Businesses are struggling with the cost and complexity of legacy technology while keeping operations running and preparing for AI. Our work maps what your systems actually do, removes technical debt, modernizes applications a piece at a time, and proves the result before anything cuts over.
Legacy technology rarely fails outright. It falls out of support, gets slower to change, grows more expensive to run and more dependent on a small number of people, until the systems that once gave the business an advantage become the reason it cannot move. These are the five we hear most often.
Five stages, one piece at a time. The old system keeps serving customers while the new one is proven beside it.
Media & entertainment · Sports streaming
Subscriptions ran on legacy in-app purchases across four app stores, each with its own receipt rules. Sign-in predated the way customers now sign in. All of it had to change on a platform with live subscribers, where no migration could disturb an existing customer and no renewal in flight could be lost.
Building the new platform was never the hard part. Finding every path a purchase, renewal, entitlement check or cancellation could take - before touching any of them - was. AI traced the order and payment flows across the backend first, and that trace became the plan. Each migration case, including a store subscription that had silently stopped charging, was written as its own tested path while the old flows kept running alongside.
80%
faster analysis of legacy code
75%
less production code effort
Five
workstreams running at once
Billing moved with no subscriber-facing incident. Tracing a production flow went from several days to a few hours, five workstreams ran at the same time without the usual drop in quality, and old payment methods were tagged rather than deleted so both billing systems could coexist instead of splitting the customer base.
Financial technology · Rental housing access
The estimate came back at more than 5,000 engineering hours across four concurrent workstreams - well outside budget. Adding people raised cost. Cutting scope shipped a weaker product into a market where the product is the position. Neither lever was acceptable.
DCT applied AI across the whole lifecycle rather than only at code generation: reading requirements straight from the issue tracker, generating tests with each change, reviewing every difference before an engineer opened it, and drafting documentation beside the code. The roadmap shipped without changing the scope or the size of the team.
40%
less engineering effort
40%+
cost vs. estimate
100%
standards adherence
On time, in budget, with no trade. Scope intact, team size unchanged, and every tool provisioned under client-owned accounts so intellectual property never left the client's boundary.
DCT's modernization engagements run inside your team, on your repositories, under your accounts. These are the questions that come up before anyone signs anything.
We read your systems and write down what they do, rank what should change and in what order, rebuild the parts worth rebuilding a piece at a time, and run the old and new together until the behaviour matches. Scope is set by what moves an operational or commercial number, not by a mandate to replace everything.
The first engagement produces three things you keep: a dependency map, a written specification of how the systems in scope actually behave, and a ranked sequence with the evidence attached.
It helps most where the work used to be slow and unrewarding: reading a codebase nobody fully understands, reconstructing what a decade of undocumented behaviour does, writing the specification, generating the tests, and comparing the new path against the old one. On one live programme, that took tracing a production flow from several days to a few hours.
It does not choose your architecture. The only material a model has is the system you are trying to leave, so it can describe that system precisely and tell you nothing about what should replace it. That is why unguided code-to-code translation produces a modern-looking copy of the same problem.
Because proving it is the deliverable, not the last phase. Before a piece is rebuilt, the behaviour of the existing path is captured as tests, edge cases included, and the replacement has to satisfy them.
Then both paths run on the same real traffic and the answers are compared until they agree. Cutover is a decision someone makes after reading the differences. A comparison you can read is what separates modernization from a rewrite you hope works.
No, and often you should not. Replacement is one option among retaining, stabilizing, integrating, replatforming, refactoring and retiring, chosen on technical risk, what the business depends on it for, and what else it is wired into.
Cheap analysis makes this a better decision rather than a bigger programme. It is now affordable to find out that a twenty-year-old system is doing its job and should be left alone.
From the dependency map, not from age or from who complains loudest. Work is ranked on operational risk, what the business depends on, run cost, effort and the order connected systems allow.
The ranking ships with the evidence behind each position, so your architects can argue with it. That argument is usually where the sequence gets better.
No. A person approves every change, and that limit is enforced in the repository through branch protection and automated gates rather than written into a policy.
What changes is where engineering attention goes. Instead of reading every line of a generated difference, engineers review the intent, the plan, the comparison results and whatever the tooling flagged as risky.
That is the point of rebuilding a piece at a time behind the interfaces the rest of the business already uses. The existing path keeps serving customers while the new one is validated next to it, and anything that goes wrong is reversed by routing traffic back.
On a subscription platform rebuilt while it kept charging customers, billing moved with no subscriber-facing incident because every migration case existed as a tested path before release.
They are in scope from the first day. Interfaces, data flows, batch schedules and the operational processes around them are usually where the real risk sits, and they are the first things an undocumented system hides.
Treating this as an application-only exercise is the most common way a modernization programme discovers a problem too late.
Tooling runs under client-owned accounts inside your own cloud boundary. Your code and your data do not pass through a DCT environment and are not used to train models.
Everything the work produces - the map, the specification, the tests, the code - stays in your version control and remains yours if the engagement ends.
Only where the evidence supports it. Most modernization tooling is built by platform vendors whose definition of success is your workload landing on their platform, which quietly turns an engineering decision into a procurement one.
We recommend the target from the dependency map, the run cost and the operational constraints. That includes hybrid, and it includes staying where you are.
