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Generative AI Development

Generative AI is useful when it produces a draft a human can accept, edit or reject: copy, an image variation, a summary. It is expensive and unsafe when it is treated as an oracle. AppsAura implements generation behind product rules — Try Ware’s try-on path is the existence proof we already ship, not a Midjourney collage.

India-based global teamStartups, SMBs and enterprises worldwide
Architecture-firstBuilt to scale cleanly
Transparent deliveryMilestones you can track
Confidential intakeNo obligation consultation

Service overview

We wire generation into an existing domain: catalogue, resume, ticket, reading. The model never owns the system of record. We log prompts and outputs for debugging, keep PII out of logs where the product requires it, and set spend caps so a retry loop cannot invoice you into a hole.

Problems we solve

  • Unbounded generation that invents catalogue items, medical advice or prices.
  • No evaluation set, so a prompt change ships as a personality shift.
  • Image or text generation that ignores brand, size charts or policy.
  • Cost that looks cheap per token until the product is used in a loop.

What we build

  • Constrained generation (schema, catalogue IDs, tone limits)
  • Human accept / edit / reject in the UI
  • Evaluation sets before prompt changes go live
  • Image generation or try-on only where the product already has a visual job
  • Spend caps, caching and cheaper models for non-critical paths
  • Citations or ‘this is a draft’ labelling when the user might treat output as fact

Benefits

  • Generation that is a step in a workflow, not a novelty chat.
  • A way to roll back a prompt without a full app release when the API allows it.

Use cases

  • Virtual try-on and style exploration (Try Ware).
  • Draft summaries of tickets or readings for a human to send.
  • Resume phrasing suggestions that the candidate still owns (JobForge-adjacent).

Technologies

PythonNode.jsAWSReactFlutter

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