Proximal Agentic Compute (PAC) is Proximal's enterprise AI platform — it decomposes your question, routes across sources and silicon, resolves conflicts, and synthesises a single grounded answer. All at 50% lower total cost of ownership.
PAC combines intelligent workload distribution, CPU+GPU optimization, and unified management to deliver superior AI performance with 50% cost savings.
Large language model inference, reasoning, and synthesis — routed to tensor cores for maximum throughput.
Transactions, financials, and ERP queries — executed on x86 cores where relational workloads run most efficiently.
Semantic search and embedding lookups — flexibly routed to CPU or GPU depending on index size and latency budget.
Relationship traversal and supply-chain queries — CPU-bound graph algorithms that map naturally to x86 cores.
Balances latency, cost, and quality across every sub-query — automatically picking the cheapest silicon that clears the quality bar.
PAC continuously monitors workload patterns and re-routes in real time — no manual tuning, no over-provisioning.
Llama, Mistral, Qwen, DeepSeek — run any open model on your own infrastructure at near-zero marginal cost.
Bring your fine-tuned models. Enterprise fine-tuning with full control over training data and weights.
Advanced analytics across structured datasets — SQL, time-series, and financial modelling at enterprise scale.
Comprehensive insights across text, images, PDFs, and documents — all queryable through one interface.