Axionic
You built fast. Axionic makes sure it holds.
About
Axionic is an AI architecture practice in Atlanta, spun out of an 18-year-old software development shop, that reviews AI-generated applications before they meet real users, real data or a funding round. The founding team are senior enterprise architects with 30 years in software architecture, including work for IHG, Ticketmaster, Disney and TAG Heuer, and several years running technology due-diligence reviews for acquisitions. The Readiness Review packages that process for a solo founder: a structured assessment of an app built in Lovable, Bolt, v0, Cursor or Claude Code, scored across security, scalability, maintainability, correctness, operability, data and compliance, and AI or agent governance. The deliverable is a severity-ranked report on a five-level rubric, a business one-pager with the top risks, a findings register with evidence pointers and remediation steps, and a remediation package a coding agent can act on directly. Quoted at $3,000 with a seven-day turnaround. Beyond the review, Axionic takes on rescue, refactor and AI integration work, including governed multi-agent systems through its Axionic Agents platform, and can audit traditionally built stacks as well.
Services
Axionic Readiness Review
Structured audit of an AI-generated codebase across seven pillars, scored on a five-level severity rubric, delivered as a report, a business one-pager and a remediation package a coding agent can act on.
Security and Data Handling Audit
Auth, secrets, data handling and compliance findings with evidence pointers and remediation steps, as part of or alongside the Readiness Review.
Rescue and Refactor
Hands-on remediation of the findings: architecture, performance, database and deployment work on the existing codebase rather than a rewrite.
Architecture Refactor
Restructuring an AI-built app so it can carry real users and real data: boundaries, data model, deployment topology.
AI Integration and Governed Agents
Production AI features and governed multi-agent systems built into an existing product through the Axionic Agents platform.
Deployment and DevOps
AWS, Azure, Google Cloud, Vercel and Netlify deployment, Terraform and GitHub Actions pipelines.
Vibe Tool Expertise
Tech Stack
Problems This Agency Can Fix
AI coding tools often generate code with exposed API keys, missing input validation, broken authentication, and insecure data handling. These vulnerabilities can lead to data breaches, unauthorized access, and compliance failures.
Authentication is one of the most common failure points in vibe-coded apps. AI tools frequently generate insecure auth flows, missing session validation, broken password resets, and improperly configured OAuth.
AI-generated database schemas often lack proper indexes, have no Row Level Security, use inefficient query patterns, and create data integrity problems. These issues worsen as your app grows.
AI-generated codebases frequently have duplicated logic, inconsistent patterns, missing error handling, no TypeScript strict mode, and poor separation of concerns. This makes maintenance and feature additions increasingly difficult.
AI tools often generate API integrations with missing error handling, no retry logic, hardcoded endpoints, and insecure credential storage. These integrations break silently and are difficult to debug.
AI-generated UIs often look great on desktop but break on mobile devices. Missing responsive breakpoints, oversized images, touch-unfriendly controls, and fixed-width layouts create poor mobile experiences.
AI-generated apps often lack proper meta tags, structured data, semantic HTML, and server-side rendering. This makes them invisible to search engines and kills organic traffic potential.
AI-generated code often works locally but fails during deployment. Common issues include missing environment variables, incorrect build configurations, incompatible dependencies, and misconfigured hosting platforms.
AI-generated applications often suffer from unoptimized database queries, excessive re-renders, large bundle sizes, and missing caching. This leads to slow page loads, poor Core Web Vitals, and frustrated users.
AI-generated apps often hit walls when traffic or data volume increases. Missing caching, unoptimized queries, no CDN configuration, and monolithic architectures prevent apps from handling real-world load.