive. That's what keeps the 99th-percentile response time under 12ms even when traffic spikes.
Kubernetes-native orchestration that knows which pods need a GPU and which don't, splits capacity accordingly, and rolls out updates without taking anything offline.
React and WebAssembly front ends with real-time, two-way data streams. Dashboards are white-labeled and built to hold up on a screen in front of your leadership team.
Raw telemetry and financial data get reshaped on GPU, not CPU, so queries that used to take minutes at petabyte scale come back in under a second — with anomalies flagged automatically.
We read the market the way a graph reads relationships: trend analysis, competitor mapping, and live streaming signals combined into one continuously updating picture.
Verified production numbers from a live enterprise cluster, not a lab environment.
The runtime libraries and acceleration engines every product on the platform is built on.
Custom kernels for feature engineering, time-series anomaly detection, and matrix factorization — written for this workload, not adapted from a general-purpose library.
End-to-end GPU dataframes, machine learning, and graph analytics in one toolkit, so market network mapping never has to leave the GPU to finish a job.
Model orchestration with dynamic batching and concurrent execution, running TensorRT-optimized branches to hold sub-12ms latency under load.
Three ways to run Altitude, depending on how much control your infrastructure team needs.
The next two milestones for the computational core.