Case Studies

Capabilities & Engineering Standards

How we approach complex technical challenges across scalability, data, and performance.

CASE STUDY // 01 DATABASE & CONCURRENCY

High-Volume Relational Database Optimization

Domain: Fintech & Transactional Scale-Up

Challenge

Monolithic PostgreSQL database suffering from connection exhaustion and 800ms+ slow queries during peak market trading spikes.

Engineering Solution

Architected a distributed connection pool layer with PgBouncer, restructured critical JSONB columns into B-tree indexed composite keys, and implemented automated read-replica routing for analytical queries.

Verified Outcomes

✓ Query response times dropped from 820ms to 6.4ms P95
✓ Supported 10x concurrency (50,000 TPS) without hardware upgrades
✓ Zero downtime migration executed over a scheduled maintenance window

PostgreSQL 16 PgBouncer Go (Golang) Redis AWS RDS Aurora

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CASE STUDY // 02 B2B SAAS PLATFORM

Multi-Tenant B2B SaaS Architecture & Billing Engine

Domain: Enterprise Workflow SaaS

Challenge

Transitioning from single-tenant isolated deployments to a scalable multi-tenant architecture with strict tenant isolation, role-based access control (RBAC), and global subscription billing.

Engineering Solution

Designed a schema-based multi-tenancy model in Next.js and Go with row-level security (RLS), integrated automated Stripe and Razorpay webhook reconciliation, and built a customizable audit logging pipeline.

Verified Outcomes

✓ Tenant provisioning time reduced from 2 days to under 4 seconds
✓ 100% compliant tenant data isolation and audit logging
✓ 99.99% platform availability across 12 consecutive months

Next.js 15 Go PostgreSQL RLS Stripe API Docker Cloudflare Edge

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CASE STUDY // 03 AI & DETERMINISTIC AGENTS

Production AI Tool-Calling Engine

Domain: Healthcare Tech Enterprise

Challenge

Generative AI prototype suffered from frequent hallucination, high latency (4.5s+), and brittle prompt chains when extracting structured medical invoice data.

Engineering Solution

Replaced unstructured text generation with a compiled Go and Python microservice using JSON-schema constrained decoding, self-hosted quantized model inference with vLLM, and a pgvector semantic search cache.

Verified Outcomes

✓ Extraction accuracy increased to 99.8% with zero schema violations
✓ Inference latency dropped by 74% (from 4.5s to 1.1s)
✓ Infrastructure costs reduced by 60% via self-hosted open-weights models

Python vLLM Go pgvector FastAPI Docker AWS EC2 GPU

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