Talentica > Enterprise > AI-Assisted Modernization
AI-Assisted Modernization
Move beyond aging architectures with a spec-driven, AI-native approach that cuts modernization time, cost, and risk while preserving critical behavior and verifying every change.
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The decision to modernize is made. The execution risk is still yours.
You’ve already made the case for modernization. Now comes the challenge: delivering it on time, without stalling the roadmap, and with real business impact. In our experience, programs go off track in three places, often because modernization is treated as a slow, manual rewrite.
A 12-month rewrite stretches to 30 as manually tracing legacy systems reveals new complexities and shifts estimates.
Manual migration and testing pull your best engineers off the roadmap, slowing delivery and stretching the team.
Modern stacks inherit legacy dependencies and tangled data, leaving the AI initiatives that drove modernization blocked.
What we do
Modernize the entire product stack
AI-assisted modernization changes this. AI accelerates code analysis, dependency mapping, and testing, freeing engineers to redesign systems for AI while keeping roadmaps on track.
Build the engineering platform and workflows teams need to ship software more consistently and efficiently.
- Internal Developer Platform (IDP) Design & Build: Give developers a unified platform to build, deploy, and manage applications.
- Self-Service Infrastructure Provisioning: Enable teams to provision infrastructure without operational dependencies.
- Golden Path & Paved Road Creation: Standardize proven development workflows to accelerate delivery.
- Developer Experience (DevEx) Tooling: Reduce developer friction and improve engineering productivity.
- Platform Engineering Team Enablement: Equip internal teams to own, operate, and evolve the platform.
- CI/CD Pipeline Standardization: Automate and standardize releases for faster, more reliable deployments.
Reshape applications and infrastructure to improve scalability, resilience, security, and cloud economics.
- Cloud-Native Architecture Design: Build scalable, resilient applications optimized for cloud environments.
- Containerization & Orchestration: Simplify deployments and scale workloads consistently across environments.
- Multi-Cloud & Hybrid Cloud Strategy: Improve workload flexibility and reduce infrastructure dependencies.
- Cloud Cost Optimization: Eliminate waste and improve infrastructure cost efficiency.
- Infrastructure as Code: Automate infrastructure provisioning for consistent, repeatable deployments.
- Cloud Security & Compliance Hardening: Strengthen cloud defenses and meet enterprise compliance requirements.
Reduce constraints created by aging code, frameworks, and architectures without disrupting the running product.
- Legacy Codebase Assessment & Remediation: Identify and resolve code-level risks that slow development.
- Monolith Decomposition: Break tightly coupled applications into independently scalable components.
- Language & Framework Migration: Move to modern technology stacks while preserving critical functionality.
- Mainframe Modernization: Modernize core systems while maintaining business continuity.
- Technical Debt Reduction Roadmap: Prioritize engineering improvements that unlock faster product development.
- End-of-Life System Replacement: Replace unsupported systems with maintainable, future-ready alternatives.
Modernize data infrastructure so information is easier to govern, access, process, and use across analytics and AI.
- Data Lake & Data Warehouse Design: Unify enterprise data for faster analytics, reporting, and AI adoption.
- Data Pipeline & ETL Modernization: Automate data integration and processing for faster, more reliable data flows.
- Real-Time Data Streaming Architecture: Process data as it arrives to power real-time insights and decisions.
- Data Governance & Quality Frameworks: Improve data accuracy, consistency, security, and compliance.
- Master Data Management: Create a single, trusted view of critical business data across systems.
- Analytics & AI-Ready Data Infrastructure: Build scalable data foundations for advanced analytics, ML, and GenAI.
Improve how systems expose, secure, manage, and integrate services across the product ecosystem.
- API Strategy & Design: Design scalable, reusable APIs that simplify integration and accelerate product development.
- REST to GraphQL Migration: Enable flexible data access and reduce unnecessary API calls.
- API Gateway Implementation: Centralize API management, routing, security, and traffic control.
- API Documentation & Developer Portals: Simplify API discovery, integration, and adoption with developer-friendly resources.
- Legacy API Wrapping & Abstraction: Expose legacy functionality through modern APIs without disrupting existing systems.
- API Security & Rate Governance: Protect APIs from unauthorized access, misuse, and excessive traffic.
Evolve the underlying architecture to make the product easier to scale, change, and operate over time.
- Monolith-to-Microservices Decomposition: Break monolithic applications into independently deployable services for greater agility and scalability.
- Domain-Driven Design Implementation: Align software architecture with business domains to simplify development and maintenance.
- Event-Driven Architecture Design: Enable real-time communication between services for faster, more responsive systems.
- Service Mesh Implementation: Simplify service communication, traffic management, security, and observability.
- Modular Architecture & Bounded Contexts: Reduce dependencies and enable teams to develop and scale components independently.
- Change Management & Safe Deployment Patterns: Minimize release risks and downtime through controlled deployments.
How we work
How we modernize at AI-native speed
AI agents accelerate high-volume engineering work across the modernization lifecycle, while senior engineers guide architecture, validate outcomes, and retain sign-off.
Discover & Design
AI accelerates codebase analysis, dependency mapping, and knowledge-gap detection. Engineers define the target architecture and migration strategy.
You get: Landscape map · Target architecture · Migration roadmap
Specify & Decompose
AI extracts business rules and system behavior, helping turn them into specifications and sequenced work packages.
You get: Behavior specification · Sequenced work packages
Build & Verify
AI accelerates implementation and test generation across parallel workstreams. Every increment is validated against the parity harness.
You get: Parity test suite · Working increments per slice
Assure & Trace
AI supports defect detection, security analysis, code review, and test validation, with engineers overseeing quality.
You get: Traceability coverage · Quality & security reports
Integrate & Deliver
AI assists with CI/CD analysis, end-to-end validation, and root-cause analysis. Engineers oversee production readiness, cutover, and rollback.
You get: Production-ready system · Go-live & rollback plan
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
Modernize the core. Keep the product moving.
Define the right architecture, modernize in verified increments, and accelerate every step with AI.