Snowman Labs provides legacy application modernization services for US enterprises that need aging systems to stop absorbing budget and start supporting the roadmap. We pair a compact senior engineering core with AI agents that run assessment, documentation, test generation, and migration work in parallel — modernizing in controlled waves instead of a multi-year rewrite.
If your teams spend expensive engineering time understanding old systems instead of moving the business forward, the legacy problem is already a P&L problem. It shows up as a discovery tax on every change: nobody fully knows what the monolith does, releases wait on manual regression cycles, and critical knowledge lives in two or three heads.
The standard industry answer — a full rewrite scoped in years — is the highest-risk option available. Mid-rewrite, you fund two systems at once and the business gets nothing until cutover; most such programs stall in discovery before a single workload moves.
Modernization should pay for itself in increments. Our entire method is built around that constraint.
We run application modernization consulting and delivery as one engagement, across four workstreams:
Repository and runtime analysis of the systems in scope: dependency mapping, integration inventory, risk ranking, and a wave plan sequenced by business value and blast radius — a defensible answer to “what do we modernize first, and why” before anyone refactors anything.
Agents reconstruct current-state documentation from the code and runtime behavior itself — service maps, data flows, and behavior specs for systems nobody has documented in years — removing the key-person dependency before migration starts. It is standalone value even if you pause after wave one.
Strangler-fig decomposition of monoliths, service-boundary extraction, API wrapping of systems that must stay, and re-platforming of components that shouldn't. Monolith modernization happens one bounded slice at a time; old and new run side by side until the numbers say cut over.
Before any refactor, agents generate characterization and integration test coverage across the critical paths, so behavior is pinned down before it changes. After deployment, runtime intelligence from Hud verifies production behavior at the function level — errors, regressions, and the exact code paths involved — so each wave is judged on production evidence, not a demo.
Most of a modernization budget is spent reading, not writing: reconstructing intent from undocumented code, tracing dependencies, and hand-writing tests for behavior nobody remembers specifying. That is precisely the work coding agents parallelize.
In our delivery model, agents accelerate analysis, implementation, testing, documentation, and repetitive work, while a compact senior core keeps decisions close to execution. We implement and operate Cognition's Devin for repeatable engineering work across repositories — dependency mapping, test generation, mechanical migration steps — and use Replit's enterprise platform where an aging internal tool is better rebuilt than migrated. Humans own what agents should not: architecture, service boundaries, data migration strategy, and the cutover decision.
Every agent-produced change lands as a reviewable pull request with tests attached, under the practices described in our enterprise AI coding governance approach. This is the delivery model behind our agentic engineering services, applied to legacy: it is why we target a 40–60% reduction in time-to-market and a first production milestone within two weeks — numbers a manual-only team cannot structurally reach, because their throughput is capped by headcount.
We deliver modernization as bounded waves, each following the same anatomy shown in the Devin workstream on our homepage (legacy-modernization / wave-01):
Repository and runtime analysis of the slice in scope, so the boundary is drawn from evidence rather than tribal memory.
Integration and characterization tests across the critical paths, written before the refactor, so existing behavior is locked in.
The extraction or migration itself, delivered as pull requests with migration notes, reviewed by senior engineers.
Cutover follows the strangler-fig pattern: traffic moves to the new component incrementally while the legacy path stays live as the fallback. A wave is done only when its exit criteria pass — the generated test suite is green on the new path, production error rates and latency match or beat the legacy baseline under real traffic, documentation reflects the new state, and rollback has been exercised. No wave starts until the previous one exits, and there is no big-bang cutover — no single day on which the business bets everything.
The full reasoning is documented in our roadmap for AI-powered legacy modernization and our guide to modernizing legacy systems without a big-bang rewrite.
400+ software projects delivered, with a 4.9/5 rating across 32 verified Clutch reviews. Two weeks to a first production milestone — the standard we hold every engagement to. 40–60% target time-to-market reduction through a senior core and agents working in parallel. Trusted to deliver for teams at Paraná Banco, Banco BV, RCI Bank, B3, Renault, Scania, iFood, Lockton, Neogrid, Positivo, and Starian.
“The team was very disciplined with dates and commitments, delivering everything with great quality.”
whose roadmap is blocked by a monolith that makes every release slow and every estimate unreliable.
carrying undocumented systems where onboarding takes months and departures are emergencies.
that want AI-powered legacy modernization with governance, not vibes.
who need value shipping in weeks, with every wave individually justifiable.
If your bottleneck is broader than legacy — new product delivery, backlog reduction — start with our AI software development services instead; the delivery model is the same.
Almost never, and we advise against it. A full rewrite forces you to fund two systems for years with no incremental payoff. We use strangler-fig decomposition: extract one bounded slice at a time, run old and new side by side, and cut traffic over only when production metrics prove parity. Rewriting from scratch is reserved for small components where rebuilding is demonstrably cheaper than migrating.
Duration depends on system size and coupling, but you should not wait quarters for results. Our engagements target a first production milestone within two weeks, and every wave after that ships a working increment with its own exit criteria. Because waves are independently valuable, you can stop, pause, or re-scope at any wave boundary without stranding the investment.
It is when the process is built for it. Agents generate test coverage before any refactor, every change lands as a pull request reviewed by senior engineers, and post-deployment behavior is verified against production runtime data. These practices are documented in our enterprise AI coding governance approach, and our delivery record includes mobile banking and legacy integrations work rated by verified clients on Clutch.
Monoliths that need decomposition, aging Java and .NET applications, undocumented internal systems, database-coupled applications, and legacy integrations that constrain everything built on top of them. We also handle monolith-to-microservices modernization where the domain boundaries justify it — and we will tell you when they don't; microservices are an outcome of the assessment, not a default.
Large SIs staff modernization as a pyramid: long discovery phases, layers of handoffs, and pricing proportional to headcount. We run a compact senior team whose throughput is multiplied by agents working in parallel, so discovery, documentation, and test generation happen in days rather than months. Every wave has explicit exit criteria and produces measurable results — you see working software and the numbers every sprint, not a status deck.
Before committing to a program, know what it will return. Our executive assessment maps the current state of your systems, ranks opportunities by value, feasibility, and risk, and gives you a 90-day path to the first measurable results.
Find where you can save time and cost — start your assessment
The assessment deliversCurrent state of your systems, mapped
Opportunities ranked by value, feasibility, and risk
A 90-day path to the first measurable results