Turn your monolith into an AI-ready system. No rip-and-replace. No downtime.
We re-architect legacy platforms one service at a time. AI becomes part of the architecture, not a demo bolted on the side. You keep shipping features the whole way through.
You have a platform that makes money. It also slows you down.
The code grew for years. It works. It also fights every change you try to make. New features take longer each quarter. One deploy can break three things.
Now your board wants AI. So you try to plug it in. It does not fit. Your data is locked inside the monolith. Your services cannot talk to each other in real time. The AI demo looks great in a meeting and dies in production.
Here is the trap. You cannot pause a revenue-generating system to rebuild it. A rewrite from scratch is the most expensive mistake in software. So most teams freeze. They bolt AI on the edge and hope.
That is not modernization. That is a band-aid. If this sounds like your platform, you are who we build for.
AI-first does not mean an AI feature. It means an architecture AI can actually use.
We modernize the system while it keeps running. One piece at a time.
Incremental service extraction (strangler-fig)
We pull high-value domains out of the monolith and turn each into its own service. The old code keeps serving traffic until the new service is proven. Then we route over. The monolith shrinks. Nothing stops.
Event-driven re-architecture
We connect your services with an event backbone (Kafka). Systems publish what happened. Other systems react. This is also what AI needs: a live stream of events to read from and act on. We use CQRS where reads and writes pull in different directions.
Data layer modernization
AI is only as good as the data it can reach. We unlock data trapped inside the monolith and shape it so models and pipelines can use it. Clean events in. Useful answers out.
Frontend migration to a modern SPA
A modern backend deserves a modern front end. We migrate legacy UIs to React, screen by screen, without freezing the product.
Mirror-mode and zero-downtime cutovers
Before we switch anything live, we run the new service in mirror mode. It handles real traffic in the shadows. We compare its output to the old system. Only when it matches do we cut over. Your users notice nothing.
Strangler-fig: monolith to AI-ready services
Domains peel off the monolith into services on an event backbone, an AI layer subscribes to the stream, and mirror-mode traffic validates each step before cutover.
We have done this on systems we were not allowed to break.
We have done this on systems we were not allowed to break.
An eight-year monolith-to-microservices migration at Allego. Allego is a market-leading revenue enablement platform, used by a quarter of Dow Jones Industrial Average companies. We grew from one engineer to a 31-person team and re-architected the platform with the strangler-fig pattern. We extracted domains into independent services on a Kafka event backbone. We migrated the front end to React. All of it incremental. All of it zero downtime, with no pause in feature delivery. On top of that re-architected base, we then built AI: conversation intelligence and an LLM-powered virtual avatar for sales roleplay. That is AI as infrastructure, not a bolt-on. Read the Allego engineering partnership case study.
Zero-downtime migrations on live traceability systems. As a core contributor to OpenEPCIS, we built a conversion layer that lets legacy and modern systems run side by side. Old formats and protocols on one end. Current standards on the other. The bridge handles translation in flight, then steps aside as systems upgrade. No big-bang migration. No cliff edge. See how we modernize legacy traceability without breaking interoperability.
The proof is the same in both cases. We migrate live, revenue-critical systems without downtime, and we keep delivering.
We use production-proven tools, not whatever is trending.
These are the same tools we run in production for clients across North America and Europe. See the rest of what we do.
Start small. Prove it. Scale into a team that owns it.
You do not have to bet the platform on day one. A fixed-fee assessment maps your architecture, finds the risks, and hands you a roadmap. A fixed-scope pilot extracts one service or one AI use case into production. Then a senior team owns modules end to end, for years. This is the Allego model.
- 01 Assess
Architecture & Readiness Assessment
A fixed-scope engagement. You get an architecture blueprint, risk analysis, roadmap, and ROI estimate.
- 02 Prove
Pilot / Proof-of-Value Build
One real use case, shipped to production, with monitoring in place.
- 03 Scale
Embedded Engineering Partnership
A senior team owns modules end to end, for years. The Allego and benelog model.