Softpiper Solutions

Startups · Software delivery

MVP acceleration and AI-native dev workflow

Ship a credible MVP fast with Claude Code, then keep the pace with automated PR review, issue triage and scheduled maintenance runs, without growing headcount.

  • ClaudeAnthropic
  • GeminiGoogle
  • Typical timeline: 4–10 weeks

The problem

Founders need to ship a credible product fast with a tiny team, then keep shipping without building a large engineering department.

  • The first version has to be good enough to sell, not just to demo.
  • Review, triage and dependency upkeep crowd out feature work.
  • A small team can't afford a slow feedback loop.

How we solve it

We build your MVP with Claude Code under clear repository conventions, then set up AI-assisted PR review, issue triage and scheduled maintenance runs so your team keeps the pace after handover.

It plugs into GitHub or GitLab, Supabase / Postgres, Vercel or Cloudflare, Slack and Linear or Jira, so nothing you rely on today is replaced. The architecture below shows each step and which model does the work.

Architecture

From your stack, through the AI layer, to numbers you can track

Each step names the model or system doing the work. Writes to your systems wait for a person to approve them.
MVP acceleration and AI-native dev workflow. Your systems (GitHub or GitLab, Supabase / Postgres, Vercel or Cloudflare, Slack and Linear or Jira) connect to Softpiper's AI layer, which runs these steps: Repository conventions (CLAUDE.md · AGENTS.md); Build (Claude Code); Review and triage (GitHub Actions · GitLab CI); Scheduled upkeep (Claude Code Routines); Multimodal product features (Gemini API · Claude API); Ship (Vercel / Cloudflare). Outcomes are measured as lead time for changes, deployment frequency, pr review turnaround and escaped-defect rate.

Your stack

  • GitHub or GitLab
  • Supabase / Postgres
  • Vercel or Cloudflare
  • Slack
  • Linear or Jira

AI layer

Softpiper AI layer

  • Claude
  • Gemini
  1. Repository conventions, using CLAUDE.md · AGENTS.md

    Rules, commands and architecture notes live in CLAUDE.md and AGENTS.md, so every agent run follows them.

  2. Build, using Claude Code

    Features are planned, implemented and tested with Claude Code; senior engineers review every change.

  3. Review and triage, using GitHub Actions · GitLab CI

    Every pull request gets an automated review; new issues are triaged and labelled.

  4. Scheduled upkeep, using Claude Code Routines

    Nightly runs update dependencies, run the test suite and open fix PRs.

  5. Multimodal product features, using Gemini API · Claude API

    Voice, vision and generated-media features use the Gemini API; reasoning and agent features use the Claude API.

  6. Ship, using Vercel / Cloudflare

    Preview deploys per pull request and a production pipeline on your platform.

Outcomes you measure

  • Lead time for changes
  • Deployment frequency
  • PR review turnaround
  • Escaped-defect rate

Why this model

Claude and Gemini, each where it leads

We choose per task on capability, cost and where your data must stay. Final versions are picked at kickoff.
ClaudeAnthropic

Claude Code plans, writes, tests and opens pull requests, and runs in GitHub Actions and GitLab CI for review and triage. Routines run scheduled jobs, such as nightly dependency and test sweeps, in the cloud.

GeminiGoogle

For product features that need voice, vision or generated media we use the Gemini API, and Firebase or Google Cloud-centred teams can work in Gemini Code Assist or Antigravity.

Delivery

What you get, how long it takes, and how we keep it safe

Guardrails

  • A person reviews and approves every merge.
  • Secrets never enter prompts or repositories.
  • Agents work on branches with least-privilege tokens.
  • Agents you ship to end users follow Anthropic's branding rules (“Powered by Claude”, never “Claude Code”).

Plus the guardrails on every build: facts from your systems, approvals for writes and money, a human one step away, audit logs and evals. See all

Deliverables

  • Production MVP and repository
  • CLAUDE.md / AGENTS.md conventions
  • PR review and issue-triage workflows
  • Scheduled maintenance routines
  • Handover session for your team

Typical timeline

  1. MVP4–8 weeks
  2. Dev-automation setup1–2 weeks

A planning range for a first production release. We confirm scope, price and dates in a tailored proposal.

KPIs to track

  • Lead time for changes
  • Deployment frequency
  • PR review turnaround
  • Escaped-defect rate
  • Feature throughput per engineer

Goals we agree with you and measure from a baseline. They are what we track, not results we promise.

Related services

The services behind this playbook

Our work in this space

What we've built for growing businesses

Sites and products we've shipped for businesses in this sector. They show how we work; they are not deployments of this playbook.
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    Live

    LuxeLens AI Studio

    Our generative product-photo and video studio · Gemini on Google Cloud

    • Generative AI
    • Gemini
    • Google Cloud
  • Greenberg Engineering home page: “Create the future with us” beside the Greenberg monogram on black
    Live

    Greenberg Engineering

    Corporate platform on Next.js · Engineering and infrastructure

    • Next.js
    • Corporate platform
    • Engineering

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Pip · Softpiper's AI

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Ask Pip how this would work for you

Pip is Softpiper's AI. Tell it what you run today and it will walk through how this playbook fits, what we would build first and a tailored price range. Or book a call with the team.

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