Engineering the
Digital Core
Speed is the currency of the digital age. We deconstruct monolithic legacy systems and rebuild them as agile, cloud-native platforms that scale with demand.
The Context
A mid to large software company operating a mission critical platform had grown over a decade into a tightly coupled monolithic system. Release cycles were slow and infrastructure costs were rising.
Reliability issues were increasing under peak load. Engineering teams were spending more time on platform maintenance than on evolving the product.
The Friction Point
Deployment risk and brittle integrations paralyzed the team. Fear of change due to unclear system dependencies made every release a high stakes event.
Tightly coupled architecture preventing independent scaling of core services.
Manual testing cycles that consumed days of engineering time.
Scaling limitations causing performance degradation during peak usage.
Cloud-Native Transformation
Incremental Strangler Pattern
We incrementally peeled off specific functionalities into microservices rather than attempting a high-risk complete rewrite.
Containerization & K8s
Standardized environments with Docker and implemented Kubernetes orchestration for horizontal scalability and fault isolation.
Automated CI/CD Pipelines
Integrated security checks and automated testing into the pipeline to eliminate manual release gates and enable safe daily deployments.
Impact on Delivery
Faster cycles.
Improved stability.
Cloud efficiency.
Fully automated.
AI Product Integration
Adding "smarts" to software. We helped a project management tool integrate Generative AI to automate task creation and summarization.
AI Product
Integration
Embedding intelligent assistants directly into the user workflow to automate repetitive tasks and drive product differentiation.
The Challenge
Users were spending significant time on repetitive tasks such as documentation, analysis, and workflow coordination. Product differentiation was plateauing and user feedback pointed to productivity friction rather than missing features.
Manual effort consuming high-value expert time.
Inconsistent outputs across different user groups.
Difficulty scaling human-intensive workflows.
User fatigue with complex tabular interfaces.
The Solution: Embedded AI Assistant
We integrated a secure, fine-tuned LLM that acts as a co-pilot directly within the application. It understands user context and provides generative assistance with strict guardrails.
Auto-generating concise summaries from complex project threads.
Drafting documents and updates with human-in-the-loop control.
Enabling natural language search across proprietary product data.