Real-Time Fraud Detection Platform
Cybersecurity / FinTech
Real-time fraud detection and risk scoring platform
$2.4M+
Fraud blocked annually
<50ms
Checkout latency
<0.1%
False positive rate
80%+
Test coverage
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System Architectures
Architectures that scale
Examine how we engineer high-performance, compliance-ready systems that free organizations from vendor lock-in.
Quick Answer
In short: 6 ERPStack architectures are documented here in full — a real-time fraud detection platform, an NGO field operations and compliance portal, an artisan manufacturing ERP, a GST order-to-cash compliance engine, a public-health data dashboard, and a Meta ads analytics pipeline. Each records the legacy system it replaced, the architecture that replaced it, the stack it runs on, and the measured result. Each study is titled by system rather than by client, and several of the URLs name the client or the project.
What these custom ERP and B2B software case studies cover
6 real ERPStack engagements. Each case study records the legacy system it replaced, the architecture that replaced it, the stack it runs on, and the measured result.
The six architectures at a glance
- Real-time fraud detection platform — finance, PCI DSS and SOC 2 scope. ClickHouse analytics database, Redis, PostgreSQL with Drizzle ORM, Zod, Terraform. 380ms to 48ms at checkout; false positives under 0.1%.
- Field operations & compliance portal — ngo, GDPR and ISO 27001 scope. Payload CMS on MongoDB Atlas, Next.js, Node.js, AWS, Sentry, RBAC. 41 collections; 98% faster reporting; offline-first.
- Artisan manufacturing ERP — manufacturing and retail. React, TypeScript, Node.js, MongoDB Atlas, Microsoft Azure, shadcn/ui, Tailwind CSS. 500+ artisans; payouts from 3 weeks to under 2 hours.
- Order-to-cash & GST compliance engine — retail. Next.js, React, TypeScript, MongoDB Atlas, Vercel, REST API. 5 gapless document prefixes; ₹0.00 rounding drift across 4 invoice types.
- Public-health data dashboard — government and healthcare, HIPAA scope. React, Node.js, PostgreSQL with Drizzle ORM, AWS, shadcn/ui, Tailwind CSS. 2,500+ clinics; refresh under 10 seconds.
- Meta ads analytics pipeline — retail marketing analytics. Node.js collectors over a REST API, reconciled against the same brand’s custom ERP. ₹34,721 of spend and 379,676 impressions on 1 daily axis.
Field Operations & Compliance Portal
Non-profit / NGO
Unified operational portal for field tracking and compliance
6x
Code Reduction
70+
Collections
35+
Background Jobs
12
Modules
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Artisan Manufacturing ERP
D2C / Artisan Fashion
Supply chain and production system for artisan manufacturing
36
Data Models
118
API Routes
42
Dashboard Pages
68K+
Lines of Code
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Order-to-Cash & GST Compliance Engine
Retail / D2C Commerce
GST-compliant invoicing, consignment settlement, and receivables automation for a D2C handcraft brand
5
GST prefixes sequenced gaplessly
₹0.00
Rounding drift in GSTR-1
5,313
Catalog rows reconciled
4
Invoice types unified
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Public-Health Data Dashboard
Government / Healthcare
Health data dashboards for public administration
React
Platform
shadcn/ui
UI System
AWS
Deployment
Gov
Sector
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Meta Ads Performance Analytics Pipeline
Retail / Marketing Analytics
Unified paid and organic Meta Graph API analytics for a D2C handcraft brand
286K
People reached (audit window)
379,676
Impressions normalized
₹34,721
Ad spend reconciled
31 days
Daily series unified
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Questions about these architectures
Are these real client architectures or reference builds?
All 6 architectures are real engineering engagements, written up from the delivered system rather than from a brief. The write-ups are titled by system rather than by client, but they are not anonymised: several of the URLs name the client or the project. The figures in each study — measured latency, reconciled totals, row counts, collection counts — come from the delivered systems and the client’s own reporting, not from a marketing estimate.
Which of these architectures is relevant to my sector?
6 sectors are covered. Cybersecurity and finance: a real-time fraud detection platform. NGO and non-profit: an offline-first field operations and compliance portal. Manufacturing and D2C retail: an artisan production ERP plus a GST order-to-cash engine. Government and healthcare: a public-health data dashboard over 2,500+ clinics. Marketing analytics: a Meta ads pipeline. Where none of the architectures matches your domain, the underlying patterns usually do.
What technology do these architectures have in common?
TypeScript everywhere, and a database the client owns outright. Across the 6 architectures the recurring stack is Next.js and React on the front end, Node.js services behind a REST API, and either PostgreSQL with Drizzle ORM or MongoDB Atlas underneath, deployed to Vercel or AWS. Specialised layers appear only where the workload demands them: a ClickHouse analytics database for fraud scoring, Payload CMS for NGO field operations.
How long does an engagement like these take?
The published architectures ran from a 4-month engineering cycle for the fraud detection platform to phased module delivery on the artisan ERP. ERPStack quotes 6 to 24 weeks by scope: 6 to 8 weeks for a single module, 10 to 16 weeks for a departmental ERP of 3 to 5 modules, and 18 to 24 weeks for a full platform, invoiced against fixed milestones rather than time and materials.