You took an AI prototype further than most software projects ever get: it runs, people use it, the idea is proven. We handle what comes next — the security, data integrity, infrastructure, and engineering discipline that keep it working when real customers, payments, and sensitive data arrive.
Base44 · Lovable · Claude Code · Replit · Bolt · Cursor · v0 · custom AI-generated codebases
AI builders are genuinely good at what they do: they compress months of product discovery into days. If you shipped a working prototype with Lovable, Base44, Bolt, or Claude Code, you did the hardest part of any software project — you found out whether anyone wants the thing.
But a demo exercises the happy path, with friendly users, on small data. Production is the opposite of all three. The failure patterns we find in AI-generated apps are consistent: authentication that checks the UI but not the API, tenant data separated by convention rather than enforcement, database permissions broad enough that any bug becomes a breach, payments that work until the first refund or webhook retry, and no tests — so every change risks breaking something unrelated, and nobody can say what.
None of that means the prototype was a mistake. It means the prototype did its job, and the job is now different: the codebase needs an owner who can read all of it, reason about what happens under failure, and answer the questions a technical investor, enterprise buyer, or compliance review will ask.
Whether the structure can absorb the next year of features. AI-generated codebases accumulate duplicated logic and contradictory patterns across hundreds of prompts — we map what's coherent, what's fragile, and what will fight every future change.
Authentication, authorization, tenant isolation, secret handling, and exposed surface area. This is where the critical findings live: API routes that trust the client, service keys in front-end bundles, row-level security that was never turned on.
Whether the schema enforces the rules the business depends on — constraints, transactions, migrations, and a model that can support the requirements you already know are coming. Data corruption is the failure you can't roll back.
Staging environments, repeatable deploys, CI, secret management, backups you have actually restored from, and a rollback story. "Push to the builder and hope" is a prototype workflow, not an operating procedure.
How the app behaves on real data volumes: query plans, N+1 patterns, missing indexes, unbounded lists, background work running inside request handlers. Demos are fast because demo databases are small.
Automated tests around money, auth, and data writes; error tracking; structured logs; alerts that reach a human. Without these, your customers are the monitoring system.
Webhook verification, retries, idempotency, and failure handling for Stripe, email, uploads, and every third-party API the app leans on. Integrations fail routinely in production; the question is whether the app notices.
This is not a linting report or an AI-generated summary of your repo. Senior engineers read the code, inspect the infrastructure, and exercise the app the way production will. You receive:
An architecture and codebase review; security and access-control findings with severity ratings; a database and data-integrity assessment; an infrastructure and deployment review; performance and scalability risks; testing and observability gaps; and a review of every third-party integration, including payments.
For each major area of the application we state whether to keep it as is, refactor it in place, replace it with a proven component, or rebuild it — with the reasoning. We do not default to "rebuild everything." That recommendation has to earn itself against the codebase, the product's maturity, the risk profile, and the scale you actually expect.
Every finding lands in one of three buckets: critical (fix before anything else — active security or data-loss exposure), launch-blocking (fix before real customers depend on the app), and post-launch (schedule after go-live). Each bucket comes with an implementation estimate, so the roadmap doubles as a budget.
The report is written to be handed to other people — a technical co-founder, an investor doing diligence, an enterprise customer's security team. It's yours either way: act on it with us, with your own hires, or with another firm.
Fixed-fee, typically two weeks. The full assessment above, delivered as a written report with a prioritized, priced remediation plan. The right entry point when you need to know where you stand — before a launch, a raise, a pilot, or an enterprise security review.
We fix the critical and launch-blocking findings: lock down auth and tenant isolation, repair the data layer, stand up real deployment infrastructure, put tests around the code that touches money and data. The product keeps running while we work. The right path when the prototype is fundamentally sound but not yet safe.
We take responsibility for the codebase: remediation plus the product roadmap, delivered as fixed-scope build sprints or an embedded engineering retainer. The right path when the app is becoming the business and you want a senior team accountable for it — without hiring one.
Which path fits depends on what the audit finds, not on what we'd prefer to sell. Plenty of audits end with "stabilize these six things and launch" — and some end with "this layer will not hold; here is the cheapest safe way to replace it."
Base44's built-in backend and hosting are why the prototype shipped fast. Teams reach out when the app needs custom integrations, infrastructure they control, or answers for a security review that a fully managed platform can't provide — usually a planned migration to a stack you own, keeping the validated product behavior.
Lovable exports real React and TypeScript, typically on Supabase. The common inflection points: Supabase row-level security that was never configured correctly, auth edge cases, and a codebase that has grown past what prompting can change safely. Because the code is exportable, this is usually a stabilize-in-place job.
Claude Code produces real codebases in your own repo, and output quality tracks the direction it was given. Founders come to us for the senior review they didn't have during the build: architecture coherence across sessions, a test suite, CI, and the deployment discipline to ship changes without fear. We build with Claude daily — it's core to our own stack — so we know its habits, good and bad.
Replit collapses build and deploy into one place, which is exactly right for validation. The typical call comes when the app needs to move onto dedicated infrastructure — a production database, background jobs, environment separation — while staying live for the users it already has.
