How we build
Phoenix Tree AI products are built around a local working record. Code handles facts that should be repeatable. The agent investigates within a defined task. A person keeps control of the decision and any external action.
Method
The first question is what a person has to check, compare or reconcile again next week. A vague request for AI is not a workflow.
Raw responses, documents and imported files keep their identity. Later output can point back to the record that produced it.
Rules, ledgers, joins and arithmetic live in deterministic code. The same inputs should produce the same result.
The agent receives selected context and tools for the current task. It can investigate, but it does not become the system of record.
Conflicts, stale evidence and unconfirmed mappings stay on screen. A useful system can say what it does not know.
A screenshot proves the interface exists. Installation, first run, real inputs and recovery prove that the product path works.
Two concrete paths
Global investment research
Market-specific data paths collect source records and preserve where each item came from.
A research case keeps claims, selected evidence, calculations and conflicts together.
The agent works inside a restricted tool catalog and must cite the evidence it uses.
Missing inputs remain visible, and an earlier decision is frozen instead of quietly rewritten.
Multi-channel commerce
Files and read-only marketplace adapters create immutable local source snapshots.
Product, variant and location matches remain candidates until a person confirms them.
Deterministic ledgers and rules calculate inventory, profit and exceptions.
The workbench queues what needs attention while remote marketplace writes stay disabled.
Division of work