AI for Banking and Financial Institutions — Secure, Compliant, and Scalable

Yes. Kompact AI can be deployed fully on-premise on your CPU servers. All inference, data processing, and model execution remain within your network, ensuring complete data residency and regulatory compliance.

Yes. Kompact AI integrates with OpenTelemetry to provide detailed audit logs, usage tracking, performance metrics, and system-level monitoring for every model interaction.

• The runtime to execute the Models.
• Remote REST‑based server for serving model inferences remotely.
• Observability to track model and system performance.
• Client-Side SDKs in Go, Python, Java, .NET, and JavaScript, which are OpenAI Compatible for writing downstream applications that use the Kompact AI models.

Yes. Kompact AI supports large language models up to 32B parameters on CPUs with GPU-comparable throughput, enabling banks to use existing server infrastructure without additional GPU investment.

Yes. All access control, data flow, and permissions remain fully under your control within your infrastructure.

Yes. Kompact AI is fully self-hosted. Your internal infrastructure, security, and DevOps teams can deploy and manage the system without reliance on third-party cloud services.

Yes. Your model’s IP and weights remain entirely yours. Kompact AI provides the SDK and runtime to wire up your model securely. No model weights or proprietary logic are exposed externally.

Yes. Kompact AI supports enterprise-grade RAG, agentic workflows, and large document processing, making it well-suited for KYC, onboarding, regulatory reporting, and compliance automation.

Yes. CPU core-level isolation and controlled scheduling allow predictable latency for customer-facing applications such as chatbots, virtual assistants, and automated support systems.

Yes. Kompact AI provides OpenAI-compatible APIs and SDKs in Java, Python, Go, and C++, enabling seamless integration and easy migration from GPU-based environments.

Yes. CPU-based AI consumes significantly less power than GPU clusters. Scaling with CPUs reduces power draw, cooling requirements, and overall operational cost compared to GPU expansion.

Yes. A single model can be deployed across all available CPU cores to maximise throughput for high-priority workloads such as real-time fraud detection or transaction monitoring.

Yes. Multiple models can run on the same CPU with dedicated core groups assigned to each model for parallel execution.

Yes. CPU cores can be allocated per model based on workload criticality, latency sensitivity, or regulatory priority.

Yes. A single CPU can support multiple models simultaneously with separate core allocations for each use case.

Yes. CPU cores can be mapped to specific departments, ensuring operational isolation and predictable performance.

Yes. Core allocation can be adjusted dynamically to align with transaction spikes, batch processing windows, or peak customer activity periods.

Yes. Dedicated cores eliminate resource contention and ensure consistent, predictable latency for real-time workloads.

Yes. Sensitive regulatory workloads can run on fully isolated CPU cores to ensure performance stability and security separation.

Yes. Core-level isolation ensures that resource-heavy workloads do not affect the performance of other applications running on the same server.

Yes. Multiple business units can operate their own AI workloads independently on the same server with secure core-level and workload-level isolation.