How to preserve AI purchasing decision records for easier business review

When purchasing evidence sits across shared folders, chat approvals and AI traces, reviewing a purchase months later means reconstructing the work. Preserve AI purchasing decision records that connect the evidence, recommendation, approval and confirmed ERP outcome so your team can review the case without gathering everything again.

An ontology is a shared model of your business objects and their relationships. Connecting requisitions, evidence versions, recommendations, reviewers and executed actions gives automation a structure for assembling the record as purchasing happens. You gain less retrospective evidence gathering and a reusable foundation for subsequent workflows.

FAIS’s business ontology and workflow approach connects that model to existing systems. Your ERP remains the transaction system, while evidence repositories supply the supporting material. Suitable connectors and shared definitions preserve the value of your current software and let future agents reuse established relationships rather than recreate them for each task.

What should your purchasing record contain?

A purchasing decision record should preserve the recommendation, the evidence and rules used, the reviewer’s decision and the confirmed ERP outcome. The following is a proposed implementation schema—not a PostHog requirement or a universal legal minimum.

• Requisition: stable identifier, requested items, quantities, requester and relevant business purpose.
• Evidence versions: the supplier quotation, stock position or other material used, with its version and retrieval time. Preserve an authorised snapshot or selected excerpt where the original can change.
• Applicable rules: the purchasing-policy version and approval conditions applied to this case.
• Recommendation: proposed action, recorded business rationale and supporting evidence references; retain a trace identifier for diagnostic lookup while available.
• Reviewer decision: reviewer identity, approval or rejection, changes made and timestamp.
• Executed action: ERP record identifier, submitted values and confirmed outcome, including failure or subsequent correction.

Keep a recommendation distinct from an approval, and an approval distinct from an executed transaction. Each is a separate object connected to the same requisition, giving reviewers explicit questions to answer instead of a conversation to interpret.

Replace scattered evidence with a connected purchasing case

Consider an illustrative Taiwan manufacturer whose purchasing assistant recommends replenishing bearings. Before implementation, the quotation sits in a shared folder, the recommendation in a trace, approval in chat and the eventual purchase order in the ERP. A later reviewer must assemble the case manually.

After implementation, the connected workflow captures the handoffs:

1. The assistant’s recommendation references the exact quotation and stock evidence used.
2. Workflow automation records the applicable purchasing rule, requests human approval and attaches the reviewer’s decision.
3. Following approval, the ERP connector submits the purchase order and records the confirmed identifier—or failed submission—against the same case.

FAIS would model these relationships and connect the relevant systems, building evidence capture into each handoff rather than adding an archive task afterwards. A recorded rationale is the explanation supplied with the recommendation, not access to the model’s internal reasoning or proof of correctness.

What survives when PostHog diagnostics expire?

PostHog’s retention policies illustrate why diagnostic telemetry and business evidence need separate jobs. Traces help investigate software behaviour; purchasing records explain why a purchase was recommended, who accepted it and what happened next.

PostHog’s October 27, 2026 announcement schedules event-storage changes for November 30, 2026. Selected diagnostic properties and person-update instructions, including $set and $set_once, expire 60 days after receipt. Resulting person properties remain; events and their other properties are retained as normal. Feature-flag calls move to dedicated storage with 90-day retention. The change concerns specific data, not all customer events. Announcement.

Documentation accessed on October 29, 2026 describes a full AI-event copy retained for 30 days, alongside a trimmed event without large properties such as inputs, outputs and tools. Older traces can retain model, provider, token counts, cost, latency and trace IDs, but lack generation input or output content. AI event retention.

PostHog documents Cloud event retention of 1 year on the free plan and 7 years on paid plans. Those periods are separate from raw conversation retention. General event retention.

Prove retrieval before expanding the workflow

Start with one existing purchasing process. Ask purchasing, finance, legal and relevant data owners to choose the evidence needed, retention periods for each record type and access permissions. Base those decisions on your obligations rather than a diagnostic retention period.

Run a reconstruction test with diagnostic payloads unavailable. Ask an authorised reviewer to identify the request, inspect the evidence version, explain the applicable rule, identify the approver and verify the ERP outcome. Test broken source links and an unauthorised access attempt too.

FAIS’s agent-operation work could include repeating the test across retention boundaries. Measure retrieval time, missing evidence and cases with unconfirmed ERP outcomes before expanding automation.

Your next decision is which evidence one purchasing workflow must preserve—not how to retain every conversation. Discussing your purchasing process and system relationships with FAIS can help establish where the shared model and automated capture belong, so later reviews depend on a connected case rather than retrospective reconstruction.