HomePortfolio Stabilizing an AI-Built SaaS Platform for Production Deployment

Stabilizing an AI-Built SaaS Platform for Production Deployment

AI Recovery
AWS
MySQL
React
Information technology
Supply chain
Python

A US-based SaaS company partnered with Altoros to rescue their AI-coded project management platform that worked in demos but failed in production. Through a structured diagnosis and 10-day remediation, the team resolved critical architecture and security gaps, achieving 99.5% uptime and enabling the company to onboard its first enterprise customers.

Stabilizing an AI-Built SaaS Platform for Production Deployment

About the project

Brief results of the AI Recovery engagement:

  • A 3-day diagnostic audit identified 23 critical issues across architecture, security, and integration layers—resulting in a Yellow verdict with a clear remediation roadmap.
  • The 10-day stabilization sprint replaced fragile AI-generated patterns with industry-standard solutions, reducing production incidents by 85%.
  • Proper secrets management, audit logging, and authentication hardening addressed security vulnerabilities that had blocked enterprise sales.
  • CI/CD pipeline implementation and monitoring setup reduced deployment time from 4 hours of manual work to 12 minutes of automated delivery.

The customer

Based in Austin, Texas, the customer is a Series A SaaS startup offering a project management and resource planning platform for professional services firms. Founded in 2023, the company built its MVP rapidly using AI coding assistants (GitHub Copilot and ChatGPT) to accelerate time-to-market. The approach worked—they secured seed funding and signed several pilot customers within months.

The need

The AI-accelerated development allowed the startup to move fast, but the codebase accumulated significant technical debt. When the company attempted to scale beyond pilot customers, problems emerged:

  • The application crashed under load—what worked for 50 users failed spectacularly at 200.
  • Enterprise prospects required SOC 2 compliance, but a security review revealed hardcoded API keys, missing audit logs, and inconsistent authentication flows.
  • Integration with third-party tools (Slack, calendar systems, invoicing) broke unpredictably, requiring daily manual intervention.
  • Deployments were a high-risk, multi-hour manual process—the team avoided releasing updates out of fear of breaking production.

With enterprise deals stalling and investor pressure mounting, the company needed to get their product production-ready without starting over. They turned to Altoros for an AI Recovery engagement.

The challenges

The diagnostic audit revealed issues characteristic of AI-assisted codebases built without architectural oversight:

  • Inconsistent patterns throughout the codebase—AI-generated code solved similar problems in different ways across modules, making maintenance unpredictable and onboarding new developers nearly impossible.
  • Missing error handling and retry logic—integrations with external services had no circuit breakers or graceful degradation, causing cascading failures.
  • Security gaps, including secrets committed to the repository, overly permissive CORS policies, and SQL queries vulnerable to injection.
  • No observability—the team had no visibility into system health, relying on customer complaints to discover outages.
  • Environment drift—development, staging, and production configurations diverged significantly, making it impossible to reproduce bugs locally.

The solution

Stage 1: Diagnostic Audit (3 days)

The Altoros team conducted a comprehensive assessment covering architecture, security, and business analysis. The audit documented system topology, identified single points of failure, and mapped all integration points. A dependency scan revealed 12 known vulnerabilities in third-party packages. The output was a Yellow verdict—structural problems requiring stabilization, but not a complete rebuild.

Stage 2b: Yellow Remediation Sprint (10 days)

Based on the prioritized remediation roadmap, the team executed fixes in parallel workstreams:

Architecture: Introduced service boundary design to decouple tightly coupled modules. Implemented retry and circuit-breaker patterns for all external integrations. Replaced inconsistent AI-generated data access patterns with a standardized repository layer.

Security: Migrated all secrets to environment variables and a vault service. Implemented proper authentication middleware with consistent session handling. Added audit logging for compliance requirements and hardened API endpoints with rate limiting and input validation.

Development: Fixed critical bugs, including a race condition in the billing module. Standardized error handling across all API routes. Added automated test coverage for the critical user paths (signup, project creation, invoicing).

DevOps: Established environment parity across dev, staging, and production. Built a CI/CD pipeline with automated testing gates. Deployed monitoring and alerting using industry-standard observability tools. Conducted load testing to verify the system could handle 500+ concurrent users.

The outcome

Within two weeks of completing the engagement, the platform achieved 99.5% uptime—up from approximately 94% during the troubled period. Production incidents dropped from an average of 8 per week to fewer than 2. The security improvements unblocked the enterprise sales pipeline, with the company closing its first two enterprise contracts within 45 days.

The automated deployment pipeline transformed release management—the team now ships updates 3–4 times per week instead of dreading monthly releases. New developer onboarding time decreased from 3 weeks to 5 days, thanks to standardized code patterns and proper documentation.

Most importantly, the engagement preserved the business logic and features the company had built. The AI-assisted development got them to market quickly; the AI Recovery engagement made that investment production-ready.

Technology stack

Platforms

AWS (EC2, RDS, S3), Vercel

Programming languages

TypeScript, Python

Frameworks and tools

React, Next.js, Node.js, Express, Prisma ORM

Databases

PostgreSQL, Redis

Development Environment

GitHub Actions, Docker, Terraform, Datadog, PagerDuty

Security

AWS Secrets Manager, Auth0, Snyk

Testing

Jest, Playwright, k6 (load testing)

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Alex Tsimashenka

Alex Tsimashenka

Business Development Director

a.tsimashenka@altoros.com +1 (650) 419-3379