Prompt-to-Production Sprint

Turn Your AI Prototype Into Production Software

The demo worked. Now every fix is a game of whack-a-mole. Geisel's Prompt-to-Production Sprint transforms your AI-generated prototype into secure, tested, production-ready software in just three weeks.

Schedule a Viability Review

In a 30-minute conversation, we'll determine whether your application is a fit for the Prompt-to-Production Sprint and identify the biggest launch risks.

A mallet whacks down a pop-up risk tag such as an exposed API key, and more risks like a public database and an unpatched CVE pop up somewhere else: the endless whack-a-mole of hardening AI-built code
// Whack one risk, two more pop up. AI-generated code turns every change into whack-a-mole.

01 The production gap

The Demo Worked. The Launch Is Where Things Get Risky.

AI tools got you from idea to a working demo in days. But generated code isn't production-ready. It hides secrets in the repo, skips testing, pulls in unvetted dependencies, and creates an architecture that won't survive real product development. Before long, the codebase starts fighting you. Every fix uncovers another issue, confidence drops, and the launch date keeps moving.

What "it works" is hiding

  • × Hardcoded secrets
  • × Untested critical workflows
  • × Dependency vulnerabilities
  • × Fragile architecture
  • × Missing CI/CD
  • × Technical debt nobody understands
The Demo Wins Buy-In day 0
A Launch Date Gets Set commitment
Hidden Issues Surface reality
Every Fix Breaks Something whack-a-mole
The Launch Date Slips failure

// You proved the idea works. Now prove it won't break.

02 The failure modes

Why AI-Built Applications Break in Production

Security Risks

Exposed credentials, vulnerable dependencies, weak authentication, and missing safeguards create launch risk.

No Testing Foundation

Changes become dangerous when no automated tests exist to catch regressions.

Architecture Debt

What worked for a demo often collapses under new features, user growth, and operational demands.

Ownership Risk

Teams inherit code they didn't write and cannot confidently maintain or extend.

03 Inside the sprint

One AI-Built Application. Secured, Tested, and Production-Ready.

In just 3 weeks, Geisel engineers take one AI-generated application from a working demo to a secure, tested, production-ready baseline. Every week has a defined focus and concrete deliverables, so you always know exactly where the work stands and what comes next.

Start AI-Built Demo Proven concept, not production-ready
Week 1

Assess & Prioritize

  • Code review
  • Security review
  • Dependency audit
  • Architecture assessment
  • Risk register
Week 2

Harden & Stabilize

  • Security fixes
  • Secrets management
  • Error handling
  • Dependency stabilization
  • Refactoring critical risks
Week 3

Test & Launch Prep

  • Automated testing
  • CI/CD setup
  • Documentation
  • Executive readout
  • Production roadmap
Finish Production Ready Secure, tested, launch-ready

04 Secure, tested, production-ready

Everything You Need to Ship With Confidence

01 / DELIVERABLE

Hardened Codebase

Critical and high-severity issues fixed by senior engineers and delivered into your own git. The foundation everything else builds on.

secrets removed deps patched auth strengthened errors handled
02

Security & Reliability Improvements

Secrets management, validation, dependency stabilization, and error handling.

03

Test Foundation

Automated testing for core workflows.

04

CI/CD Pipeline

Every change automatically validated before deployment.

05

Architecture & Risk Readout

What was wrong, what was fixed, and what remains, ranked by severity and written to be read by a non-engineer.

06

Path-to-Production Roadmap

Phased next steps to launch and scale, on your timeline.

05 Engineering judgment AI can't generate

AI Can Generate Code. Senior Engineers Decide What Can Ship.

The hard part isn't generating code. It's engineering software that survives production. Geisel's engineers have built software for NASA, Teledyne FLIR, iRobot, and the world's largest autonomous mobile robot fleet, where security, reliability, and accuracy are non-negotiable. We know what breaks because we've spent years building systems that can't. That's the difference between cleaning up AI-generated code and engineering software that's ready for real users.

Where we've shipped production software

Robotics & Automation

Autonomous systems that run in the real world.

Aerospace & Defense

Flight and defense software that can't fail.

Digital Healthcare

Regulated systems, security and validation built in.

AI & Intelligent Systems

Production-grade AI and ML under real load.

