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Programs · Applications · Business processes · Data · Infrastructure

Legacy Modernization

Enterprise systems built for a different decade still run most businesses, because a rewrite is a tech-debt line that loses to feature work every year. What changed is the means. AI can now read an entire system, millions of lines and years of decisions, in days. For the first time, understanding the old system costs less than living with it.

Check your systems

Legacy systems · self-check

How an old system shows up in the business.

A one-minute self-check. Tick the ones you recognize. What you tick is where an assessment would start.

Ticked

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Start with an assessment

The premise

Good modernization changes how the work gets done, not the outcomes the business relies on.

Business Value Legacy system Modernized system 0 0 closed to AI coupled manual undocumented expensive to change slow to release hard to hire for unsupported dependencies waiting in the system 0 AI-native simpler faster cheaper documented tested waiting in the system 0 Step 1 Step 2 Step 3 Step 4 Manual idle Code idle Manual idle Manual idle Agent ready Code ready Agent ready Manual retired Manual
Point of view
Every system takes work in from the business and hands results back. Modernization keeps the results the business relies on and changes how they are produced: the same requests come back sooner and at a lower cost, with nothing waiting on a person. What does change inside is a recorded decision, never an accident.

The purpose

What does the business get?

A system that stops holding the business back.

Legacy systems survive because they still work. But every new product, process, integration, or business need becomes harder to support. The business starts adapting around the system: with manual work, extra people, and workarounds.

Modernization removes those constraints. Manual work can be automated, AI can become part of the operating model, and new capabilities become easier to introduce.

The result is not just newer technology. It is a system the business can understand, control, and change again.

The moment

Why can’t modernization wait?

Because AI has changed the economics of modernization.

Rebuilding a system that has run a business for fifteen years used to take years: much of that time spent understanding what it actually does.

Today, agents can analyze legacy code, recover business logic, document behavior, and accelerate new code and test generation. The modernized system can then be validated against the old one before migration.

What used to be a multi-year bet can now be planned, priced, and proven. That is what changed.

The rule

Understanding first.

A legacy system carries years of business decisions. Some live in the code. Others live in manual steps, workarounds, and the people who run the process. Together, they form the real specification.

AI changes how fast we can recover it. Agents can read across code, data, integrations, documentation, and process knowledge to trace business rules, dependencies, validations, and exceptions.

We turn that into one agreed specification: the basis for scope, pricing, rebuilding, and validation.

Start with an assessment

The path

From understanding to switch‑off.

Every modernization follows the same order: understand before deciding, decide before rebuilding, prove before switching over. AI speeds up the work at every step. Business and engineering keep the decisions.

  1. 01

    Understand

    Read the system as it actually runs.

    The assessment above. Agents read the code, we read the process with the people who run it, and the two become one specification of how the business runs through the system today.

  2. 02

    Decide

    Choose what stays, changes, and disappears.

    Agents surface dependencies, duplicated logic, manual work, obsolete rules, and the places where the system holds the business back. With you, we decide what is policy and what is legacy accident, and what to keep, redesign, or retire. The decisions and the order of moves go into one document everyone builds from. Scope is set and the first phase is priced here.

  3. 03

    Redesign

    Design the process first. Then the system.

    With the current state understood, we use agents to explore alternatives and model the target state in days rather than months. The business process comes first: fewer handoffs, fewer manual checks, no steps that existed only because the old system required them. Architecture, services, and data follow that decision. Nothing outside it gets touched.

  4. 04

    Rebuild

    Faster implementation, same ownership.

    Agents generate code, tests, documentation, and migration assets from the agreed specification. Engineers write by hand where the specification is not explicit, and review, refine, and sign every module that goes to production. The business stays the owner of the process being rebuilt and sees it written up before it ships.

  5. 05

    Prove and transition

    Prove the replacement before switching off the old system.

    Recorded production inputs are replayed through both systems and compared field by field, with agents doing the comparison at volume. Data is reconciled to row counts and checksums. Performance is measured at production load. Every difference is either a decision recorded in the specification or a defect that gets fixed. Cutover is rehearsed on production-sized data, rollback is tested, and the people who will run the new process approve the switch.

The map

What a rebuilt layer looks like, one at a time.

Each layer, with the engagements we have run in it and what we have written about it. Every move on this map runs the path above.

Application and code

Languages, frameworks, dependencies, code structure, maintainability.

