ERR_01 // RUNTIME
Works Locally, Fails in Production
Environment configuration drift, untracked serverless cold starts, unpinned Node engine runtimes, and local mock assumptions failing in stateless edge regions.
Impact: Deployment 500sHigh Severity
SPECIALIZED INFRASTRUCTURE REMEDIATION
Your AI-Built App Works in Preview. Let's Make It Production-Ready.
Full-stack production engineering, eight-plus years in: Next.js, React, Node.js, Python, and .NET. Relational data on PostgreSQL and MySQL, documents on MongoDB. AI automation, agents, and RAG for fintech, health, education, and real estate.
Zero-Credential Disclosure3-5 Day Audit SLAPull Request Ready Fixes
sys-arch-diff // v0-cursor-spec
LIVE DIFF ENGINE
PREVIEW: VULNERABLEUNSECURED
x SUPABASE_SERVICE_ROLE_KEY leak in bundle
x RLS Disabled on public.user_profiles
x Next.js SSR cascade: 18 N+1 requests
x Unbounded JSON parsing > OOM risk
x Rate-limit: Missing (Open to LLM drain)
p99 Latency:2,840ms
PROD: HARDENEDVERIFIED
✓ Strict RLS + auth.uid() scoping
✓ Edge-cached RPC query aggregations
✓ Ephemeral tokens via Supabase Vault
✓ Upstash sliding-window rate limit
✓ Streaming payload validation (Zod)
p99 Latency:142ms
PostgreSQL Pool: PgBouncer HealthyZero-Leak Guard: ACTIVE
SYSTEMIC FAILURE MODES
The 6 Breakpoints of AI-Generated Apps
Generative coding tools (Cursor, v0, Lovable, Claude) create astonishing prototypes that quietly disintegrate under actual users and real production workloads.
ROOT-CAUSE DIAGNOSTICS
ERR_02 // DATABASE EXPOSURE
Database Exposes Sensitive Rows
Supabase Row-Level Security (RLS) policies completely bypassed, service-role keys bundled into client Next.js bundles, or permissive public table read rights.
Impact: Critical Data LeakCatastrophic
ERR_03 // AUTH & RBAC
Unreliable Auth & Permissions
Flaky session hydration during client-to-server transitions, unauthorized route navigation via manipulated JWT state, and missing organization multi-tenancy checks.
Impact: Privilege EscalationHigh Severity
ERR_04 // EDGE & APIS
APIs Fail Under Real Load
Missing distributed rate-limiting, unhandled upstream LLM timeouts, deadlocks during simultaneous write operations, and lack of idempotency on payment webhooks.
Impact: System Stalls & Double ChargesModerate
ERR_05 // PERFORMANCE
Severe Performance Degradation
Sequential waterfall fetch calls in nested React Server Components, zero index optimization on query filters, and gigantic uncompressed npm dependency trees.
Impact: 5s+ TTFB LatencyHigh Degradation
ERR_06 // CI/CD & DEPLOY
Broken Deployment Pipeline
Docker build crashes during multi-stage compiles, brittle Vercel build-step script errors, wild CORS permission blocks, and failed automated schema migrations.
Impact: Stuck Production DeploysCritical Blocker
PRECISION CONSULTING
Engineered Services to Harden Your Application
From deep diagnostic code reviews to fully managed hands-on remediation and dedicated scale advisory retainers.
FIXED SCOPE3-5 DAYS
Comprehensive Production Audit
A comprehensive static & runtime vulnerability scan of your AI codebase, architecture, and database permissions.
Key Deliverables:
- Full Supabase RLS security penetration report
- Next.js bundle bloat & waterfall trace analysis
- Environment variable exposure audit
- Prioritized action roadmap with remediation code
MOST POPULAR FOR LAUNCHES
HANDS-ON CODE REMEDIATION1-2 WEEKS
Production Rescue & Hardening
We join your repository and directly rewrite, fix, refactor, and harden the codebase to make it stable before commercial release.
Key Deliverables:
- Rewritten RLS policies with automated test suites
- Database indexing & connection pool configuration
- Edge rate limiting & idempotent webhooks implementation
- Direct GitHub Pull Requests merged with verification
DEDICATED PRINCIPAL PARTNERMONTHLY
Scalability Retainer
On-demand Staff/Principal engineer backing your team as you scale past initial traction to tens of thousands of active users.
