Cloud migration and data integration services
The pipes, the migrations and the observability that everything else quietly depends on.
Integration work is unglamorous and it is where most delivery risk actually lives. Two systems that each work perfectly will still disagree about what a customer is, when a day starts, and whether an order that was cancelled and reinstated counts once or twice.
Cloud migrations fail for a related reason: teams move the machines and not the assumptions. Something was relying on a shared filesystem, or on a cron job nobody documented, or on the fact that two services happened to sit on the same host. Lifting that into a cloud account without finding it first is how a migration turns into an outage.
So the work starts by finding what the current system actually does, rather than what its diagram says — then moving it in pieces small enough that each one can be reversed.
What the work includes
Migration in reversible steps
One service at a time, each with a way back. Big-bang cutovers concentrate all the risk into the single hour you least want it.
Integration between systems
Reconciling an ERP, a CRM, a payment provider and a warehouse — mostly a problem of agreeing on identity and ordering, not of moving bytes.
Data pipelines and ETL
Scheduled and streaming, with backfills that can be re-run safely. A pipeline you cannot replay is one you cannot fix.
Idempotency and retries
Everything that crosses a network boundary assumed to fail sometimes, so a retry does not double-charge a customer.
Observability that predates the incident
Traces, structured logs and alerts wired in as part of the migration rather than after the first outage teaches you what you needed.
Infrastructure as code
Environments you can rebuild from the repository, so the setup lives somewhere other than in one engineer's memory.
Scope against a decision, not a wishlist
The first conversation is about what you are trying to decide or change, and we say plainly which parts of the request we think are wrong. A scope that survives contact with the first sprint is worth more than one that flatters the brief.
Architecture written down before code
The decisions that are expensive to reverse — tenancy model, data ownership, auth, where state lives — get written down and argued about while they are still cheap. You keep that document; it is what lets another team pick the system up.
Two-week increments, working software each time
Every increment ends with something you can open and use, not a status update. If a plan is going wrong you find out in a fortnight rather than at handover.
Handover you could act on without us
Tests, logs, runbooks and the reasoning behind the architecture. We would rather you were able to leave than locked in, and the engagements that last are the ones where that was true from the start.
What it is built on
Questions we get asked
Can you migrate us without downtime?
Usually yes, by running old and new side by side and moving traffic gradually rather than switching at a moment in time. Where a short window is genuinely unavoidable we say so during scoping instead of discovering it on the night.
We do not know what our current system does. Is that a problem?
It is the normal starting point. The first phase is discovery — reading the code, watching real traffic and documenting what actually happens, including the parts nobody remembers building. You keep that document regardless of what follows.
Do we have to move to a specific cloud?
No. We work mainly with AWS and Vercel but the goal is infrastructure you control and could move again. Anything that locks you to one provider gets flagged as a trade-off rather than made quietly.
What about data residency?
It is a design input, not an afterthought. Where data must stay in India or in your own account, that constrains the architecture and we build to it from the start.