AI と異常検知

モデルはプロジェクトの三つの場所で働きます: イベントがベースラインを離れたときを告げる検知、見たものにどう対処するかを述べる提案、そして問いをクエリに変える Ask。どれも単独では動きません。提案が現実になるのは人が承認したときだけです。

At a glance

Detection: Daily counts against a fourteen-day baseline
Proposals: Origin, rationale, and a recorded human decision
Ask: Question to query, query kept on screen
Model: Chosen by the plan, per tier
Auth: Access token; nothing executes unapproved

検知は日次カウントを十四日のベースラインと比べます。逸脱は算術であり、意見ではありません。
すべての提案は出どころと根拠を携え、その決定は誰が下したかとともに記録されます。
Ask の翻訳されたクエリは画面に残ります。どのモデルが答えるかはプランの選択です。

仕組み

Detection is arithmetic over the warehouse: a day whose count deviates from the fourteen-day baseline past the threshold is an insight, marked as a spike or a drop. A deviation worth acting on, or a pattern in support threads, becomes a proposal: a cohort to build, a flag to add, a message to send, or a dashboard to create, each carrying what it saw and why it suggests what it does. Proposals queue in the Inbox until a person approves, edits or rejects them, and the decision is stored with its decider. Ask is the third piece: the model emits only the validated query language, never free SQL, and its translation stays on screen beside the answer. The broader model story, entitlements and safety posture live on the AI pillar.

アクセス先

Surface	Operation
REST	None
GraphQL	aiInsights, aiProposals, aiTranslateQuery, aiAnswer queries · approveProposal, editProposal, rejectProposal mutations
Realtime	aiProposals subscription: new proposals stream as they are raised
MCP	None

例

Bash

curl "https://api.prodantix.com/graphql" \
  -H "Authorization: Bearer $ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "query ($p: String!, $e: String!, $f: String!, $t: String!) { aiInsights(projectId: $p, event: $e, from: $f, to: $t, threshold: 0.3) { bucket kind value baseline } }",
    "variables": { "p": "'$PROJECT_ID'", "e": "signup_completed", "f": "2026-08-01", "t": "2026-08-28" }
  }'

ここから始める

概念

AI と異常検知

仕組み

アクセス先

例