The Acme invoice is checked and ready. It matches the order and what was delivered, and it’s within budget. Here’s what’s left for you:
Next Steps
Done. The payment to Acme is scheduled for Friday, and the invoice and your approval are filed.
Sent. Their contact got a note that the invoice is approved and scheduled to pay Friday.
Connectors ingest your ERP, warehouse, and comms on read-only scopes you grant. One resolution engine decides what is the same across systems, so every vendor, item, and posting lands in a single governed graph with lineage back to the source.
Phyvant observes real work: the queries your experts run, the exceptions they resolve. It infers procedures your team can read and edit, corrections become rules, and decisions become precedents. No interviews, no workshops.
Procedures run back against your systems under graduated autonomy. Approval gates pause exactly where you would require sign-off from a person, and every action traces to the rule and precedent behind it. Audit-ready by construction.
Everything it learns is a versioned, cited asset that compounds with every run. It lives in your VPC with RBAC and immutable audit logging. Bring any model; nothing leaves your boundary or trains anyone else’s.
Phyvant is enterprise software that learns how your team does expert work by observing it, turns that judgment into governed, auditable procedures, and runs them with AI agents inside your own cloud. It captures the operational knowledge that usually lives only in people’s heads, then executes it at scale.
It starts by letting the model improvise: the AI works the problem freely while your experts watch and validate the approach. Once it is proven, Phyvant locks it into a fixed, deterministic procedure that runs the same way every time and cites every step, so you get an agent’s speed with a result you can reproduce and audit.
Read: deterministic vs. agentic engineering →Phyvant captures tacit, tribal knowledge by observing experts at work and recording the judgment behind each decision, then encoding it as a living procedure your company owns. When a senior expert leaves, the know-how stays and keeps running, improving as other people correct it.
Traditional documentation means pulling experts into interviews and workshops, then writing process maps that are stale the day they ship. Phyvant skips that entirely. It learns by watching the real work happen, the systems people touch, the decisions they make, and the judgment behind each one, and turns that into living documentation that stays accurate because it is the same procedure your agents run. No consultants, no questionnaires, no binders nobody reads.
Read: how Phyvant documents how your team works →The procedures are living, not a one-time snapshot. Every exception your experts correct and every change to a system, rule, or workflow is captured back into the encoded procedure, so the documentation of how your team works stays current instead of going stale. Each change is versioned and auditable, so the procedure your agents run tomorrow reflects how your team actually works today.
Yes. Phyvant runs inside your own VPC or data center with your choice of model, whether frontier, open, or fine-tuned. Your data, prompts, and model weights never leave your perimeter, and nothing is used for cross-tenant training, so regulated teams keep full data sovereignty and a complete audit trail.
RPA automates fixed clicks and breaks when a screen changes. Copilots suggest but never own the work. General agents improvise without your rules. Phyvant captures your experts’ real procedure and runs it end to end, governed, cited, and auditable, while adapting as the work changes and learning from every correction.
Read: why AI that aces the demo fails on day one →