AlphaBitCore provides an agent‑native virtualization layer that aggregates APIs, scripts, and data sources into a single Managed Control Plane, allowing LLM agents to access only the tools required for a task. The platform enforces policy‑driven workspaces, hybrid on‑prem sandbox and cloud routing, and automatic model selection with failover, delivering low‑latency, governed, and auditable AI workflows for large financial institutions. It is sold under enterprise subscription licenses.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprise AI agents that consume raw APIs encounter three core issues: (1) exposure to hundreds of endpoints creates context gaps that increase hallucination risk, (2) remote‑only routing adds latency that degrades real‑time decision making, and (3) sensitive data cannot be safely transmitted to external gateways, violating data‑sovereignty policies. These challenges prevent large financial institutions from deploying production‑grade, agentic workflows at scale.
Solution
AlphaBitCore delivers an agent‑native virtualization layer that aggregates disparate tools, scripts, and data sources into a single executable Virtual MCP (Managed Control Plane). By partitioning resources into policy‑driven workspaces, the platform limits each LLM’s view to the exact tools it needs, reducing hallucination and enforcing governance. The Virtual MCP orchestrates tool selection, performs automatic model routing (e.g., OpenAI vs. DeepSeek), and provides built‑in failover across backends. Execution occurs in a hybrid runtime: sensitive code runs locally inside a sandboxed container, while SaaS calls are routed securely to the cloud. All interactions are logged and observable through a unified dashboard, enabling compliance teams to audit access and performance. The result is a production‑ready AI control plane that delivers low‑latency, governed, and data‑secure agentic workflows for enterprise use cases.
Target Audience
Primary customers are engineering and AI platform teams at large financial services firms—including global investment banks, credit‑rating agencies, and wealth‑management platforms—that require governed, low‑latency agentic AI deployments.
Features
- Workspace‑scoped catalogs that bind tools, context, and policy into isolated execution domains
- Virtual MCP that virtualizes multiple backends into a single service endpoint with automatic model selection and failover
- Hybrid runtime architecture: on‑prem sandbox for confidential code, remote SaaS routing for scalable services
- Policy engine that enforces fine‑grained access controls, rate limits, and data‑handling rules per workspace
- Integrated observability stack with real‑time metrics, audit logs, and compliance reporting
- Secure sandbox environment certified for SOC 2 readiness and end‑to‑end encryption of data in transit
- Central registry for versioned scripts, libraries, and connector definitions, supporting CI/CD pipelines
- API‑agnostic LLM gateway that abstracts model providers and abstracts credential management