The
Overview
A B2B commerce platform was struggling with an unstable, monolithic legacy stack. Wishtree stepped in to re-architect the platform from the ground up – moving to microservices, implementing autoscaling infrastructure, and layering in AI readiness for future intelligence.
Problem
Statement
The client’s legacy stack was actively hurting their business. Downtime damaged customer trust, high cloud costs eroded margins, and slow feature delivery let competitors pull ahead.
Highlights
Autoscaling cloud infrastructure
Ai
Readiness layer
70%
Reduction in downtime
45%
Lower cloud costs
3x
Faster feature delivery
Future-proofed for AI
Agentic AI refers to autonomous, goal-driven software agents that act with
limited human input to optimize specific goals like pricing, forecast demand,
and detect fraud in real time.
About Client
A B2B commerce platform serving enterprise clients across multiple industries. Their legacy monolithic system had grown organically over a decade – fragile, and expensive to maintain.
- Frequent downtime incidents frustrated enterprise customers and damaged trust.
- Cloud costs were unpredictable and climbing.
- Adding new features took weeks or months due to monolithic dependencies and spaghetti code.
- Scaling during peak demand required over-provisioning.
- The platform was not ready for AI.
- Engineers spent more time fighting the legacy system than building new value
- Re-architected the monolithic platform into loosely coupled microservices.
- Implemented Kubernetes orchestration with autoscaling infrastructure that adjusts resources in real time based on demand.
- Built an AI readiness layer with structured data pipelines, event streaming, and model-serving infrastructure for future AI features.
- Deployed comprehensive observability (logging, metrics, traces) to detect and resolve issues before customers noticed.
- Established CI/CD pipelines enabling rapid, safe deployments.
- Executed a phased migration strategy.
- The AI readiness layer captures structured event data from every transaction.
- Autoscaling infrastructure uses predictive algorithms to anticipate demand spikes and scale resources proactively.
- Observability data feeds into anomaly detection models that identify potential issues before they cause downtime.
- The new architecture is designed to support AI-powered features like personalized recommendations, dynamic pricing, and demand forecasting.
Core Features
Microservices architecture
Kubernetes orchestration
Comprehensive observability
CI/CD pipelines
Phased migration strategy
Impact
- 70% reduction in downtime
- 45% drop in monthly cloud costs
- 3x faster delivery velocity
- Peak demand handled seamlessly
- AI-ready foundation
- Reliable platform performance
- Competitive advantage restored
Why Wishtree
Wishtree modernizes legacy systems without disrupting live operations.
For this client, we:
- Cut downtime by 70% with microservices and autoscaling
- Reduced cloud costs by 45% through efficient resource management
- Accelerated feature delivery 3x with modern DevOps practices
- Built an AI-ready foundation for future innovation