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migVisor Suite Tools for data migration and modernization

4.6

(27 ratings)
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Overview
Differentiators
Features
Reviews
migVisor suite new logo

migVisor Suite

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Overview
Differentiators
Features
Reviews

Solution Overview

The migVisor Suite is an end-to-end modernization platform built to transform legacy data analytics and transactional systems into AI-native, cloud-ready foundations for enterprise AI adoption. EPAM deploys migVisor as the core accelerator of its Data & AI Modernization Service Offering, achieving 80–90% faster delivery timelines and 30–60% average TCO improvement across 75+ enterprise modernization projects delivered worldwide.

Customer Problem

  • High Total Cost of Ownership (TCO) for data platforms 

  • Outdated or sunset technologies creating direct compliance, infosec, security, and business continuity risks 

  • Inability to capitalize on AI and cloud innovation, acting as a direct roadblock to growth and market parity 

  • Manual-heavy modernization execution that limits scalability and quickly inflates project costs 

EPAM Solution

migVisor Suite powers end-to-end Data & AI Modernization across three phases: 

  • Phase 1: Assessment & Design. migVisor Analytics ascans the full legacy estate — DWH, ETL, BI, and data models — providing automated dependency mapping, complexity scoring, and AI-driven insights. Modernization planning and design draw on EPAM's proven delivery experience to produce a pragmatic, execution-ready roadmap.

  • Phase 2: AI-Driven Implementation. The migVisor agentic suite powers the transformation. Explainer interprets legacy logic into human-readable BRDs. Co-Pilot designs the optimized target state using curated patterns and SME best practices. SmartBuilder generates validated, production-grade code, fully traceable to its legacy source. Code Converter and BI Converter accelerate platform-specific migration (for example, Tableau to Power BI). Human oversight at each handoff keeps quality intact — achieving 80–90% faster delivery.

  • Phase 3: Data Quality Testing. migVisor Reconciler performs row-level and aggregate reconciliation before cutover, comparing source and target across record counts, key-level values, and aggregate metrics, with full drill-down to offending rows. Automated reports give stakeholders objective, evidence-based sign-off — de-risking go-live.

AI Driven Modernization

The migVisor 2.0 agentic suite transforms legacy platforms through its core AI tools: 

migVisor Analyticsassessment engine: scans the estate, scores complexity, and maps dependencies to ground every decision in an accurate current-state picture. 

migVisor Explainer — documentation engine: interprets code via LLM agents into a queryable knowledge base of business logic, data flows, and dependencies, eliminating weeks of manual analysis. 

migVisor Co-Pilot — AI architect: applies a curated library of architectural patterns to design, optimize, and rationalize the target state, with SMEs injecting vision and best practices. 

migVisor SmartBuilder — automated code factory: generates fully validated, production-grade code from approved specs, with a continuous loop linking every line to a spec and legacy source. 

migVisor Code Converter and BI Converter — platform-specific translation: Code Converter rewrites legacy ETL and SQL into target-native code; BI Converter migrates dashboards, reports, and semantic models with structure and logic preserved. 

migVisor Explainer
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1/5

Key Differentiators

Automated Migration

Agentic pipeline analyzes legacy logic and generates production code, delivering 80–90% faster migration

Built-In Traceability

Every generated line traces back to a spec and its legacy source, ensuring audit-ready, correct output

Full-Stack Suite

One toolset spans assessment, design, code generation, BI conversion, and data quality — no gaps

Benefits

80–90% faster modernization timelines

Agentic AI dramatically accelerates assessment, design, and implementation

30–60% average TCO improvement

Delivered across licenses, infrastructure, support, and fully optimized scope

Day 1 AI-ready foundation

The target state is governed, analytics-enabled, and agent-ready at cutover

Features

  • Inventory assessment: migVisor Analytics automates data estate scanning with AI-driven insights, object complexity and migration effort analysis, dependency identification, and full migration strategy creation complete with a calculated delivery timeline for each phase. 
  • AI-driven modernization design: migVisor Co-Pilot uses curated architectural patterns and SME best practices to design the optimized target state, rationalize the data landscape, and produce BI harmonization and detailed data product plans. 
  • Production-grade code generation: migVisor SmartBuilder generates validated code from approved specs with a continuous validation loop, ensuring every output traces back to a legacy source and meets all platform quality standards before deployment. 
  • Data quality & reconciliation: migVisor Reconciler performs row-level, column-level, and aggregate reconciliation across multiple databases and cloud targets, guaranteeing production readiness before cutover and providing comprehensive discrepancy reporting. 

Customer Ratings & Reviews

4.6

Based on 27 votes

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Most recent reviews

November 20, 2023

When my company realized that our on-premise products didn't meet the needs of all our users, we decided to switch to cloud-managed services. We used migVisor to plan the cloud migration process and to estimate and reduce costs as much as possible. migVisor did a great job in building our business case by providing a migration assessment. It made our cloud migration to AWS services smooth and helped us gain visibility into the expected costs and time necessary to perform all the migration phases.

Author: Simon L.

14 users found this review helpful

September 3, 2023

migVisor was a great help when we did data migration to Google Cloud. It precisely estimated and planned our cloud journey helping us understand the next steps and reduce costs of cloud migration overall.

Author: Anthony Drake

9 users found this review helpful

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