Backend infrastructure for TrustSeal and ScamCheck: Firestore (rate limiting, user data, check history), Firebase Auth (user accounts and session management), and Firebase Functions v2 (serverless AI + payment endpoints). Both products share a Firebase project. Three documented failure patterns: default Node 18 runtime crashes on production invocation (fix: explicit nodejs22 in firebase.json), custom domain omission from Authorized Domains causes silent session loss on refresh, and Gemini API 429 rate limits require structured UX handling in the Cloud Function layer.
Operational records — 31 total
Modular, dependency-free engine that turns a single scam input into a full bilingual content bundle: article, SEO metadata, GEO summary, social copy for five platforms, Shorts/Reels script, FAQ + Article JSON-LD schema, auto internal links, and a per-channel publishing queue. Provider-agnostic AI over REST, Firebase-compatible store adapter, caching, rate limits, and audit logging.
Scalable pipeline that ingests public scam reports and turns them into deduplicated, classified, severity-scored intelligence: rule+AI classification across 12 scam types, PII redaction, spam/abuse pre-filtering, Gemini moderation, embedding-based semantic deduplication and clustering, vector search, trending dashboard, regional heatmap, admin moderation queue, and a public alert feed. Includes Firestore collections, indexing strategy, prompt structure, and moderation flow.
How AI Execution Lab uses Vertex AI Gemini 2.5 (Flash + Pro) exclusively: dependency-free service-account auth, model-tier routing with automatic fallback, graceful rate-limit handling, token usage + cost tracking, Vertex quota monitoring, batched multilingual embeddings, and a deterministic mock fallback. Includes the exact env vars, IAM roles, and a go-live checklist.
Operational cost governance doctrine for TrustSeal and ScamCheck. Documents where costs originate, concrete free-tier economics, the 7 cost invariants that prevent runaway resource consumption, scaling thresholds with upgrade triggers, abuse containment strategy, and silent cost escalation vectors. All figures derived from real architecture — Gemini 1.5-flash free tier, Firebase Spark plan, Razorpay transaction fees.
Platform-specific deployment verification checklists for Vercel (AI Execution Lab), Firebase (TrustSeal and ScamCheck Cloud Functions), GitHub Pages (TrustSeal and ScamCheck SPAs), and WordPress (asquaresolution.com). A deploy is not safe until every item on the relevant checklist has been confirmed in production — not in the emulator, not locally, not from build logs.
Recovery invariants, incident classification, blast radius model, and recovery posture for the A Square Solutions ecosystem. Extracted from real production incidents across TrustSeal, ScamCheck, AI Execution Lab, and WordPress. Answers the question: when production behavior diverges from expected state, how do we restore safe operation predictably and without making the incident worse?
The 20 operational invariants governing the A Square Solutions ecosystem, extracted from real production failures and operational history. Each invariant is a condition that must remain true for the system to behave safely and predictably — an explicit reliability contract derived from TrustSeal, ScamCheck, AI Execution Lab, and WordPress production experience.
Orientation for new operators, contributors, and AI sessions entering the A Square Solutions ecosystem. Covers the three-product architecture, platform independence model, doctrine navigation map, safe contribution zones, the ten most operationally critical facts, and a glossary of platform-specific behaviors. Start here before making any production changes.
Security invariants, credential governance, trust boundary model, and access discipline for the A Square Solutions ecosystem. Documents the three-tier access architecture across TrustSeal and ScamCheck, all credentials and where they are allowed, the security implications of historical operational failures, silent security drift scenarios, and lightweight security observability patterns. Grounded entirely in real production architecture.
How operators make sound decisions during deployments, failures, recovery, and production uncertainty. Ten operator invariants extracted from real incidents where assumption, pressure, and incomplete verification made incidents worse or masked them for weeks. Answers the question: how do humans avoid making production incidents worse under pressure?
Detection invariants, signal taxonomy, and monitoring doctrine for the A Square Solutions ecosystem. Extracted from real production failure history across TrustSeal, ScamCheck, AI Execution Lab, and WordPress. Documents how 15 historical failures were detected, what signals were missing, and what detection rules prevent the same classes from being discovered by user reports instead of operators.
How changes move safely from intent to stable production operation. Change classification framework, blast radius evaluation, preflight discipline, staging philosophy, and change-management invariants extracted from real deployment history across TrustSeal, ScamCheck, AI Execution Lab, and WordPress. Answers: how do we reduce the probability that a production change introduces unexpected operational behavior?
Step-by-step detection procedures for every production system in the A Square Solutions ecosystem. Covers TrustSeal, ScamCheck, AI Execution Lab, and WordPress. For each system: what healthy looks like, what each failure mode looks like, and what to check first when something is wrong.