Key Deliverables:
- Weekly PR and architectural schema reviews
- Dedicated Slack / Discord engineering war room
- Load testing simulation & auto-scaling tuning
- Zero-downtime database migration leadership
Full-stack, expert level
The stack we ship and harden
JavaScript is the product surface. Python and .NET carry APIs, workers, and the systems enterprises already run. Relational data lives in PostgreSQL or MySQL. Documents live in MongoDB. AI is a pipeline with tools, citations, and an access check, not a chat box on the homepage.
Next.jsApp RouterRead moreReactServer and clientRead moreNode.jsAPIs and workersRead moreNestJSNode frameworkRead moreTypeScriptStrict typesRead morePythonWorkers and AIRead more.NETASP.NET CoreRead moreStrapiCMS or customRead morePostgreSQLRelationalRead moreMySQLRelationalRead moreMongoDBDocumentRead moreSupabaseAuth and dataRead more
AI automation
Repeatable jobs that used to sit in inboxes and spreadsheets: intake, document extraction, CRM updates, approvals, and scheduled follow-up.
- A workflow with a named owner, a queue, and a retry policy. Not a prompt pasted into a button.
- Python or Node workers behind the product, with webhooks in and a human step where a wrong action costs money.
- Logs of what the automation read, what it wrote, and what it refused.
Agentic AI
Agents that plan, call tools, and stop. Built for a job inside the product, with a budget and an audit trail.
- Tool contracts: the agent can search, draft, or create a ticket. It cannot move money or delete records unless that tool exists and is gated.
- Memory that is scoped to the tenant and the task. No shared scratchpad across customers.
- Evals before launch: a fixed set of tasks, a cost cap per run, and a trace you can read when it fails.
RAG pipelines
Answers grounded in your documents, with the passage cited and the wrong tenant’s files unreachable.
- Ingest, chunk, embed, and re-index when the source changes. Stale policy docs are a product bug.
- Hybrid retrieval and a reranker, then a citation. If the corpus does not contain the answer, the system says so.
- Access control at retrieval time, not only in the chat UI. One school, clinic, fund, or brokerage cannot retrieve another’s files.
Industries
Fintech, health, education, real estate
The same engineering, pointed at the workflow that industry actually pays for.
Fintech
Onboarding, payments, ledgers, and the access rules around customer money. Built so a webhook can be delivered twice and the balance still moves once.
- KYC and KYB onboarding that fails closed
- Idempotent payment webhooks and reconciliation
- Tenant isolation and an audit log on sensitive actions
- Lending, payout, and billing workflows with a clear state machine
Health
Intake, scheduling, clinician tools, and patient access. Designed around minimum necessary access. This site does not claim a HIPAA certification.
- Patient intake and appointment scheduling
- Clinician and patient views with separate permissions
- Document and result delivery with an access log
- Integrations shaped for the systems clinics already run
Education
Enrollment, course delivery, assessments, and an assistant that can only answer from the school’s own material.
- Enrollment, cohorts, and payments
- Lessons, assignments, and progress a teacher can trust
- Separate roles for student, parent, instructor, and admin
- Curriculum RAG so the tutor cites the course, not the open web
Real estate
Listing content, portal leads, AI calls, and a score that tells the agent who is worth a callback.
- Strapi for listings, area pages, and agent profiles
- Leads from Property Finder, Bayut, Zillow, and Bitrix24 on one record
- User tracking: listing, portal, campaign, and pages viewed
- AI call with a score: important or low
METHODICAL PROTOCOL
How an Engagement Works
A structured, auditable six-step engineering workflow built for founders who value transparent execution without bureaucratic overhead.
STEP 01INITIATION
Submit Application Details
Provide repository scope, current architectural pains, hosting setup, and symptoms (e.g., sporadic 500s or high DB CPU).
Standard bilateral NDA provided instantly
STEP 02TRIAGE
Technical Requirements Review
A Principal Architect completes a preliminary scan of your repository structure under read-only access within 24 hours.
Turnaround: < 24h
STEP 03SCOPING
Audit or Project Proposal
You receive an itemized proposal specifying vulnerabilities, performance bottlenecks, and exact scope of remediation.
Granular deliverable checklist
STEP 04AGREEMENT
Scope & Pricing Signoff
Simple fixed-fee commitment. No surprise hourly billings or creeping timelines. Work schedule is locked.
100% Fixed-fee guarantee
STEP 05EXECUTION
Implement & Test Fixes
We harden RLS, patch security flaws, optimize SQL queries, and deliver cleanly documented, atomic Pull Requests.
Staging environment validated
STEP 06HANDOVER
Final Verification Report
Live walkthrough of system metrics, benchmark proof, test coverage verification, and architectural documentation handover.
Production Readiness Certification
EMPIRICAL PROOF
Verified Engineering Post-Mortems
Real before-and-after case studies from production audits and emergency interventions.
