Direct answers about AI-built app rescue, production deployment, App Store submission, LangChain, RAG, and automation sprints.
Start by checking exposed API keys, missing server-side auth, open database rules, unsafe CORS, unvalidated inputs, and missing rate limits. A production rescue pass moves secrets into environment variables, hardens auth middleware, fixes database access, adds logging, and redeploys the app with monitoring.
Yes. The rescue process is built for AI-generated and AI-assisted apps. I audit the code, identify launch blockers, fix critical production issues, and document what changed so the product can be maintained after the sprint.
Most AI-built apps are optimized for a working demo, not production operations. Common gaps include weak authentication, exposed secrets, missing error handling, incomplete deployment configuration, poor database rules, no observability, and untested edge cases.
Review the generated architecture, move secrets out of client code, add server-side permission checks, configure production hosting, set up database rules, add error monitoring, test critical flows, and document rollback and handover steps.
Apple usually rejects apps for incomplete functionality, missing privacy information, duplicate or spam-like submissions, broken sign-in flows, wrong entitlements, or poor metadata. Each rejection includes a guideline reference that maps to a specific fix before resubmission.
Yes. I can prepare certificates, provisioning profiles, EAS builds, TestFlight, Play Console tracks, screenshots, metadata, privacy details, and release submissions for iOS, Android, or both.
RAG means retrieval-augmented generation. A business needs RAG when AI answers must use private or changing knowledge such as help docs, SOPs, policies, product documentation, or internal knowledge bases. If the answer should be deterministic, a form or rules engine may be better.
Yes. A production RAG sprint includes source cleanup, chunking, metadata, retrieval, prompt architecture, citations, evaluation examples, tracing, and monitoring so answers stay grounded in approved business knowledge.
Yes. I use LangChain for model, retriever, tool, and prompt components, and LangGraph-style orchestration when the workflow needs state, branching, durable execution, or human review.
AI can help classify requests, extract structured fields, draft client updates, summarize tickets, detect exceptions, and route work. Risky actions should still use deterministic rules, approval steps, and audit logs.
Yes. Platform setup can include custom domains, lead capture, CRM fields, funnels, automations, analytics, welcome emails, and handover documentation so the founder can operate the system safely.
Yes. I deploy Next.js apps to Vercel, Render, Railway, Fly.io, or other suitable hosts with domains, SSL, redirects, environment variables, monitoring, and production handover notes.
The most useful context is the repository, product URL, deploy preview, known blockers, target launch date, current stack, database provider, hosting provider, and any security or app-store rejection messages.
Yes. I can work inside the current repository, review existing work, leave implementation notes, coordinate with freelancers, and hand over the system clearly after the sprint.
Simple launch and deployment fixes can often be completed in days. Security rescue, app-store submission, RAG, or workflow automation usually depends on the current code quality, access, and scope.
Use the contact page or any service page calendar. Share the product link, repo context, app-store blocker, or launch checklist so the call can focus on the fastest path to production.