Software modernization services that end with the old stack turned off.
We rebuild the legacy module, migrate its data and customers, and hand it over documented and tested — without pulling your core engineers off the roadmap.
Official services partner of the platforms defining AI
NVIDIA
Anthropic
Legacy application modernization starts with the module nobody understands anymore.
From two anonymized calls: a product-led SaaS CEO whose last legacy module keeps a $40K-a-month stack alive, and a data lead at a 95%-on-prem wealth manager. Different estates, same ask — rebuild what matters, migrate the data, turn the rest off.
One module holds the whole stack hostage
A product you no longer want to support is the last thing running on a legacy stack costing $40K a month. It is an all-or-nothing light switch — the stack cannot go off until the module lives somewhere else.
Nobody understands the old system anymore
The people who built it are gone, and the business rules live in the code. Every change is archaeology — and no AI agent can automate work on a system nobody can explain.
Your core engineers cannot be the ones who fix it
Pulling your team off the new-platform roadmap to rebuild the old world costs you twice — once in the rebuild, once in the roadmap that slips while they do it.
The stack is a cost drag and a security drag
In the buyer’s words: “both a big cost drag on the organization and mild security things holding us back.” Every compliance review keeps circling a stack you want gone.
Or the whole estate is on-prem and fragmented
The wealth-tech version: 95% on-prem, two data warehouses, multiple CRMs, five versions of the same table — “aspirations to get out of 1999.” AI agents have nothing clean to stand on until the data is gold rather than bronze.
Rip-and-replace quotes miss the point
You do not need to bulldoze everything and start again, and you do not need to boil the ocean. You need one module rebuilt, the data migrated, and the old stack turned off — then the AI work can start.
The old stack bills you every month for standing still.
The meter runs every month the module stays where it is — and none of that spend moves the roadmap forward.
From anonymized calls — a product-led SaaS CEO (~1,000 buildings on the platform) and a wealth-tech data lead
Rebuild it, migrate the data, turn the old stack off.
A fixed-scope rebuild run by an outside team, inside your engineering standards. Legacy system migration without the big-bang rewrite of everything.
- 01
Map what the legacy system actually does
AI agents read the legacy codebase, the database, and the integrations alongside our engineers — every business rule, edge case, and report the old system quietly produces. The map becomes the rebuild spec, with nothing tribal left in it.
- 02
Rebuild the functionality, migrate the data
We rebuild the module on a clean, current stack — AI-accelerated, engineer-reviewed — and migrate the legacy data and customers across. The work lands in your repos, your CI/CD, your SDLC, so what ships is already yours.
- 03
Hand it over clean, then turn it off
Full documentation and an automated test suite come with the handover — the safety net that means your team owns it from day one. Then the old stack goes off, and the monthly bill stops.
Legacy software modernization without the next maintenance nightmare.
Application modernization services from a dev shop end in code and a departure. The buyer’s objection is our design brief: “I don’t want to hand off one maintenance nightmare for another.” Every engagement ends in a clean handover — documentation, automated tests, and a walkthrough — built to your SDLC and ISO 27001 controls, with role-based access and SSO where you require them.
And we build AI systems for a living. AI agents do the reading and the grunt work — extracting business rules from legacy code, drafting the test suites and docs — which is how the rebuild moves at AI speed without vibe-coding your production system. Once the stack is clean, those same agents are what plug in next: modernization is where automation starts.
I don’t want to hand off one maintenance nightmare for another.
Led by Peter Enestrom.
On-prem to cloud migration, data migration services, and other questions.
Straight answers, before you book anything.
It means moving the software your business still depends on off the stack that is holding it hostage. In practice, most buyers mean something plainer: rebuild the functionality on a current stack, migrate the data and customers across, and turn the old system off. Legacy software modernization done this way ends with a light-switch moment — not an eternal migration.
No. AI agents do the reading and the drafting: extracting business rules from legacy code nobody understands anymore, mapping the data underneath, and generating the first pass of tests and documentation. Engineers review everything that ships. That is how the rebuild moves faster than a manual rewrite without betting your production system on unreviewed AI output.
Often, yes. Legacy system integration — an orchestration layer over the systems you keep — is frequently the right first move: connect the disparate sources, wind down the legacy software people are not using anymore, and rebuild only the modules that justify it. Don’t boil the ocean.
No — that is the point of bringing in an outside team. The rebuild runs alongside your roadmap, not instead of it: our engineers do the legacy work inside your repos and your ceremonies while your core team keeps building the new platform. Your team reviews at the milestones that matter; it does not carry the rebuild.
Everything your team needs to own it: full documentation, an automated test suite, and a guided walkthrough, all inside your repositories. The work is done to your SDLC, secure-coding standards, and ISO 27001 controls — role-based access and SSO included. After handover you can maintain it yourselves — that is the design goal — or we stay on as your AI operating partner.
Yes — data migration services are part of the rebuild, not an afterthought: the legacy data and customers move across, cleaned before they land, because moving swampy data just relocates the swamp. For estates that are mostly on-prem, on-prem to cloud migration runs the same way — module by module, in phases, with the old systems wound down as the new ones take over.
It gets turned off — that is the success metric. After a parallel-run period, legacy system decommissioning retires the old stack: the infrastructure bill stops, the security surface shrinks, and the technical debt reduction shows up in every sprint after. Replacing a legacy system is only finished when the old one is off.
Start with one workflow.
Tell us where your team loses hours. We will come back with a straight answer on whether AI can help, what it would take, and what it would pay.