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AI MVP Development

An AI MVP is smaller than a pitch. AppsAura’s 20-day MVP track applies when scope fits; AI does not magically fit. We cut to one journey: a score, a try-on, a grounded Q&A. Everything else is a later milestone. If the data is not available, we will not pretend a public LLM is a substitute for your domain.

Scope an AI MVP How this works →
India-based global teamStartups, SMBs and enterprises worldwide
Architecture-firstBuilt to scale cleanly
Transparent deliveryMilestones you can track
Confidential intakeNo obligation consultation

Service overview

The MVP still needs UX, auth and a place to put the result. We reuse our MVP development process: discovery, a written must-have list, then a build that can fail in public without taking down a business. The AI piece has an eval set from day one or it is not in version one.

Problems we solve

  • An MVP with five models and no users.
  • Skipping evaluation ‘until we have data’ — then never evaluating.
  • Store rejection because the listing over-claims AI.
  • A 20-day promise for a product that needs a data partnership.

What we build

  • One job, one success metric you can observe
  • A written out-of-scope list
  • Eval set of real examples
  • Feature flag and a non-AI fallback
  • Store or web launch hygiene
  • A next-milestone backlog, not a surprise rewrite

Benefits

  • You learn whether the AI job is real before you fund a platform.
  • Compatible with our MVP development company track when the slice is small enough.

Use cases

  • A try-on on a handful of SKUs.
  • A score on a resume upload.
  • A help bot on a 30-article centre.

Technologies

FlutterReactNode.jsPython

Industries this shows up in

Honest tracking firstPlay that still teaches

Relevant case studies