06 Trusted track record

Trusted Where Software Failure Isn't an Option

NASA Raytheon Teledyne FLIR iRobot Crop One

For decades, we've helped organizations in robotics, aerospace, defense, healthcare, industrial automation, and AI bring complex software systems into production. Now we're applying that same production engineering discipline to a new challenge: turning AI-generated prototypes into software that can withstand real users, security reviews, and production environments.

15+
Years in production
5+
Regulated industries
3 wk
Demo to launch-ready
$25K
Fixed, all-in

07 Is this right for you?

Built a Working AI Application? You're Probably a Fit.

Building with Claude Code, Codex, Cursor, Windsurf, Lovable, Bolt, v0, Replit, Copilot, or any other AI coding tool? If you have a working application, you're likely a fit.

Good Fit Qualifies

  • ✓ Working application exists
  • ✓ Built using Claude Code, Codex, Cursor, Windsurf, Lovable, Bolt, v0, Replit, or Copilot
  • ✓ Launch planned within 3–6 months
  • ✓ Team needs production readiness
  • ✓ Budget and stakeholder support exist

Not a Fit Not yet

  • ✗ Idea-stage concept
  • ✗ No working codebase
  • ✗ Planned full rewrite
  • ✗ No launch timeline
  • ✗ No budget owner

08 The engagement

Don't Let a Successful Demo
Become a Failed Launch

Your prototype already proved the idea.
Now prove it can survive the real world.

Fixed-fee engagement
$25,000
Fixed Fee · No surprises
3 Weeks
One Application
One Production Target
Schedule a Viability Review

In a 30-minute conversation, we'll assess whether your application is a good fit for the Prompt-to-Production Sprint and identify the biggest risks standing between your demo and production.

09 AI & search optimized

Frequently Asked Questions

Is vibe coding production-ready?
Not on its own. Vibe coding, prompting tools like Claude Code, Cursor, Lovable, or Bolt to generate an app, is built for speed, not for security, testing, or scale. The result works in a demo but usually ships with hardcoded secrets, no tests, unvetted dependencies, and a fragile architecture. It becomes production-ready once a senior engineering team hardens it, which is exactly what the Prompt-to-Production Sprint does in three weeks.
How do you make AI-generated code production-ready?
Production readiness comes from disciplined engineering, not more prompting. We pull secrets out of the repo, patch vulnerable dependencies, strengthen authentication and input validation, add automated tests for critical workflows, and stand up CI/CD so every change is verified before it ships. You walk away with a hardened, deployable baseline and a roadmap to launch and scale.
Is code from Cursor, Claude Code, Lovable, or Bolt secure?
It can be, but it rarely is out of the box. These tools optimize for a working result, so generated apps often expose API keys, leave the database open, skip authentication, and pull in packages with known CVEs. Once senior engineers review and harden the codebase, an app built with any AI coding tool can meet real production security standards.
What are the risks of shipping a vibe-coded app?
The most common are exposed secrets, a publicly accessible database, missing or broken authentication, unpatched dependency vulnerabilities, untested critical paths, and an architecture that cracks under new features. None of these show up in a demo, but all of them surface under real users, a security review, or your next release.
Why do AI-generated apps break in production?
Because AI tools generate for a demo, not for production. There are no tests to catch regressions, the architecture buckles as users and features grow, and every fix tends to break something else. Teams end up with a codebase they cannot confidently change, and the launch date keeps slipping.
How long does it take to get an AI-built app production-ready?
Three weeks for a single application. Week one assesses and prioritizes the real risks, week two hardens and stabilizes the codebase, and week three adds testing, CI/CD, documentation, and a path-to-production roadmap. Fixed scope, fixed price, no surprises.
Is this a code audit or security review?
No. An audit hands you a report of problems; the Prompt-to-Production Sprint fixes them. We resolve the critical security and reliability issues, deliver a hardened codebase with automated tests and CI/CD, and hand you a prioritized roadmap, all committed into your own git.
Can you take my AI prototype or MVP to production?
Yes, as long as you have a working application. We take one AI-built prototype or MVP and turn it into a secure, tested, production-ready baseline you can launch from. Many teams ship directly off that baseline; others continue with Geisel for ongoing feature work or a deeper engagement.
Schedule a Viability Review