Receipts, Screenshots and Voice Notes

The situation

Support tickets don't arrive as tidy JSON. A creator disputing a payout sends a photo of a receipt, a screenshot of the payouts page, a forty-second voice note and a clip of the stream that died — four formats, one problem. Code Nexus Assist now has an analyzer that reads forms, images, audio and video and returns fields with confidence scores. The team wants those fields turned into records the assistant can search and cite, without anyone having to re-read every attachment.

What you'll learn

• AI-103 content extraction: analyzers for documents, images, audio and video, OCR and layout, structured and Markdown output, per-field confidence, evidence-linked records, indexing clean representations, reconciling contradictory readings, and routing uncertainty to review instead of inventing values.
• Choosing analyzer output: Markdown for downstream reasoning and RAG, structured JSON fields with confidence and grounding for records.

Who you work with

• 

  Amara Okafor

  ML and Data Engineer

• 

  Jax Rivera

  Creator Partnerships Lead

Scenes

1. 1.Four formats, one problem
2. 2.Which output can you build a record from?
3. 3.The extraction and indexing flow
4. 4.Two readings that disagree
5. 5.Extraction check

What you leave with

The four attachments became one honest picture. The extraction flow accepts a field only when it is present and confident, names every gap, keeps doubtful records out of the index, and writes clean field lines for the records that pass. The reconciliation step builds only on agreement: conflicting readings stay unresolved with both candidates kept, a one-sided reading isn't treated as confirmation, an empty reading stays missing, and a failed analyzer is reported by id. Nothing was invented, and every doubt was routed to a person with the evidence attached.