Capability Code Modernization The code read first, rebuilt against audited specs Capability → Application rebuild · Lane Health A no-code platform rebuilt as a cloud-native application on AWS TCO down 60%, live in four months, no user interruption Case study → Stored procedures to code · Blue Ridge A planning engine of 1,500 stored procedures rebuilt as tested application code 4x faster than a conventional rebuild was projected Case study → Point of view Legacy Modernization with Claude Code: A Workflow for Product-Led Development Read →

Business process

Business rules, workflow, and the operating logic the company runs on.

Capability Expert Workspace The process read with the people who run it, rebuilt as one working context Capability → Point of view The Modernization Trap: Why Old Habits Build Old Systems Read → Medium A People Business Partner With No Coding Background Just Replaced Months of Dev Work Using Claude Read →

Architecture

Application boundaries and the patterns systems use to talk to each other.

Monolith to services · Live Nation A ticketing monolith replaced one bounded context at a time Hundreds of microservices behind every on-sale Case study →

Data

Databases, models, pipelines, warehouses, and governance.

Batch to streaming · Navan Overnight batch syncs replaced by a real-time streaming platform on AWS TCO down 80%, analytics releases 12x faster Case study → Shared MLOps platform · Healthcare enterprise One-off models moved onto a shared platform every new use case starts from New AI use cases start from a platform instead of from zero Case study → Medium Three Levels of Truth: How We Validated a Snowflake → Databricks Migration Read → Point of view Migrating and Optimizing Amazon EMR Workloads: Best Practices from the Provectus Data Team Read →

Infrastructure and cloud

Compute, storage, networking, and how environments get provisioned.

Data-center exit · Revenue-management platform On-premises to AWS, with CI/CD rebuilt and operations automated Release cycles 2x faster, TCO down 40% Case study → Multi-cloud consolidation · Blue Bottle Coffee Workloads spread across providers consolidated on AWS Infrastructure costs down 50%, release speed doubled Case study →

Engineering and SDLC

How software gets built, tested, shipped, and run.

Program Agentic SDLC with Claude Code Agents in the loop, with specs, tests, and review gates Program → Point of view Measuring the ‘Claude Effect’: Provectus’ Metrics Framework for Dev Productivity ROI Read → Point of view Agent Telemetry: Building Traces, Metrics & CI/CD for Claude Agents Read → Medium One Orchestrating Repo: Spec-Driven Development Across a Live Multi-Repo Product Read →

Security and compliance

Identity, secrets, controls, and the evidence auditors ask for.

Compliance environment · Navan A PCI-DSS-ready environment on AWS with the evidence collected automatically TCO down 35%, releases 40% shorter Case study →

The questions

The hard questions, answered.

What if nobody fully understands the current system?

That is normal for systems that have evolved for years. We do not rely on one person or one document to explain them. AI agents read the code, data structures, integrations, jobs, and documentation, while we capture the knowledge held by the people who run the process. Together, that becomes one specification of how the system works today.

How do you know what should actually be rebuilt?

We do not start with a rewrite. The assessment separates business logic that still matters from technical debt, obsolete rules, manual work, and constraints created by the old architecture. The result is a clear decision on what to keep, redesign, automate, replace, or retire.

What if AI gets it wrong?

AI accelerates the work; it does not make the decisions. Recovered logic is reviewed, generated code goes through engineering review and testing, and acceptance criteria are agreed with the business. AI helps us work across much more of the system, but people remain accountable for what is accepted and shipped.

What if the new system does not match the old one?

That is what the Prove step is for. Old and new systems run against the same inputs, and their behavior and data are compared before acceptance. Differences are investigated, explained, or fixed: not simply accepted because the new system “looks right.”

What happens to the business during the cutover?

The old system stays in place until the replacement is proven. Cutover is rehearsed on production-scale data, migration is done in stages where possible, and rollback is tested before the final switch. The goal is to modernize the system without making the business absorb unnecessary operational risk.

How do we know the scope, cost, and timeline?

That is one of the main outcomes of the assessment. Once the current system is understood and the target state is agreed, we can define the modernization scope, dependencies, risks, migration approach, and delivery plan. What started as an open-ended legacy problem becomes a program that can be estimated, priced, and managed.

The system still runs the business. Start by reading it.

An assessment names the layer holding you back, the route that fits, and the cost of the first phase.

Start with an assessment