Expert Full-Stack Development Solutions for Modern Business

We structure data, auth, and billing for teams of 1 to 10,000. The core decision is row-level security versus schema-per-tenant: RLS keeps ops simple and works for most products; schema isolation is warranted when tenants demand hard data boundaries or have strict compliance requirements.

Billing isolation follows the same pattern: each tenant's subscription state is scoped so a failed payment on one account never bleeds into another. We wire Stripe's customer-per-tenant model to your data layer from day one, not bolted on later.

Auth is handled with JWT + refresh token rotation, role hierarchies stored in Postgres, and org-scoped middleware on every API route. Resolving these decisions upfront protects your timeline. A rearchitecture mid-project to support enterprise SSO or multi-tenancy is avoidable, and we make sure it is.

Most RAG demos look great until you stress-test retrieval on a real corpus. The failure modes are predictable: chunks too large to fit context windows, embeddings trained on general text that miss domain vocabulary, and no evaluation loop to catch regressions.

We solve this with fixed-size overlapping chunks (512 tokens, 64 overlap), domain-tuned embedding models, and a hybrid BM25 + vector search retrieval step. That combination beats pure vector search on precision for technical and legal documents by 15–30% in our measured benchmarks.

Retrieval systems that pass internal testing can still fail silently on real user queries. An offline evaluation harness with a curated Q&A dataset gives you a measurable quality baseline before any change goes to production.

Auditing 20+ React apps last quarter surfaced the same four culprits across nearly every codebase: unguarded third-party scripts blocking the main thread, full-bundle imports of icon or UI libraries, missing route-level code splitting, and LCP images without preload hints.

Route-level lazy loading with React.lazy + Suspense is the highest-leverage single change: it drops initial JS parse time by 40–60% on most SPAs. Paired with a <link rel="preload"> on the hero image and deferred analytics scripts, the average TTI improvement across our audits was 2.1 seconds on a mid-range mobile device.

We target Lighthouse Performance above 90 on every project. Core Web Vitals are a confirmed Google ranking signal, and the drop-off data at slow load times is significant enough to treat them as a business metric, not just a technical one.

Every HoangCore project ships with a CI/CD pipeline that enforces the same deploy strategy: blue/green on ECS with ALB target group switching, automated health checks before traffic shifts, and a one-command rollback that completes in under 90 seconds.

Database migrations are handled separately from code deployments and are always backward-compatible, with a staged rollout pattern so the old and new app version can run simultaneously during the switchover window. No 2 AM downtime windows, no maintenance pages.

Observability is wired from the start: structured JSON logs to CloudWatch, custom metrics to a Grafana dashboard, and PagerDuty alerts on error-rate spikes. Having this observability in place before launch means your first production incident is a manageable event, not an uncoordinated scramble.

The most common dashboard mistake is showing everything at once. The most common dashboard failure is information overload. We structure the top level around the 3 to 5 metrics that actually drive decisions, with supporting detail available on demand rather than competing for attention.

Skeleton states and optimistic updates are non-negotiable on data-heavy views. A dashboard that flashes empty states while loading creates anxiety because users assume something is broken. Skeleton screens keep perceived performance high even when real performance isn't.

We test dashboards with real users before launch using Maze or UserTesting, targeting task-completion rate on the three most critical workflows. User testing consistently surfaces navigation and labeling issues that internal review misses. Running these sessions before launch is far less costly than addressing them after.

React Native is our default for cross-platform mobile because the business logic, API layer, and state management are identical between iOS and Android; only platform-specific UI components and native modules differ. For most products, that's less than 10% of the codebase.

We keep the JS bundle lean with Metro's tree-shaking, Hermes engine enabled by default, and image assets delivered at the correct density from a CDN. Cold start times under 1.5 seconds are achievable on mid-range Android devices without native rewrites.

Over-the-air updates via Expo EAS Update let us ship bug fixes and feature flags to users within minutes, with no App Store review wait. We use this for hotfixes and A/B experiments while reserving full store releases for significant new features.