Bolt scaffolds full-stack apps remarkably fast, and fast scaffolds carry prototype defaults: permissive access rules, thin error handling, integrations wired for the demo. The work is production hardening — auth, data constraints, integration reliability — on top of a structure that's often worth keeping.
Cursor projects are ordinary repos, which makes them the most direct to take over. What they usually need is what any solo-built codebase needs before customers depend on it: an independent review, tests, CI, and a second set of senior eyes on the architecture decisions made at speed.
v0 generates strong React and Tailwind front-ends. The gap is everything behind them: real authentication, a data layer with enforced rules, APIs, and deployment. We keep the UI you validated and build the production system underneath it.
What the app does, who depends on it, what's launching when, and what "production" means for you — ten pilot users and a compliance review are different problems than ten thousand consumers.
Repo access, infrastructure access, integration inventory. We read the code, trace the data flows, and test the boundaries — the same way an attacker or an enterprise security reviewer would.
Critical findings are flagged the day we confirm them. If there's an exposed secret or an open tenant boundary, you hear about it immediately, not in the report.
Findings, per-area recommendations, and the critical / launch-blocking / post-launch roadmap with estimates. This is the audit deliverable, and the decision point for what happens next.
If you engage us for remediation: fixes land behind tests, changes deploy through staging, and the app stays live throughout. You see the work in the repo, not in a status deck.
Runbooks, architecture notes, and operational handoff — to your team, your future hires, or an ongoing retainer with us. The goal is a system someone can own, not a dependency on the people who fixed it.
Sytepoint is a software product and applied AI engineering company. The systems we build and operate are the unglamorous kind that have to work: multi-tenant operations platforms, payment and payout flows, document processing pipelines, mobile field apps, and human-in-the-loop AI workflows — with the observability, access control, and failure handling that keeps them running for years, not demos.
That matters here because productionizing an AI-built app is operations work, not greenfield work. The judgment calls — what to keep, what to replace, what breaks first under load — come from having carried production systems through real incidents. How we operate covers the substrate: deployment discipline, monitoring, runbooks, and reliability practices.
Yes. If the platform exports code (Lovable, Bolt, v0) or the codebase already lives in a repo (Claude Code, Cursor, Replit), we work directly on it. For closed platforms like Base44, we work with what the platform exposes — and if the app has outgrown it, we plan the migration so you keep the product behavior you validated while gaining control of the code and infrastructure.
In most cases, yes. The audit answers this per area: retain, refactor, replace, or rebuild. It is common for the data model and core product logic to be worth keeping while auth, deployment, and integration layers get rebuilt. A full rewrite is the conclusion only when the evidence supports it.
Not by default. AI builders produce real code, and some of it is fine. What they don't produce reliably is coherent architecture across hundreds of prompts, defense-in-depth security, or a schema designed for where the product is going. We rebuild specific layers when fixing them costs more than replacing them — a per-area decision backed by the audit, not a blanket recommendation.
We test it against the same categories every serious engineering team uses: security and access control, data integrity, infrastructure and deployment, performance under realistic load, test coverage, observability, and integration failure handling. Each area gets a finding, a severity, and a remediation cost — so "production-ready" becomes a checklist you can verify, not a feeling.
Yes. That is the targeted stabilization path: we remediate the critical and launch-blocking findings — typically security, data integrity, and deployment — and leave a documented backlog for the rest. Many teams launch on stabilized AI-generated code and address post-launch findings on a retainer. See how our retainer works.
Yes. We set up environments (staging and production), CI/CD, database migrations, secret management, backups with tested restores, and rollback procedures. If the app currently deploys only from the AI builder's hosting, we move it to infrastructure you own — usually AWS or Vercel plus a managed Postgres — without changing what users see.
Yes, and it is usually the first thing we add, because it makes every subsequent change safer. We prioritize tests around money, auth, and data: payment flows, permission boundaries, and anything that writes to the database. Broad coverage comes later; high-consequence coverage comes first.
Yes. Tenant isolation is the single most common critical finding in AI-generated apps — queries that filter by tenant in the UI but not at the data layer, or Supabase row-level security policies that are missing or too permissive. We enforce isolation at the database and API layers and verify it with tests that attempt cross-tenant access. We have run multi-tenant systems in production for years — LoadQuest is one example.
Yes. Enterprise buyers and their security reviews ask for specific evidence: access-control documentation, audit logging, data-handling policies, backup and recovery procedures, and answers to security questionnaires. The audit maps what you have against what they will ask for, and the remediation plan closes the gap. Our governance page describes the operational substrate we build on.
Yes. Some clients want the audit and stabilization only; others hand us the product roadmap. Ongoing work runs as an embedded engineering retainer or as fixed-scope build sprints, whichever fits. Either way you own the repo, the infrastructure, and the IP.
It depends on what the audit finds, which is why we scope the audit as a fixed-fee engagement first — you get the full findings and a priced remediation plan before committing to anything larger. Stabilization work is quoted from the plan. Ground-up rebuilds, when warranted, run as 12-week build sprints from $45K.
Typically two weeks from repo access to delivered report. Larger codebases or apps with many third-party integrations can take three. You get findings as we go — critical security issues are flagged the day we confirm them, not held for the final report.
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