Lightweight, system-specific recovery procedures for every documented failure class across the A Square Solutions ecosystem. For each failure: the minimum recovery action, the correct recovery sequence, how to confirm the system is restored, and what residual risk remains. Companion to the Incident Detection Playbook.
Production pattern for per-user quota tracking, monthly reset logic, atomic increment, pre-AI-call enforcement, and abuse prevention using Firestore. Implemented in TrustSeal (10 free checks/month, premium tier) and ScamCheck (unlimited free after sign-up). Covers the data model, the enforcement code, the reset mechanism, and the cost protection logic that prevents free-tier Gemini quota from being exhausted by a single user.
Operational reference for running Gemini AI in production via Firebase Cloud Functions. Covers: structured output enforcement, JSON parse failure handling, 429 rate limit UX design, server-side key isolation, cold start latency mitigation, Node runtime requirements, and the three-part prompt architecture that produces reliable structured output across calls.
Production implementation reference for Razorpay subscription payments with Firebase Cloud Functions and Firestore. Covers the full flow: subscription creation, checkout modal, webhook verification, Firestore state synchronization, realtime client unlock via onSnapshot, idempotency, and failure modes. Built and verified in production on TrustSeal.
Operational pattern for managing test vs. live mode separation across payment processors, analytics platforms, and authentication providers. Covers the full failure surface: mode-mixed credentials, preview environment contamination, domain authorization gaps, and the unifying root cause — credentials or configuration valid in one scope that are absent, wrong, or mismatched in production.
Firebase Cloud Functions returned 403 errors with missing auth context for 12 minutes after a redeploy that included a Firestore rules update. Root cause: Functions were deployed before Rules, creating a window where new function code ran against stale IAM/rules state. Fix: always deploy Firestore rules before Cloud Functions when both change in the same release.
Complete operational provenance for ScamCheck (scamcheck.asquaresolution.com) from concept through production. Build phases, infrastructure changes, Gemini rate limit incident, auth configuration, CSS architecture decisions, and deployment milestones — consolidated as a queryable operational record.
Complete operational provenance for TrustSeal (trustseal.asquaresolution.com) from concept through production. Build phases, infrastructure changes, auth incidents, payment integration, deployment milestones, and failure resolutions — consolidated as a queryable operational record.
Monthly operational state of the TrustSeal trust badge platform: deployment health, Firebase usage, Razorpay integration status, and current priorities.
Architecture and build record for TrustSeal (trustseal.asquaresolution.com) — an AI-powered Trust Intelligence Platform for domain and business verification. React/Vite/Firebase/Gemini/Razorpay on GitHub Pages.
Architecture and build record for ScamCheck (scamcheck.asquaresolution.com) — an AI-powered scam detection tool. React/Vite/Firebase/Gemini on GitHub Pages with plain CSS.
TrustSeal (trustseal.asquaresolution.com) — AI-powered website trust verification tool. React/Vite/GitHub Pages frontend, Firebase Auth + Firestore backend, Firebase Functions v2 for Gemini AI analysis and Razorpay webhook handling. Subscription-based monetization via Razorpay (INR). Node 22 runtime required.
Firebase Auth login succeeded but session was lost on every page refresh after moving to a custom domain. Root cause: the custom domain was not added to Firebase Console's Authorized Domains list for the Authentication project. Session cookies and token refresh calls require the domain to be explicitly authorized.
ScamCheck (scamcheck.asquaresolution.com) — AI-powered scam detection tool. React/Vite/GitHub Pages frontend, Firebase Auth + Firestore backend, Firebase Functions v2 for Gemini AI scam analysis. Plain CSS (no Tailwind — justified at this UI scope). Free-tier AI tool with no payment layer. Node 22 runtime required.
Razorpay checkout modal opened and payment appeared to complete, but the webhook was never fired and the subscription wasn't activated. Root cause: client-side key was in test mode (rzp_test_) while the server-side Cloud Function key was in live mode (rzp_live_), or vice versa. Both keys must match modes simultaneously.
ScamCheck's Gemini scam detection Cloud Function hit the free tier rate limit (429 Too Many Requests) during rapid testing. The client had no handling for the 429 case and showed an indefinite spinning loader. Root cause: the Cloud Function did not return a structured error response for 429, and the client had no branch for anything other than success. Fix: return { rateLimited: true } from the Cloud Function on 429, detect it client-side, and render a specific message.
Firebase Cloud Functions deployed and appeared active in the console but crashed on every invocation in production. Cold start succeeded but function execution failed with unhandled promise rejections and module resolution errors not present in local development. Root cause: default Node runtime version (Node 18) had known incompatibilities with the npm packages used. Migrating to Node 22 runtime resolved production crashes.