Prepare and import App context - FloKit.AI

FloKit can start from customer text and files even when a website is unfinished. The package is uploaded through the authenticated dashboard; never publish customer materials, tokens, private account IDs or customer lists in a public page or URL.

Workflow

Select the App and open Asset Library → Context.
Open Import an agent-prepared context package.
Give the agent only permitted customer materials and public sources.
Paste the JSON package and choose Validate and import for review.
Correct any validation errors, then review product context, audiences and assets before approval.

Replacing non-empty active context requires an explicit context restart. Imported content is inert data and is never executed.

Accepted package format

The importer accepts only schemaVersion: 1 and containsPrivateData: false. Every asserted fact carries sourceIds naming entries in sources, plus an evidenceState: verified, user_confirmed, inferred, missing, or conflicting. Top-level fields are:

Field	Meaning
`generatedAt`	ISO timestamp for package creation
`sources`	Customer file/text, website, store, provider, or other source references
`product`	One-liner, description, use cases, features, benefits, and jobs to be done
`audiences`	Named audiences with needs, motivations, objections, and provenance
`messaging`	Value proposition, positioning, approved/forbidden claims, proof, hooks, and tone
`market`	Countries, locales, compliance facts, and product constraints
`brand`	Confirmed colors, typography, and voice
`pricingFacts`	Read-only pricing facts; never instructions to edit provider prices
`media`	Permitted HTTP(S) media references and explicit rights confirmation
`missingFacts` / `conflicts`	Explicit unknown or contradictory information

Valid synthetic example

{
  "schemaVersion": 1,
  "containsPrivateData": false,
  "generatedAt": "2026-09-09T10:00:00.000Z",
  "sources": [
    { "sourceId": "brief-1", "type": "customer_file", "label": "Approved product brief" }
  ],
  "product": {
    "oneLiner": {
      "value": "Guided focus sessions for busy professionals.",
      "sourceIds": ["brief-1"],
      "evidenceState": "user_confirmed"
    },
    "features": {
      "values": ["Timed focus sessions", "Weekly progress review"],
      "sourceIds": ["brief-1"],
      "evidenceState": "user_confirmed"
    }
  },
  "audiences": [
    {
      "name": "Busy professionals",
      "painPoints": ["Frequent interruptions"],
      "motivations": ["Finish priority work"],
      "objections": [],
      "jobsToBeDone": ["Protect focused time"],
      "sourceIds": ["brief-1"],
      "evidenceState": "user_confirmed"
    }
  ],
  "messaging": {
    "approvedClaims": [],
    "forbiddenClaims": [],
    "proofPoints": [],
    "hooks": []
  },
  "market": {},
  "brand": {},
  "pricingFacts": [],
  "media": [],
  "missingFacts": ["Approved outcome claim"],
  "conflicts": []
}

Arrays omitted from the example are optional only when the schema gives them a default. Unknown top-level fields, unknown source IDs, non-HTTP(S) URLs, and secret-shaped content are rejected. Bundled text is always treated as inert customer data and never executed as instructions.