POST-MORTEM #041 // Fintech B2B SaaSRESCUE COMPLETED
Stack:Next.js, Node.js, Python, PostgreSQL
Original latency2,800ms (p95)
Identified Failures:
The Next.js app, the Node.js webhook worker, and a Python reconciliation job all used a service-role credential. Twelve billing tables were readable without a tenant check, and the dashboard ran about 40 queries per page.
Engineering Remediation:
Row policies now key off membership. The dashboard reads one Postgres function instead of a query waterfall. Stripe events and the reconciliation job both post once. Pooling stopped serverless invocations from exhausting connections.
Latency2.8s → 180ms
RLS leaks0
Ledger replaysNo double post
POST-MORTEM #078 // AI analytics platformRESCUE COMPLETED
Stack:Next.js, Node.js, Python, PostgreSQL
Original stateDaily serverless 504 timeouts
Identified Failures:
The agent retrieved chunks and called the model inside the HTTP request. Long runs were killed at the function timeout, the stream died, and retrieval was not limited to the caller’s documents.
Engineering Remediation:
Node.js accepts the run and returns immediately. A Python worker retrieves with the tenant id in the query, reranks, and stores citations. The client resumes from the last acknowledged chunk instead of marking a dead stream complete.
Stream deliveryRuns no longer truncate
Tenant retrievalScoped in the query
504s0 on the request path
POST-MORTEM #112 // Health intakeRESCUE COMPLETED
Stack:.NET, Next.js, PostgreSQL
Original stateOne login for patient and clinician
Identified Failures:
A Next.js front end called a .NET API that trusted the role sent by the browser. Patient and clinician screens were the same session with fields hidden in the client. Appointment reminders included clinical detail.
Engineering Remediation:
ASP.NET Core policies split patient, clinician, and staff. Opening a document writes who did it. The Next.js portals only render what that policy already returned.
Cross-role readsRejected
Access logWritten on open
Reminder bodyNo clinical detail
POST-MORTEM #126 // Real estate leadsRESCUE COMPLETED
Stack:Next.js, Strapi, Python, Node.js, PostgreSQL
Original stateLeads with no source
Identified Failures:
Listing inquiries from the site, ads, Property Finder, Bayut, and Zillow landed in inboxes and in Bitrix24 as separate people. The same phone number opened two pipelines. Nobody recorded which listing the caller had viewed, and every new lead was treated as urgent.
Engineering Remediation:
Strapi holds the listing, the area guide, and the agent. Next.js publishes that content. Inbound webhooks from Property Finder, Bayut, Dubizzle, Zillow, Realtor.com, and Bitrix24 store source and listing, and Python merges on phone before a new pipeline opens. The session log is attached before the AI call. The call writes an important or low score onto the lead. The drip sends from a queue with an unsubscribe, quiet hours, and a stop when status is under contract.
SourcePortal, campaign, and listing
Call scoreImportant or low
TrackingListing and pages viewed
CMSStrapi
CRMBitrix24, one contact
DuplicatesMerged on phone
FREQUENTLY ASKED QUESTIONS
Built Specifically for Founders Using AI Tools
Everything you need to know about access credentials, timeline guarantees, and working with modern AI-generated codebases.
My app was built with Cursor / v0 / Lovable. Can you still help?
Yes. Most of the codebases we review were built with Cursor, v0, Lovable, Claude, or Replit. Those tools write functional UI quickly. They do not design multi-tenant isolation, row-level security, or database locking under concurrency.
What access do you need to audit my application?
Read-only repository access via GitHub or GitLab. We do not ask for production master database passwords, live payment keys, or customer records. Live checks happen in preview or staging.
How fast is the turnaround time?
A Comprehensive Production Audit is delivered in 3 to 5 business days. Production Rescue & Hardening typically takes 1 to 2 weeks, depending on schema complexity and table count.
Do you rewrite our entire application from scratch?
No. A rewrite throws away the time you already spent getting to market. We keep the existing UI and business logic, and fix the foundation: database policies, API timeouts, auth sessions, and caching.
Do you provide an NDA before code review?
Yes. A mutual NDA with IP and confidentiality terms is signed before repository access is granted.
What happens after you finish the fixes?
Rescue work includes a warranty window for regressions inside the agreed scope. Ongoing help is available through the scalability retainer.
AVAILABLE FOR IMMEDIATE AUDIT INTAKE
Don't Wait for Your First Outage to Find Out What's Broken.
Schedule your technical audit today. In 3 to 5 days, your app will be hardened against data leaks, rate-limit failures, and scaling bottlenecks.