Enterprise AI Automation & Compute.

Not every computation benefits from the same hardware. A 1,000-element filter is faster on a single CPU core than on a GPU that needs 2ms just to set up the dispatch. A 10-million-row aggregation is orders of magnitude faster on GPU. The correct answer depends on the operation, the dataset, and the device.

Our adaptive dispatch engine evaluates every operation at runtime using a scoring function that considers dataset cardinality, operation type, data distribution characteristics, and available hardware capabilities. Each of the three execution tiers receives a score:

The highest-scoring tier executes the operation. If a workload would cause pathological GPU behaviour (SIMD branch divergence, atomic contention), the GPU score is set to negative infinity via categorical GPU inhibition, preventing selection regardless of dataset size.

When Tier 3 is selected, operations execute on the GPU via WebGPU compute shaders. These are general-purpose programs that run in the browser security sandbox with direct access to GPU compute pipelines, storage buffers, and workgroup shared memory.

We have built GPU implementations for the operations that dominate enterprise data workloads:

• •Sorting. IEEE 754 float radix sort. O(n) time. Pending patent GB2606693.6.
• •Filtering. Parallel predicate evaluation with stream compaction. GPU processes all rows simultaneously.
• •Aggregation. Workgroup-local reduction with atomic global accumulation. SUM, COUNT, AVG, MIN, MAX on millions of rows in milliseconds.
• •Text search. Two-phase pattern matching: frequency histogram pre-filter followed by brute-force candidate matching.

Multi-step pipelines use our pipeline fusion engine to chain operations without round-tripping data between GPU and CPU. The output buffer of one operation becomes the input of the next. Only the final result transfers back.

Every tier in our architecture executes locally. CPU operations run on the main thread. Web Workers run in the same origin sandbox. WebGPU compute shaders execute on the device GPU. At no point does data leave the client machine for processing.

This is not a feature toggle. It is a structural guarantee. There is no server endpoint to send data to, no cloud function to invoke, no third-party API in the processing pipeline. The system is architecturally incapable of exfiltrating data because the network path does not exist.

For regulated industries (healthcare, finance, government, education), this eliminates an entire category of compliance risk. GDPR Article 28 processor agreements, HIPAA BAAs, and cross-border transfer impact assessments become unnecessary for the compute layer because there is no processor and no transfer.