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Talentica > Enterprise > Product Modernization

AI-Assisted Modernization

Re-architect, re-engineer, and future-proof your product without interrupting live operations or product delivery.

Talk to us
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The Problem

Most modernization stalls before it ships

Modernization can have significant capital and execution risk. Large rewrites may run for years, while planned features continue to compete for the same resources. Lift-and-shift can move existing architecture to the cloud without resolving underlying constraints. Pausing feature development can also slow the roadmap. The challenge is to modernize the core while keeping the product running and evolving. 

What we do

How the engine gets changed

Modernization rarely comes down to one project. It spans six areas, from the platform underneath to the APIs on top, each addressed while the product stays live.

Build the engineering platform and workflows teams need to ship software more consistently and efficiently.

  • Internal developer platform (IDP) design and build 
  • Self-service infrastructure provisioning 
  • Golden path and paved road creation 
  • Developer experience (DevEx) tooling 
  • Platform engineering team enablement 
  • CI/CD pipeline standardization 

Reshape applications and infrastructure to improve scalability, resilience, security, and cloud economics.

  • Cloud-native architecture design 
  • Containerization and orchestration (Kubernetes) 
  • Multi-cloud and hybrid cloud strategy 
  • Cloud cost optimization (FinOps) 
  • Infrastructure as code implementation 
  • Cloud security and compliance hardening 

Reduce constraints created by aging code, frameworks, and architectures without disrupting the running product.

  • Legacy codebase assessment and remediation 
  • Monolith decomposition 
  • Language and framework migration 
  • Mainframe modernization 
  • Technical debt reduction roadmap 
  • End-of-life system replacement 

Modernize data infrastructure so information is easier to govern, access, process, and use across analytics and AI.

  • Data lake and data warehouse design 
  • Data pipeline and ETL modernization 
  • Real-time data streaming architecture 
  • Data governance and quality frameworks 
  • Master data management 
  • Analytics and AI-ready data infrastructure 

Improve how systems expose, secure, manage, and integrate services across the product ecosystem.

  • API strategy and design 
  • REST to GraphQL migration 
  • API gateway implementation 
  • API documentation and developer portals 
  • Legacy API wrapping and abstraction 
  • API security and rate governance 

Evolve the underlying architecture to make the product easier to scale, change, and operate over time.

  • Monolith-to-microservices decomposition 
  • Domain-driven design implementation 
  • Event-driven architecture design 
  • Service mesh implementation 
  • Modular architecture and bounded contexts 
  • Change management and safe deployment patterns

How we work

No big bang. No feature freeze.

Legacy out, modern in, nothing goes dark in between. 

01

Map before we move  

We assess the architecture, the debt, and the risk before touching a line of code. 

02

AI-native engineering

We use AI across the work itself. This includes reading legacy code, generating tests, and accelerating migration to make modernization faster. 

03

Modernize in slices

We replace the old system piece by piece while it keeps running. This prevents any disruption in existing shipping plans. 

04

Small teams own the outcome

Our focused pods have experienced engineers. They don’t operate like contractors.

05

Handed back better

Tested, documented, and owned by your team.

Case Study

Modernization in practice

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The problem

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What we built

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Result

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Our partners

Tools & technologies

Change the core without stopping the product

Modernize critical systems in stages while keeping existing commitments and feature development moving.