Where the systems of tomorrow are built.
Pluggedspace Labs is our research and innovation engine. Four specialized engineering facilities investigate difficult frontiers across autonomous intelligence, distributed networks, zero-trust security, and physical edge hardware.
Four Specialized Engineering Labs
Each lab focuses on a critical operational domain, bridging theoretical research with mission-critical systems engineering.
Intelligence & Agentic Systems Lab
Investigating multi-agent swarm coordination, autonomous reasoning loops, deterministic state machines, and human-in-the-loop constraint enforcement.
Distributed Infrastructure & Data Engine Lab
Developing high-throughput vector indexing, sub-second knowledge bridges, and distributed streaming meshes connecting enterprise data lakes to live agent runtimes.
Zero-Trust & Cryptographic Security Lab
Architecting continuous compliance verification, tamper-proof cryptographic audit ledgers, and sovereign air-gapped sandboxes for high-consequence environments.
Physical Systems & Edge Hardware Lab
Exploring the convergence of software intelligence with the physical world — robotics controllers, industrial sensor telemetry, and edge-computing field hardware.
Lab → Research → Product
Every Pluggedspace platform begins as a lab experiment, passes through rigorous empirical validation, and matures into enterprise-grade operating infrastructure.
Explore (Labs)
Four specialized research facilities formulate theoretical blueprints, explore difficult technological frontiers, and engineer rapid prototypes.
Validate (Research)
Empirical benchmarking, mathematical proof formulation, and publishing formal whitepapers from production field test telemetry.
Deploy (Platforms & Systems)
Validated engineering is hardened into production operating platforms (Console, Atlas) and physical field infrastructure.
From the Labs & Research Hub
Pluggedspace Console: An Enterprise Agentic Operating System with Unified Memory Substrate, Tool SDK, and Marketplace for Cross-Agent Intelligence Transfer
The rapid proliferation of Large Language Models (LLMs) has shifted the paradigm from simple conversational AI to autonomous agents capable of executing complex business workflows. However, existing agent frameworks often suffer from "memory silos," where specialized agents cannot share learned context, creating inefficiencies and limiting cross-domain insight generation. We present **Pluggedspace Console (v4)**, an enterprise Agentic Operating System whose primary contributions include **BrainBox** (a unified memory substrate), a **Tool SDK** with 28 tools, a **Tenant Agent Builder** for custom agent creation, a **Marketplace** for agent sharing, and an **Observability Dashboard**. BrainBox utilizes a four-layer architecture comprising an Event Store, Semantic Memory (via pgvector), a Knowledge Graph, and an Inference Layer, enabling heterogeneous agents to collaborate through shared semantic, episodic, relational, and inferential memory while maintaining strict tenant isolation. The system has evolved through four major iterations: V1 (siloed agents), V2 (shared Runtime Engine and dynamic Tool Registry), V3 (unified SDK, Artifact Store, Memory Bus API, Policy API, Observability, Tenant Agents, Marketplace), and V4 (enhanced ecosystem with 28 tools, Skills System, Background Scheduler, Package System, and Console Interface). To ensure operational safety in high-stakes enterprise environments, Pluggedspace Console implements Human-in-the-Loop (HITL) orchestration through a centralized Approval Queue with configurable autonomy modes and a formal Policy Engine for constraint-based governance. We evaluated the system using enterprise workloads across seven agent verticals and 28 tools, observing improvements in memory efficiency (37% LLM call reduction), cross-agent intelligence transfer (84% entity propagation), HITL safety (100% high-risk interception), and modular deployment (90% time reduction). Our results suggest that unified memory architectures combined with SDK tooling and marketplace ecosystems have the potential to transform memory from a per-agent cost center into a shared system resource.
Trading Platform Stabilization & Deriv API Integration
VTMOption's trading platform experienced operational challenges that affected reliability, transaction processing, and integration capabilities. The existing system required stabilization, improved connectivity with external trading services, and ongoing operational support to ensure uninterrupted service delivery. Additionally, the business needed integration with the Deriv API to enable seamless communication between its trading workflows and external trading infrastructure while maintaining performance and reliability.