Ingestion · Vision-Language

Laser-guided extraction from real screenshots

The engine projects a semantic grid over Stripe, HubSpot, and ad panels: numeric regions are localized, units normalized, and readings proposed with pixel-level traceability—nothing is invented.

sha256 · a3f5c2…1e9b · vlm_payload_v3

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Declared performance

Structured extraction accuracy: 99.8%
Typical latency (1 capture): < 2.4s
HITL gate: mandatory

Reasoning, not hallucination

The model proposes typed fields (currency, percentage, cohort) and attaches a short rationale for the visual crop that supports each reading.

If the capture is ambiguous (overlapping widgets, low-contrast type), the run degrades to pending and requests a new shot or manual validation.

terminal · extraction_preview.json

{
  "v": 3,
  "model": "ee-vlm-extract",
  "kpi": { "roas": 4.62, "currency": "USD" },
  "confidence": 0.998,
  "hitl": "pending"
}

Simplified—production includes schema version, org scope, and crop hashes.

Fintech-grade flow

▸Dropzone → preview → extraction → editable diff → preliminary hash → persisted row with RLS.
▸Each KPI binds to anonymized raw_data and model revision for future audits.

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