Database design and query tuning that keep applications fast as they grow. We design schemas around the shape of your data and index for the queries your application runs most.
People usually come to us in one of two situations. Either something is being built and the data model is worth getting right before it sets in stone, or something already built has slowed down and nobody's sure why. The second is more common, and usually cheaper to fix than expected.
Relational databases where the relationships matter, document databases where records really do vary. Getting this right early costs far less than migrating later, and a wrong choice keeps getting more expensive for months before anyone notices.
The questions that decide the design are mostly about how the data will be read, more than how it will be stored. A model that makes writing easy and reading painful is the most common expensive mistake.
When an application slows down, a handful of queries are usually responsible. Finding them is the first job, and often the whole job. That's a smaller fix than a rebuild, and we'll tell you when that's what we find.
We measure first, because a gut feeling about which query is slow is wrong more often than not. The fix is often an index, a changed join, or a query that was quietly running once per row.
Moving data between systems without losing or quietly corrupting any of it. The part that matters is the checking afterwards: reconciling counts, checking edge cases, and proving the new system says the same thing as the old one.
A migration is judged on what survives it. Planning the checks before the move, and keeping the old system readable until everything reconciles, is what keeps a migration from turning into data loss.
Indexes make reads faster and writes slower, so the useful question is which queries deserve one. Storage decisions work the same way: optimise for what the application does most.
Too many indexes is as real a problem as too few, and harder to spot because everything still works, just with slower writes. Checking which indexes are used is often the first easy saving.
Every project goes through the same five stages (discovery, design, development, testing and deployment), so you always know where things stand. As a guide, a simple website takes about two weeks, web apps three to four weeks, mobile apps one to three months, and custom CRM or ERP systems two to four months. The exact timeline is agreed in writing before work starts.
You deal with one point of contact and get a progress update every day. At handover we deploy the product, set it up, and give you your accounts, your data and a user guide. We keep the source code, and every agreement says so. There's more on how we work on the about page, and the full toolset is on our tech stack page.
Often, yes. Usually a handful of queries are responsible, not the whole system. Finding them is the first job and often the entire job, which makes this a much smaller piece of work than a rebuild. We'll tell you if that's what we find.
It depends on the shape of your data. Relational databases like PostgreSQL and MySQL suit data with real relationships and rules worth enforcing, and MongoDB suits records that vary in structure. Most business applications are better served by a relational database than current trends suggest.
Yes, and much of this work is exactly that. An existing system with a performance problem or a migration coming up is a well-defined job, and it doesn't mean rebuilding what already runs.
Tell us what you're trying to build. We'll set up a requirements session and come back with the scope, our approach, a fixed quote and a realistic timeline. If this isn't quite the right fit, you can see all seven services.