Now booking software engineering engagements for Q3 — Q4 2026

Legacy systems, modernized
without the rewrite.

Mainframes, monoliths, and aging .NET or Java estates moved to cloud-native architectures. Done in planned phases, with tested rollback paths and parallel-run validation before any cutover.

The problem we solve

Why modernization projects stall.

Most modernization projects start as ambitious rewrites that promise everything and ship nothing. The business that depends on the legacy system can't tolerate a long freeze, so two years in the new system is half-built, the old system is now older, and the team that started the work has churned.

The way out is incremental. Identify the seams in the legacy system. Stand up a façade in front of it. Move services across one cohort at a time, with feature flags controlling who gets routed where. The business keeps running. The migration proves itself in production before any cutover gets signed off.

Apollo's work is incremental from the start. Strangler-fig migrations move one cohort at a time, with rollback paths tested under real conditions. Parallel runs prove the new path against the old before anything legacy gets decommissioned.

Where it usually breaks

The data migration. Schema and semantic differences accumulate, and the cutover that looked simple on the design doc takes another six months once it meets the real data.

What gets overlooked

Operational continuity during the migration. The team running the legacy system has to keep running it while the new path gets built. Staffing for that gap is not obvious until it bites.

The compliance reality

Audit and regulatory obligations don't pause for modernization. The new path has to clear the same controls as the legacy one before any production traffic moves to it.

What we deliver

Five shapes of modernization work.

Most engagements land in one of these patterns. Each has its own decomposition strategy, its own data-migration profile, and its own cutover risks.

Mainframe Modernization

COBOL, JCL, RPG, and CICS estates moved off the mainframe in cohorts. We re-platform the code where that's appropriate, and we re-architect the parts that genuinely earn the rewrite.

COBOL · CICSJCL · RPGIMS · VSAMRe-platform

Monolith Decomposition

Decompose a monolithic application into services using the strangler-fig pattern. Domain boundaries identified, anti-corruption layers built, and traffic shifted one slice at a time behind a feature-flagged façade.

Strangler-figDomain-drivenAnti-corruptionService extraction

Cloud Migration

AWS, Azure, and GCP migrations using the right mix of rehost, re-platform, and re-architect for each workload. With landing zones, networking, and observability built as part of the migration itself.

AWS · Azure · GCPLanding zonesNetworkingIaC

Application Modernization

Java EE to Spring Boot, .NET Framework to .NET 8, classic ASP to modern stacks. Done alongside the test coverage and CI pipeline that the new stack actually needs to operate, not as a code translation without infrastructure.

Java EE → Spring Boot.NET Framework → .NET 8ASP → modernTest coverage

Database Migration

Oracle to Postgres, on-prem SQL Server to cloud, OLTP estates onto modern data platforms. With schema reconciliation, change-data-capture for zero-downtime cutover, and parallel-run validation that proves the new system before the old one comes down. The piece of any modernization that most often slips its timeline, treated with the rigor that prevents that.

Oracle → PostgresOn-prem → CloudCDCParallel-run validationZero-downtime cutover
Reference architecture

A phased modernization, end to end.

Simplified, but representative of how we run a strangler-fig migration. The façade is the centerpiece. Feature-flagged routing keeps both paths live until the cutover, while change-data-capture keeps the data layers in sync.

APPLICATIONSROUTING + FAÇADESERVICESDATAUser AppsInternal · Customer-facingClient SDKsMobile · WebPartner APIsB2B · IntegrationsWeb PortalsCustomer · InternalAPI Gateway / Strangler FaçadeFeature flags · Cohort routing · Shadow readsLegacy ServicesMonolith · Mainframe APIs · Legacy stackNew ServicesMicroservices · Cloud-nativeLegacy DatabaseOracle · DB2 · SQL ServerCDC / Sync BusDebezium · AWS DMS · KafkaNew DatabasePostgres · Cloud OLTP
Cross-cutting concerns
Audit & compliance continuityObservability & tracingRollback pathsFeature-flag governanceCost controls
Cohort-by-cohort cutoverZero-downtime data migrationParallel-run validatedReversible until sign-off
What it looks like in code

Strangler façade, in code.

Migration with parity checks

Below is a simplified version of a strangler façade we’d ship. The feature flag decides who gets routed to the new path. Shadow reads keep the new service exercised on real traffic while the legacy system still serves production requests.

Once parity checks consistently agree across the traffic patterns that matter, rollout becomes a controlled engineering decision instead of a risky cutover.

apollo / migration / strangler-router.ts
// Strangler façade for phased migration
// Legacy serves traffic while new system is shadowed

import { legacyClient, newServiceClient, flag, log } from '@apollo/migration'

export async function getUser(userId: string) {
  if (await flag.enabled('user-service.new', { userId }) ) {
    return newServiceClient.getUser(userId)
  }

  // Legacy path serves production
  const [legacy, shadow] = await Promise.allSettled([
    legacyClient.getUser(userId),
    newServiceClient.getUser(userId),
  ])

  if ( shadow.status === 'fulfilled' && legacy.status === 'fulfilled') {
    log.parityCheck({
      userId,
      match: deepEqual( legacy.value, shadow.value ),
      legacy: legacy.value,
      shadow: shadow.value
    })
  }

  return legacy.status === 'fulfilled' ? legacy.value : null
}
How we engage on legacy work

Four phases. Cohort by cohort.

Apollo's standard methodology, applied to the specific failure modes of legacy modernization. Every phase produces working software, and the migration stays reversible until cutover is signed off.

Discovery

Map the system. Find the seams.

The legacy system, its real dependencies, the cohorts of traffic that flow through it. You leave with a written assessment naming what's safe to move first and what's expensive to defer.

Design

Decomposition & data plan.

Domain boundaries, target architecture, façade design, data-migration strategy. The risk register names the failure modes we'll have to design around and the rollback paths for each.

Build

Strangler, one cohort at a time.

Service extraction in two-week iterations. The façade routes traffic by flag. Shadow reads run against the new path so we have parity data before any production traffic shifts to it.

Cutover

Shift traffic. Decommission.

Traffic moves in measured increments. Parallel runs continue until metrics agree. Legacy components come down only after the new path has carried full load for the contracted observation period.

Technology stack

The shortlist we work from.

What we modernize onto. We pick specifically for the workload and the constraints, and we'll explain why in any proposal.

Target stacks

Java · Spring Boot.NET 8PythonGoNode.js

Cloud & platforms

AWSAzureGCPKubernetesOpenShiftTerraform

Data & migration

PostgresOracleSQL ServerSnowflakeDebeziumAWS DMS

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Start a conversation

Tell us about your modernization.

Send a paragraph about what you're trying to move: the legacy system, what runs on it, what's hard about it, and what "done" looks like. We'll reply within one business day, either with a 30-minute call or with an honest "this isn't the right fit; here's who you should call instead."

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