Example build conceptAI WorkflowDemo · 02

AI Research Workflow

Turn a manual research process into a guided AI workflow with review, saved context, and exportable briefs.

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Concept only. The scope, features and timeline are how we would plan this build. The screens are illustrative and there is no client behind it. Real engagements always start with a fit call and a discovery sprint.

Service: AI product workflows

app.example-studio.com/workflow
Brief
2
v1
v2
Draft
4
v1
v2
v3
Review
3
v1
v2
v3
Done
6
v1
v2
v3
Problem

What the team is trying to solve

A lean team is running the same multi step research process every week. They want an AI tool that handles the steps, but keeps them in the loop for review and approval before anything ships.

Product shape

What we would actually build

A guided AI workflow product. Users move through clear steps: gather context, run the model, review the output, save the session, export the result. Brand aware prompts. Human in the loop checkpoints.

Target users

Strategy, research, content, and operations teams running repeatable knowledge work.

Core features

The build, broken down

  • Guided intake form
  • Research queue with status states
  • AI summary draft per source
  • Inline human review and edits
  • Saved context library
  • Export to brief, doc, or PDF
  • Full run history
  • Cost guardrails per workspace
User flow

How someone moves through it

  1. 1
    Brief

    User submits a research question with sources.

  2. 2
    Collect

    The system pulls or accepts source material.

  3. 3
    Draft

    Model generates structured summaries.

  4. 4
    Review

    Human edits, accepts, or rejects sections.

  5. 5
    Export

    Result becomes a brief, doc, or shareable link.

Deliverables

What you get at the end

  • Guided workflow UI
  • Prompt and context architecture
  • Review and approval states
  • Run history with replay
  • Export pipeline
  • Cost and usage dashboard
Suggested tech

A starting stack, not a hard rule

  • Next.js or Vite plus React
  • OpenAI, Anthropic, or Gemini APIs
  • Postgres plus pgvector for embeddings
  • File storage on S3 or Supabase
  • Background jobs with Inngest or Trigger.dev
  • Resend for notifications
Typical timeline
5 to 8 weeks

Real scope depends on the discovery sprint.

Best fit for
Strategy, research, content, and operations teams running repeatable knowledge work.
Launch checklist

What we tick off before going live

  • Core flows tested end to end
  • Auth and permissions verified
  • Analytics events on every key action
  • SEO, sitemap, and Open Graph in place
  • Performance budget on the critical path
  • Accessibility quick wins shipped
  • Backups and error monitoring live
  • Handover docs and runbook delivered

Want something like this built for your product?

Send the rough version. We can help shape the scope.

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