One platform. All five context layers. Executable output. No consultants.
Enterprise AI deployment stalls on documentation. Process mining tells you what to automate; it doesn’t produce the machine-executable specs agents need. Phyvant closes that gap with a continuous loop.
Current tools are single-layer specialists. Celonis sees ERP event logs. Mimica records desktop activity. None see the full picture. Phyvant captures all five in-house:
End-to-end workflows from SAP, Oracle, NetSuite event logs
Desktop clicks, copy-paste, spreadsheet work process mining misses
Policies, SOPs, tribal knowledge ingested as typed artifacts
Every API call and tool action logged for real-time system state
Expert corrections, judgment calls, and edge-case decisions inline
What we observe doesn’t sit as raw events. It resolves into a typed graph: customers, vendors, SKUs, contracts, invoices, and the relationships between them. The inferencer reads from the graph. Agents reason over it. Corrections write back to it.
Inside Phyvant’s graph
Why a graph, not a vector store
Sample subgraph
ACME Corp · EMEA
The orchestrator assembles work sessions from all five layers and hands them to the spec inferencer. It detects recurring patterns and outputs machine-readable JSON that agents consume directly.
Candidate specs are tested against correction history and gold-standard references. Passing specs go live. Failing ones return to the inferencer with a clean diff.
The platform owns execution end-to-end. The LLM never holds tool definitions, eliminating prompt-injection risk. Every action, every API call, every escalation, every decision, has provenance back to the spec it ran against and the observation that produced the spec.
Experts don’t document. They correct. When a domain specialist fixes a classification or overrides a mapping during normal work, the correction writes back to the live spec at 100% confidence. Agents downstream pick it up on their next run.
We’ll connect to your systems and show you what Phyvant infers from your data in the first week. No embedded engineers, no multi-month implementation, no retraining cycle.