Legacy assessment
Mapping code, data flow, and hidden business rules in mainframe and monolithic applications before anything is touched.
Our specialization
Reengineering legacy applications into agentic, cloud-native systems.
This is the work our name describes: taking the winding, undocumented path a legacy system has grown along, and redrawing it deliberately — as something modern, observable, and ready for AI.
Core capabilities
Six disciplines, applied together.
Modernization fails when these are treated as separate projects. We run them as one program.
Cloud-native re-architecture
Decomposing monoliths into services, containerizing workloads, and rebuilding on Kubernetes-based infrastructure.
Agentic AI integration
Embedding agents into the re-architected system itself — not as a separate chatbot bolted onto the old UI.
Workflow automation
Replacing manual, ticket-driven processes with automated and agent-assisted workflows that people can still override.
Data modernization
Untangling siloed, poorly documented data stores into models that both applications and AI systems can trust.
DevOps & MLOps enablement
CI/CD, infrastructure as code, and model/agent evaluation pipelines so releases stop being an event.
What's actually underneath
Racks, not roadmaps.
A modernization plan is only as good as the infrastructure it lands on. We design for the real constraints of the environment your system will actually run in — not a reference architecture.
5Typical migration slices
0Big-bang cutovers
Migration approach
We move the path, not the whole mountain at once.
Every modernization follows the same sequence — scaled to the size of the system.
01
Map the system as it actually behaves
Static analysis, log mining, and interviews with the people who operate it today — legacy documentation is a starting point, not the truth.
02
Carve out a first slice
Identify a bounded piece of functionality — one domain, one workflow — that can move independently and prove the approach.
03
Run old and new in parallel
Strangler-pattern routing sends traffic to the new service while the legacy path stays live as a safety net.
04
Introduce agentic workflows
Once the slice is stable on cloud-native infrastructure, agents are layered in against real, observable data.
05
Repeat, then retire the legacy path
Slice by slice, until the legacy system has nothing left to run — and is decommissioned deliberately, not abruptly.
Every migration slice needs a way to see what actually movedThe shape of the change
What actually moves.
Before
Monolith, on-prem
- Single deployable, single point of failure
- Releases scheduled quarterly, high risk
- Business rules encoded only in code, undocumented
- No practical way to add AI without a rewrite
After
Services, cloud-native, agentic
- Independently deployable services on Kubernetes
- Continuous delivery, small and reversible releases
- Rules extracted into explicit, testable policy
- Agents plug in through the same APIs your team uses
Have a system in mind?
Let's map it together.
A short assessment tells you what's actually feasible — before any commitment to a full program.