Previous Work

Previous work that shows how DevDelta actually ships

This page shows representative product builds that reflect how DevDelta scopes, designs, ships, and hands off launch-ready work.

Selected case studies

Proof of execution, not just a process pitch.

Different products, same operating model: clear scope, AI-assisted delivery, and product decisions that stay grounded in what needs to launch first.

Case studies

2

Delivery model

AI-assisted

Priority

Launch first

Handoff

Production-ready

Selected projects

Representative builds that match our current delivery model

Two different product types, one consistent delivery pattern: scope tightly, ship the core path, and leave the product in a state that can grow.

Project 01

StudioPilot turned agency onboarding into a guided client workspace

A client onboarding SaaS for creative agencies that needed a faster path from signed proposal to active workspace.

Desk setup with laptop and monitor representing a client workspace product

Timeline

3 weeks

Core modules

5

Launch goal

Faster onboarding

What shipped

  • Mapped the activation flow around first-value instead of admin setup
  • Shipped a guided setup experience, client task board, and status dashboard
  • Used AI-assisted interface iteration and QA to shorten review cycles

Outcome

The team got a demo-ready product with a clearer activation path and a system they could extend without reworking the foundation.

Next case study

Project 02

SignalDesk AI helped teams move from conversation to action much faster

An AI workflow product for internal operations teams that needed cleaner summaries, follow-up actions, and searchable context after every conversation.

Modern workspace with laptop and monitor representing an AI operations workflow product

Timeline

100h MVP

Primary users

Ops teams

Launch goal

Faster follow-up

What shipped

  • Designed the workflow around reviewable AI output instead of blind automation
  • Built structured summaries, action capture, and a searchable activity timeline
  • Kept the first release narrow so the team could test trust before scaling features

Outcome

The first version reduced manual wrap-up work and created a stable base for expanding AI features without confusing the team.

What stays consistent

The method scales across different kinds of products

The point is not to make every project look the same. The point is to keep the execution model reliable when the product context changes.

  • The first release is shaped around a testable outcome, not feature sprawl
  • AI is used to accelerate iteration, not hide weak product decisions
  • Design and engineering stay in one loop so quality does not drift
  • Every build ends with a clean handoff and a clear next-step recommendation

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