Talentica > Enterprise > AI-Assisted Modernization
AI-Assisted Modernization
Re-architect, re-engineer, and future-proof your product without interrupting live operations or product delivery.
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 to the APIs, 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 & build
- Self-service infrastructure provisioning
- Golden path & 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 & orchestration (Kubernetes)
- Multi-cloud & hybrid cloud strategy
- Cloud cost optimization (FinOps)
- Infrastructure as code implementation
- Cloud security & compliance hardening
Reduce constraints created by aging code, frameworks, and architectures without disrupting the running product.
- Legacy codebase assessment & remediation
- Monolith decomposition
- Language & 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 & data warehouse design
- Data pipeline & ETL modernization
- Real-time data streaming architecture
- Data governance & quality frameworks
- Master data management
- Analytics & AI-ready data infrastructure
Improve how systems expose, secure, manage, and integrate services across the product ecosystem.
- API strategy & design
- REST to GraphQL migration
- API gateway implementation
- API documentation & developer portals
- Legacy API wrapping & abstraction
- API security & 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 & bounded contexts
- Change management & safe deployment patterns
How we work
No big bang. No feature freeze.
Legacy out, modern in, nothing goes dark in between.
Map before we move
We assess the architecture, the debt, and the risk before touching a line of code.
AI-native engineering
We use AI across the work itself. This includes reading legacy code, generating tests, and accelerating migration to make modernization faster.
Modernize in slices
We replace the old system piece by piece while it keeps running. This prevents any disruption in existing shipping plans.
Small teams own the outcome
Our focused pods have experienced engineers. They don’t operate like contractors.
Handed back better
Tested, documented, and owned by your team.
Case Study
Modernization in practice
Modernization
20-year monolith to AI-native platform in 6 months
A 20-year-old monolith on ColdFusion, C#, .NET 4.8, ASP.NET, and Oracle was slow, hard to change, and stood in the way of becoming an AI-native platform. It also had 3M+ lines of code against a single 800 GB database.
- Monolith-to-microservices migration to Node.js, NestJS, React, and Python
- Replaced legacy third-party subsystems and consolidated micro-frontend hosting (56 → 7 EC2 instances)
- Migrated the 800 GB Oracle database to Databricks Medallion Lakehouse
- Unified the data layer across monolith and microservices; moved reporting off the transactional DB
- Built in AI capabilities throughout with LangGraph, OpenAI, and Gemini
Retired the legacy stack entirely and modernized security and infrastructure
In 6 months, the platform moved from a 3M+ line, 20-year-old monolith to an AI-native, cloud-agnostic architecture of 40+ microservices and 35+ micro-frontends, with the legacy stack fully retired and dynamic self-service reporting live for partner accounts.
Re-Architecture
Two loan systems in parallel, zero duplicates
The loan origination stack ran on manual processes with no shared API or customer ID and thin third-party integration. It had to be replaced location-by-location, while the live legacy system kept serving 7.5L+ loans, 20L+ co-applicants, and 35L+ KYC records—with ~₹35 lakh at risk per duplicate sanction.
- A new Java/Spring Boot LOS with 20+ microservices and React micro-frontends, rolled out in phases
- Real-time CDC sync (Debezium → Kafka → Flink) between the legacy and modern databases, using deterministic UUID mapping to prevent duplicate customers
- Consolidated 75+ manual credit checks and 20+ workflows into a Drools rules engine and Zeebe workflow orchestration
- An S3 data lake feeding 80+ Power BI reports, decoupling reporting from production databases
- Microsoft SSO with granular roles and LMS integration for post-disbursement journeys
98% de-duplication accuracy across 750K+ loans synced in real time, 36% faster QC screening, near-zero downtime during migration, and 22 microservices live on AWS ECS. Legacy and modern systems running in parallel and zero code changes required to either.
Our partners
Tools & technologies
Infrastructure & Cloud
DevOps, Automation & GitOps
Data Platform & Streaming
API & Integration Layer
Change the core without stopping the product
Modernize critical systems in stages while keeping existing commitments and feature development moving.