ProcessGhost
The Silent Neural Engine for Real-Time Topological Process Observability & Sub-Millisecond Autonomous Healing.
Replacing brittle rule-based RPA and retrospective process mining with continuous graph neural foundation models and self-healing agentic micro-workers across enterprise event backbones.
Complex Enterprise Workflows Die in the Gaps.
Global enterprises operate across tens of distributed systems (SAP, Salesforce, Kafka, custom microservices). When transactions fail, they don't produce clean stack traces—they produce silent state drift.
Standard APM tools (Datadog, Dynatrace) track server CPU and memory, but have zero semantic comprehension of high-level business process flows.
Legacy process mining (Celonis) analyzes historical event logs 30 days after transactions complete. By then, customers have churned and SLAs are broken.
Static RPA bots break upon minor DOM or API changes, creating an ongoing maintenance tax requiring constant human intervention.
ProcessGhost: The Real-Time Process Intelligence Mesh
We replace static flowcharts with an active neural fabric that constantly monitors, models, and optimizes enterprise workflows with sub-millisecond precision.
Ultra-lightweight eBPF probes stream millions of events per second with < 0.12% CPU overhead. Zero custom instrumentation required.
Proprietary continuous graph neural network computes cross-system dependency embeddings, anticipating failure states up to 40 seconds early.
Stateless micro-agents execute deterministic state reconciliation loops in 3.4ms with zero-knowledge cryptographic audit trails.
Defensible Mathematical Architecture
Topological Temporal Process Graphs (TTPG)
Maps enterprise asynchronous states into continuous Riemannian manifolds. Ensures graph invariance across changing microservice API topologies and asynchronous message queues.
Reinforcement Learning from Process Feedback (RLPF)
Ghost Workers are trained via offline enterprise traces to converge only on Pareto-optimal reconciliation paths that guarantee zero collateral state damage.
Sub-Millisecond Dynamic Kernel Fusion
Custom parallel tensor kernels perform graph message passing directly inside hardware registers, achieving 18x throughput over legacy Python/PyTorch inference frameworks.
Stateless Compensation Mesh
All micro-agent operations are reversible via two-phase commit consensus hooks, providing mathematically guaranteed recovery if downstream endpoints fail.
High-Throughput Distributed Compute Architecture
Processing 4.8M+ events per second with live graph updates requires intensive hardware acceleration. Our architecture is purpose-built for high-density matrix compute nodes:
Quantized graph convolutions double execution throughput while preserving 99.98% anomaly classification accuracy on multi-gigabyte state matrices.
Parallel graph node aggregation executed directly inside hardware registers eliminates global VRAM round-trips by over 60%.
Targeting 48,000 accelerated cluster hours to pre-train our 70B parameter Cross-Enterprise Foundation Process Model across 500M real-world workflow traces.
$48B Total Addressable Market
High-Margin Enterprise SaaS + Compute Metering
Base enterprise cluster license, eBPF telemetry mesh & dashboard.
Metered execution fee per autonomous reconciliation loop successfully completed.
Air-gapped deployment with dedicated model training & hardware nodes.
Generational Shift in Enterprise Automation
| FEATURE | CELONIS / SIGNAVIO | UIPATH / RPA | PROCESSGHOST |
|---|---|---|---|
| Execution Latency | Batch (Hours to Days) | Minutes | < 4.2 Milliseconds |
| Observability Mode | Post-mortem CSV / Logs | DOM Scraping | Lossless eBPF Kernel Streams |
| Remediation Engine | Manual Consultant Task | Hardcoded Scripts | Autonomous RL Micro-Agents |
| Underlying Tech | Relational SQL Trees | Desktop Emulation | Topological Graph GNN |
Led by Systems Pioneers
Dana Lowe
Founder & Chief Executive Officer
dana.lowe@processghost.org
Specialist in high-throughput graph neural inference and non-blocking event topologies. Over a decade of experience architecting fault-tolerant distributed transaction systems handling tens of millions of concurrent enterprise operations.
Core Research Team
Graph Neural Systems & Kernel Authors
Distributed Systems Research
Comprising senior distributed systems engineers and machine learning researchers with prior exits and key publications in temporal graph neural modeling and eBPF low-level Linux performance engineering.
The Ask: $2.5M Seed + Accelerator Compute Grants
Capital Allocation
Target 18-Month Milestones
- ✓ Deploy into 15 Fortune 500 enterprise production networks ($3.2M ARR).
- ✓ Pre-train our 70B parameter Cross-Enterprise Foundation Process Model across 500M real-world workflow traces.
- ✓ Establish ProcessGhost as the definitive de facto standard for autonomous workflow observability.