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Legacy System Modernization

Modernize the system you already run.

We modernize the estate an enterprise already runs — integrations, business logic, release process — so it can carry new capability, including AI, without a rewrite. A different discipline from a commerce platform migration.

What actually blocks it

It is rarely the technology.

Most enterprises know their stack is dated. What stalls modernization is the shape of the risk — the same shape we cover in the real cost of tech debt.

01

Integration sprawl

A decade of point-to-point wiring — ERP, OMS, WMS, and tax coupled straight into application code. Touch one integration and three others move.

02

Undocumented business logic

The pricing rules, tax exceptions, and fulfillment edge cases live in code nobody wrote and no document explains. A rewrite means re-deriving decisions made a decade ago.

03

No test harness

Changes ship on faith. Without characterization tests around what the legacy system actually does, every refactor risks silently changing behavior the business depends on.

04

Release fear

Deploys happen at 2am, once a quarter, with the whole team on call. The system works — but nobody trusts changing it, so nothing changes.

The patterns we use

Five patterns, applied in the same order every time.

Named patterns, not improvisation. Each one exists to let the legacy system keep running revenue while the new one earns trust — the same sequencing approach covered in our post on legacy integration.

Modernization patternsRollback rehearsed

Strangler fig

New functionality intercepts and replaces the legacy system route by route, until nothing calls the old code path. The legacy system keeps running until it has nothing left to do.

Anti-corruption layer

A translation boundary between the legacy domain model and the new one, so new code never has to understand the old system's assumptions directly.

Change-data-capture

Legacy writes stream out as events instead of being polled or dumped nightly. New services stay current without touching the legacy write path.

Parallel run

Old and new systems process the same input side by side. Outputs are diffed until the new system proves it produces the same answer — then it takes over.

Phased cutover with rollback

Each slice of functionality moves behind a flag, with a rehearsed rollback path. No date depends on everything going right at once.

Read the full sequencing approach

Legacy integration is a sequencing problem, not a rewrite-or-don't decision — our full breakdown of the anti-corruption layer approach.

Why AI readiness depends on this

AI does not fix a system it cannot see clearly.

An agent is only as safe as the system it is pointed at. Agents need clean contracts and permissioned data access — the exact things modernization builds.

01

Clean contracts

Agents call typed interfaces, not screen-scrape or parse undocumented flat files. The anti-corruption layer we build for modernization is the same contract an agent needs to act safely.

02

Permissioned data access

An agent that can query the legacy database directly is a liability, not a capability. Change-data-capture and API boundaries give agents scoped, auditable access instead.

03

Observable behavior

An agent making decisions against a system needs to know what changed and why. Structured logs and events give both the agent and the engineer reviewing it something to reason about.

Where modernization ends and agentic work begins is covered in AI Agents for Retail Ops.

How it runs

Audit, one slice in production, then rollout.

  1. 01Phase 1

    Audit

    Read the code. Map the integrations no one documented. Find where test coverage is real and where it's theater. Output: a modernization plan, patterns applied per system — not a generic slide deck.

  2. 02Phase 2

    First slice in production

    Apply strangler fig to one bounded piece — one route, one integration, one business rule — running in production against real traffic. Proves the approach before it scales.

  3. 03Phase 3

    Phased rollout

    Each subsequent slice follows the same playbook: anti-corruption layer at the boundary, parallel run until parity, cutover with rollback armed. The legacy system keeps running until it has nothing left to do.

The honest take

When to carry it forward — and when to rebuild.

We sell modernization. We also tell buyers when a rewrite is the honest answer. The question is not which we prefer — it is which fits the system in front of us.

When to carry it forward

  • The system still drives real revenue

    And the business logic embedded in it took years to tune. A replatform means re-discovering rules nobody wrote down.

  • You need AI or new integrations to reach it

    Modernizing the boundary — not the whole system — is usually the faster path to giving agents and new services safe access.

  • The team that built it is still around

    Their knowledge is the fastest way to extract the real rules. Modernize while that knowledge is still available to consult.

When a rewrite is honest

  • It barely does what the business needs today

    Let alone what it needs next. Modernizing a system whose foundation is wrong just moves the problem forward in time.

  • The original platform decision was wrong for this business

    Not just old — wrong. A JSP monolith built for a single-region catalog won't serve a multi-brand, multi-region one no matter how it's modernized.

  • Nobody who understands it is still around

    When institutional knowledge is already gone, reverse-engineering intent from code can cost more than a clean rewrite with the current team.

Struggling to decide either way? See Project Recovery.

We inherited this fluency the same way our clients did — see how it shows up in custom commerce development.

Where we built

Engagement record

Gifting platform, modernized in stages.

90+PageSpeed score, up from 60 — P95 page load 6s to under 2s, Spring Boot rolled out incrementally with zero revenue freeze

Read the case study
Bundled with every engagement

AI-assisted QA included.

Tests aren't a separate line item — they're how engineers ship covered code. Lower QA budget, faster feedback, better coverage than traditional QA cycles.

Auto-generated E2E

Playwright + LLM scaffold tests from product flows.

Self-healing selectors

Tests don't break when copy or DOM shifts.

Production replay

Real traffic patterns become regression suites.

PR-level impact

Only the relevant tests run on each diff.

How an engagement starts

Three steps to a partnership

01

Intake call

30 minutes. We listen, you talk. No deck.

02

Diagnostic

We audit the surface, name the bottleneck, propose a path.

03

Kickoff

Senior engineer in your standup by week two.

Modernize it without stopping it

Tell us what the system runs today. We'll come back with the patterns that apply and where the first production slice should land.