Identifying the right use case is half the work.
Most automation projects fail because they target the wrong problem. We start by mapping where manual effort is highest relative to volume, then scope what a working solution actually requires. We build the pipeline, connect it to your live data, and deploy to production. You end up with a system your team runs and understands.
- Working extraction pipeline, production-deployed
- Automated workflow integrated into existing tools
- Monitoring, alerting, and retraining criteria defined
Architecture-first. Every resource has a reason to exist.
We run a full spend audit before anything moves. We map what you're running against what you actually need, design the target architecture in infrastructure-as-code, and plan the migration in phases with a rollback option at each step. Every phase runs live. At the end you have infrastructure you understand, documented in code your team owns.
- Spend audit with line-item reduction targets
- Target architecture in Terraform or CDK
- Phased migration with rollback at each stage
From the decision to decompose, through to production.
We open with an honest architecture review: whether decomposition delivers real value for your actual load, team structure, and release cadence. When it does, we design the service boundaries, event contracts, and data ownership model together. We implement in stages, with fault tolerance sized to where you are and where you're headed.
- Service boundary and event contract design
- Staged decomposition, one service boundary at a time
- Fault tolerance and observability built to your scale
Incremental by design. The business keeps running.
We plan the migration in stages with measurable milestones at each step. New capability is built alongside the old system — the legacy shrinks as the replacement earns its place. We build the API and integration layers that decouple the two, and transfer knowledge throughout so your team holds full ownership of everything built.
- Phased migration plan with clear stage gates
- API and integration layer between old and new
- Knowledge transfer and documentation throughout
E-commerce
Cloud spend
6 weeks
Three over-provisioned AWS accounts consolidated into one. Architecture redesigned before migration. Zero downtime cutover.
Logistics
Saved per week
Invoice reconciliation automated end to end. Document intelligence pipeline processing 400+ invoices daily without manual review.
Fintech
Throughput gain
Payments monolith decomposed into event-driven services. Latency cut from 800ms to under 90ms at peak load.
Most businesses migrated by lifting and shifting what they had. The architecture came across unchanged. The sizing was a guess. The result: infrastructure bills three to five times higher than they need to be, running on a setup the current team inherited rather than designed. The costs compound and the causes stay buried.
Companies are buying into chatbot wrappers and AI-branded dashboards that address surface symptoms. The real bottlenecks still cost hours every day: someone manually keying data, reconciling spreadsheets, re-reading the same document three times to extract two numbers.
The ERP from 2014 still runs. It resists integration with anything modern, and each change request opens a risk assessment that stretches to months. Maintenance costs climb. The capability gap widens.
Systems that handled low volumes just fine start breaking as the business grows: timeouts, data inconsistencies, single points of failure. By the time you know you need this expertise, you're usually already in the incident.
AI earns its place when it removes a specific, repeating cost. Here is what that looks like in practice.
Three people reconciling invoices against PO numbers every week
AI cross-checks in seconds, flags exceptions, queues the rest for payment
Support inbox backlogged by Thursday every week
AI handles the standard queries. Your team focuses on the cases that need judgement.
Compliance team reading every contract to extract the same three fields
Model extracts them in bulk. Structured output, ready for review.
Finance team pulling numbers from five different systems every Monday
Single query on live data. The report generates itself.
We spend one to two weeks looking at what you have, what it costs, and where the real problems are. We tell you what we found, including when the honest answer is that outside help isn't what's needed.
We take one contained problem and solve it. Scoped before we start, deadline on the calendar. You evaluate the result on your own systems, with your real data.
If the proof of value delivered, we expand. You decide how fast and on which problems. You've seen results before committing to anything broader.
We stay involved after delivery. As the engineers who built it, staying close to what ships. When something needs adjusting, we adjust it.
Cloud infrastructure and telecom-scale platform engineering.
Systems serving hundreds of millions of users.
We design for edge cases from the start. A 0.1% failure rate isn't acceptable when the volume is high enough.
Financial services and insurance platform architecture.
Regulated, high-stakes, zero-downtime environments.
We know how to ship where mistakes are expensive. Rollback plans and compliance checkpoints are scoped in from week one.
Real-time geospatial data systems.
Distributed processing and low-latency delivery at global scale.
When your bottleneck is throughput or latency rather than cost, we've solved it at a scale most teams never reach.
We run our own production cloud. That means we've had the 2am incidents, the cost spikes that needed explaining, the scaling events that broke something unexpected. When we recommend an architecture to a client, we've already run something like it ourselves.
Convoy Cloud ↗Tell us what you're working with. We'll spend an hour understanding it and give you an honest read on what we'd do and what it would take.