Cohort: 1

Location: New York, United States

Application Title: Automated AI platform for digital transformation and process migration

Team Members: Rohit Kapoor (executive sponsor), Deepti Kalra, Anand Logani, Shantanu Paliwal, Shubham Jain, Swati Malhotra

The challenge

Large organizations rely on software platforms that were built decades ago. Systems based on technologies such as SAS or legacy Oracle environments often remain critical to daily operations, even as organizations move toward cloud infrastructure.

Migrating these systems is difficult. Documentation is incomplete. End-to-end data and code lineage is often missing. Dependencies across jobs, tables, reports, and downstream applications are poorly understood. Redundant, unused, or overlapping logic has accumulated over years of incremental change. Specialist knowledge sits with a small number of experienced engineers.

As a result, migration projects are slow, expensive, and risky. Much of the effort is spent on manual code assessment, dependency discovery, redundancy analysis, and rewriting, with teams working through long sequences of repetitive tasks. Late-stage defects; often discovered during testing or parallel runs, can lead to significant rework and extended timelines.

Traditional automation tools help with isolated steps, but they do not address migration as an integrated, end-to-end process.

The solution

An AI platform built specifically for platform migration

EXL developed an automated AI platform designed to support end-to-end platform migration, from estate assessment through conversion, testing, and stabilization. Rather than relying on a single general-purpose model, the platform uses multiple specialized AI agents, each focused on a specific stage of the migration lifecycle. These agents are trained using EXL’s historical migration data, capturing patterns related to code structure, dependency resolution, data transformations, and validation logic that are rarely documented formally.

The goal is not to remove human oversight, but to reduce the manual effort required for assessment, rationalization, conversion, testing, and optimization—while keeping experts involved in key architectural and business decisions.

How it works

Orchestrated agents with human review

Each AI agent performs a defined role. One analyses legacy code and data structures to extract metadata, lineage, and usage patterns. Another maps dependencies across systems, applications, and reports, identifying redundancies and opportunities for simplification. Other agents handle code conversion, test-case generation, reconciliation, and performance tuning.

The agents operate within an orchestration layer that sequences tasks, manages hand-offs, and enforces validation checkpoints. For readers unfamiliar with this approach, the system functions much like a coordinated engineering team. AI handles repetitive, time-consuming analysis and transformation work. Human reviewers focus on design choices, exceptions, risk areas, and final approvals.

The platform supports a wide range of legacy technologies and target environments. It integrates securely with existing code repositories and data platforms, produces auditable lineage and validation artifacts, and runs on a cloud-agnostic infrastructure. Human-in-the-loop controls ensure outputs are reviewed where business or regulatory risk is highest.

How it differs from conventional approaches

Reducing reliance on manual rewrites

Conventional migration projects depend heavily on engineers manually analyzing dependencies, rewriting code, and validating outputs. Automation, where used, is typically limited to narrow tasks and often lacks consistency, transparency, or test coverage.EXL’s approach treats migration as a coordinated, lifecycle-driven process rather than a sequence of disconnected steps. By fine-tuning models on prior migration work and using task-specific agents, the platform systematically addresses assessment, lineage, redundancy elimination, conversion, testing, and optimization together.

This reduces rework, improves consistency, and embeds validation throughout the migration rather than deferring risk to the end. Performance optimization and cost efficiency are treated as integral outcomes, not post-migration afterthoughts.

Impact

·        More than 80% reduction in manual effort for code assessment, dependency discovery, and lineage analysis

·        70–80% reduction in manual effort for code conversion and validation

·        20–40% reduction in overall project cost

·        30–40% improvement in post-migration code and query performance

Why it matters

Making large-scale modernization more predictable

As organizations modernize critical systems, migration speed and reliability matter as much as technical accuracy. EXL’s platform demonstrates how AI can support this transition by reducing manual workload, improving transparency through lineage and testing, and preserving expert control.

By shortening timelines, improving validation, and eliminating hidden dependencies and redundancy, the approach helps organizations modernize legacy platforms with less disruption and more predictable, auditable outcomes.