Sovereign Scalable AI Solutions

Throughput-oriented jobs (captioning archives, large-scale transcription, multilingual translation, compliance scanning) can be queued and run in large batches when renewable-indexed pricing is lowest. Because latency isn’t critical, we maximise tokens per watt with aggressive batching and longer sequence windows, then export results to the customer’s object store with a carbon-accounted certificate per job.

Converting massive document, log, or media corpora into embeddings is a classic “do-it-once, update-periodically” workload. We pre-stage corpora, keep model weights resident, and roll daily/weekly deltas during low-cost energy windows—delivering the lowest $ / million embeddings while maintaining deterministic, auditable output for downstream search and analytics.

Customer-specific adaptation of open-weights models uses GB→low-TB datasets and produces compact adapter weights, which makes data movement practical. These bursty, non-interactive jobs align well with our scheduler: we spin up when renewable supply is high, checkpoint to encrypted storage, and hand back adapters with full provenance and carbon accounting.

Generating synthetic examples, extracting chunks, and rebuilding vector/RAG indexes are batch pipelines that benefit from cheap, clean energy windows. We run large fan-out map/reduce style jobs to create datasets and refresh indexes overnight, returning versioned artifacts and index snapshots optimised for downstream low-latency serving in metro DCs.

GPU-bound simulation and production (CFD, Monte Carlo risk, seismic pre-stack, and frame rendering) parallelise cleanly and tolerate queueing. By time-shifting these workloads to surplus-renewable periods, we deliver significant OPEX savings without SLA pressure, with results verified, packaged, and transferred back over backbone links for post-processing or delivery.