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
FAQs
Yes. Talentica offers gradual zero-downtime delivery with staged rollouts, feature flags and fallback mechanisms so that your product remains live throughout the modernization.
- Built for high stakes: Experts in large, business-critical systems, with deep historical dependencies, enormous data volumes and rigorous uptime requirements
- Proof point: Re-architected 10-year-old data management platform to modern observability offering, live for 250 clients in first year
- Parallel runs of old and new components before cutover, so every change is tested against real behavior.
AI takes the slow, manual work out of modernizing. Talentica employs AI-powered automation to analyze, refactor, test and detect risk in code to accelerate delivery cycles, minimize errors and make modernization predictable.
- Condensed timescales: AI-augmented engineering environments reduced multi-year modernization roadmaps into ambitious delivery timetables
- Proof point: 10-year-old legacy monolith transitioned from no AI to fully agentic product in 100 days
- Documentation of undocumented old code and automatic test generation for code with little or no test coverage, generated by AI.
An architecture assessment examines how your present system is constructed, where it is holding you back and maps the shortest, lowest-risk path ahead. It often includes scalability, performance, security, technical debt, cloud expenses, data architecture and AI readiness.
- Codebase and dependency analysis: Analyzing code and architecture to understand dependencies and improvement areas
- Bottleneck identification: Engineers review existing systems, pinpoint bottlenecks, and offer the fastest ROI path
- Aligning with outcomes: Every initiative was related to an outcome – faster delivery, scalability, or tech relevance
- A prioritized modernization roadmap with effort, risk, and projected impact for each item.
Sure. A comprehensive rewrite is only carried out when the business case requires. Most engagements focus on focused refactoring, modularization and platform evolution to achieve early ROI and mitigate risk.
- Proof point: 17-year-old recruitment platform, from monolith to modular monolith with multi-tenancy, doubled feature development pace
- Progress measured – Performance, cost and delivery indicators are recorded against milestones.
- Start with the modules that cause the most pain or impede most roadmap work, so early stages pay for later ones.
By building for tomorrow from within today’s sprints, not a separate, delayed initiative.
- API-first, cloud-native, AI-ready foundations for future-ready integrations for data, AI and ecosystem
- Designed to evolve: Modular, AI-first architecture that scales and adapts with emerging AI capabilities
- Tech debt in sprint: Tech debt is included in every sprint, not pushed downstream
- Couple modernization work with high-priority roadmap features so every modernized module delivers new customer value.
By running both the legacy and new side by side and then moving traffic gradually. In one Talentica engagement, a channel marketing platform transitioned from monolith to microservices while serving live users:
- Strangler fig pattern: Legacy functions replaced one by one with new services for a gradual, smooth transition
- Anti-corruption layer: New services, decoupled from legacy dependencies, with well-defined interfaces and protocols
- Micro frontends: React app embedded in the existing .NET program, so frontend modules are deployed independently without disrupting the user interface.
- Outcome: Feature development 2x faster
- Well defined rollback plans for each release to rectify any problem within minutes.
Yes. Talentica builds integrations based on proven patterns that work across many formats, protocols and security needs at scale.
- Safe coexistence: Secure HTTPS plus SQS and SNS queues connecting ancient and new systems for dependable, asynchronous communications
- Cloud Modernization – Re-platforming workloads, enabling multi-tenant architectures and supporting single-to-multi-cloud migrations
- Reduced risk of release: Automated CI/CD, infrastructure-as-code, observability and integrated security
- API versioning, backward-compatible contracts and testing so that partners and downstream systems stay working during the move.
Change the core without stopping the product
Modernize critical systems in stages while keeping existing commitments and feature development moving.