Query performance tuning
Finding and fixing the queries that are quietly slowing everything down.
A slow query that runs fine on a demo dataset can bring a real system to its knees at scale. We tune databases and design schemas for the load you'll actually have, not the sample data a demo gets built against. That covers indexing strategy, query optimization, and replication and backup planning across relational and NoSQL systems alike. We profile actual production query performance before touching anything, so fixes target what's really slow, not a guess. Whether it's a system that's degraded over time or a new schema being designed from scratch, the goal is the same: performance that holds as your data actually grows.
Database performance problems rarely show up in development. They show up months after launch, when the data volume is real and every query that used to be instant is suddenly not.
We do performance tuning on existing systems and data architecture design for new ones: indexing strategy, query optimization, and schema design that holds up as your data grows, not just at launch.
Finding and fixing the queries that are quietly slowing everything down.
Structured for how your data actually grows, not just how it looks at launch.
The difference between a query that takes 10ms and one that takes 10 seconds.
Data that survives a failure, tested, not just assumed to be backed up.
An honest assessment of what's actually wrong before we touch anything.
Moving from one database engine to another without losing data integrity.
We profile actual query performance before recommending changes. Tuning based on assumptions instead of data usually makes things worse. That means running diagnostics against a copy of real production data, not a synthetic benchmark that doesn't reflect your actual query patterns. Indexing and schema changes are tested for their effect on write performance too, since a fix that speeds up reads but silently slows every write isn't actually a fix. Every change is applied in a way that's reversible, with a rollback plan ready before anything touches production data.
Usually, yes. Most performance problems are query and indexing issues, not fundamental architecture problems.
Yes, MongoDB and Redis alongside traditional relational databases, depending on what fits the workload.
Yes, an audit is a standalone engagement if you just want the assessment.
Changes are tested against a copy of production data first, and applied in a way that's reversible.
Yes, the right answer depends on your actual data shape and access patterns, and we'll give you a straight recommendation rather than a default.
Both: a one-time audit and fix, or ongoing monitoring so performance regressions get caught before they become an incident.
Get a response within one business day
Whether you need a brand-new build, a redesign of something that's already live, or a second opinion on a project another vendor left behind, our in-house engineering team scopes the work honestly, keeps you updated sprint by sprint, and stays reachable after launch instead of disappearing once the invoice is paid. Fill in the quick form and we'll reply within one business day, or book a free 30-minute architecture review if you'd rather talk it through first. No obligation, no sales pitch.