Traditional SDLC

Manual coding and testing​​

Long development cycles​​

Limited documentation​​

Reactive problem solving​​

Fixed team capacity​​

Translation of requirements into code​​

Building and testing functionalities​​

Manual debugging and optimization​

AI Assisted SDLC

AI-powered code generation and validation​​

Rapid iteration and deployment​​

Comprehensive automatic documentation​

​Proactive issue detection​

Scalable hybrid intelligence​

Defining problems for AI to solve​

Data preparation and cleaning for AI​

Orchestration of intelligent agent workflows​

Full process tracking​

​

With GitHub Projects, we track AI agent activities on the Kanban board, while issues requiring human approval are automatically highlighted for review.​

Automated events workflow​

​

With GitHub Actions, the selected legacy repository is prepared for full automation in the migration process.​

​

Self-learning and improvement​

​

With GitHub MCP Server, AI agents obtain logs from past executions and continuously learn from data, improving their capabilities, adapting to new challenges, and updating the knowledge base.

Building and evolving apps​

Develop and evolve applications with AI assistance​

Resource savings​

Let AI handle repetitive tasks while your team focuses on what really matters: innovation​​

Full traceability​

Track decisions and changes with total transparency​

Human expertise + AI​

Improve results by combining artificial intelligence and human capabilities​

Self-learning agents​

Evolve your projects with AI agents that learn continuously,

always under human supervision​

​

Modernized

project scaffold

generation​

Creating a Test

Case Library

for validating

business rules​

Legacy code analysis to produce comprehensive documentation​

Proposal for an optimized database schema for a new application​

Modernized API layer, aligned with the new schema, and validated business logic​

System architecture​

Component structure with data flow diagrams

Business rules catalog​

Complete inventory of business rules mapped from legacy source code​​

Database documentation​

Initial database

schema design​

Executive summary​

High-level overview with key metrics, timeline, and manual cost estimate

Code analysis​

Detailed analysis with

complexity metrics​

Integration points​

Specifications with endpoints

for a modern system​

Migration recommendations​

Modernization strategy with technology stack and risk assessment​

Agentic AI orchestrating modernization workflows​

Continuous knowledge capture driving optimizations​

Real value delivered at enterprise scale​