The database is the one decision you cannot undo on a deadline.
We design the data layer your product is built on: schema that survives growth, queries that stay fast, and the relational and vector storage your AI features need, set up right before everything depends on it.
Indexed lookup
A query comes in, the index jumps straight to the rows that match, and the answer comes back without reading the whole table.
The problem
Code you can refactor on a Friday. Data, you cannot.
Every other layer of your product is replaceable. You can rewrite the frontend, swap the API framework, restructure the services, and the data underneath survives. The database is the opposite. It accumulates your real, production, irreplaceable data, and by the time the schema turns out to be wrong, you have a million rows depending on the wrong shape and no easy way back.
Most data problems are quiet until they are not. A query that was instant on a thousand rows takes thirty seconds on a million because nothing was indexed. A schema that made sense for the first feature now needs a join across six tables for every page load. A "quick" migration locks the table and takes the whole product offline at the worst possible time. None of it was a problem in the demo.
We design the data layer for the size you are growing into, not just the size you are. The right schema, the indexes that match how you actually query, migrations that run without downtime, and, for AI products, the vector storage and retrieval set up beside your relational data instead of bolted on as an afterthought. Get this layer right early and everything above it gets easier.
Here is the version you might recognize. Your dashboard loads fine. You open it locally, you open it in the demo, it is snappy every time. Then a real customer with two years of history opens the same page and it hangs for eight seconds. You dig in, and the page is not running one query. It is running one query to fetch the list, then another query per row to fetch each row's details, so a list of two hundred items quietly fires two hundred and one trips to the database. The ORM made it look like a single clean line of code. The database saw a stampede. Nobody wrote a slow query; the slow part was hiding behind a loop that read like nothing.
The non-obvious part
The same query is fast and slow. The difference is one index.
A query does not get slow gradually. It works, and works, and works, and then one day it does not, and the code never changed. What changed underneath is the amount of data, and whether the database had a shortcut to find what you asked for. With an index, the database walks a B-tree and lands on the rows it needs in a handful of steps. Without one, it does the only thing it can: it reads every single row and checks each one. That is a full table scan, and it is one of the most expensive things a database will ever do for you.
The scary part is the math, because it hides. An indexed lookup grows like log of the row count, so going from ten thousand rows to ten million barely moves it: a B-tree over a million rows finds a row in roughly twenty comparisons, not a million. A full table scan grows in a straight line with the table. At ten thousand rows that scan is so fast you will never notice it. At ten million it reads a thousand times more, and the same line of code that returned in a blink now sits there for thirty seconds. Same query. Same logic. The only thing that moved was the size of the table and the absence of a shortcut.
This is exactly why the problem never shows up when you are building. Your test data is small, so the scan is cheap, so everything looks fine, so the missing index ships. The bill comes due months later, in production, on your biggest and most valuable customer, because they are the one with enough rows to make the scan hurt. The query you would least suspect, the simple one, the one that has been there since day one, is the one that falls over first, because it was never told how to find anything quickly.
An index is not free, which is the honest catch. Every index you add is another structure the database has to update on every write, so indexing every column to be safe just trades slow reads for slow writes. The skill is not adding indexes. It is reading the actual query plans, seeing where the database admits it is scanning instead of seeking, and adding the few indexes that match the reads your product really makes. You index the questions you ask often, and you leave the rest alone.
How we build it
Designed for how you actually query it.
A schema is not just where data goes; it is what your product can do quickly and what it can never do without a rewrite. These are the principles we design it on.
Schema that fits the questions you ask
We design the schema around the queries your product actually runs, not an abstract diagram that looks tidy on a whiteboard. The data is shaped so the common reads are simple and fast, and the relationships are modeled once, correctly, instead of patched with a dozen lookup tables later. We pick the keys and the table boundaries by writing down the questions the product asks every day and making sure each one has a clean, direct answer. A schema is a long-lived decision: the code that wrote a row gets rewritten many times, but the row outlives all of it, and changing the shape once a million of them exist is a project, not an edit. The structure is the feature.
Indexed for the real access patterns
An index is the difference between a query that returns instantly and one that reads the entire table. With the right index the database seeks straight to the rows it needs in a few steps; without it, it falls back to scanning every row to find the ones that match, and that scan gets slower in a straight line as the table grows. We add the indexes that match how the product actually reads, including the composite indexes that cover a filter and a sort together, so the common path jumps straight to its answer. We also resist the trap of indexing everything, because every index slows down writes, so we index the questions you ask, not the ones you might. Indexes and query plans are part of the design, not something added in a panic when a page starts timing out.
Migrations that do not need a maintenance window
Changing a live database is where a lot of products break. We write migrations that run without locking the table or taking the product offline, applied in safe steps and reversible when they can be. Adding a column with a default, backfilling in batches instead of one giant statement, building an index without blocking writes, splitting one risky change into a sequence of small safe ones: these are the moves that keep the product up while the schema changes underneath it. The migration nobody wants to run is the one on the billion-row table, so we plan that one as a slow background backfill rather than a single lock that freezes the whole product. Schema changes become routine instead of a downtime event you have to schedule for 3am and pray through.
Relational and vector, side by side
AI products need both: the structured data in a relational database and the embeddings in a vector store for similarity search. We set them up to work together, with pgvector or a dedicated vector database depending on your scale, so retrieval for your AI features sits right next to the rest of your data instead of in a disconnected silo. Keeping the vectors next to the relational rows means you can filter a similarity search by the same metadata you already store, so a search stays scoped to one tenant or one document set instead of returning neighbors from the whole table. When the embedding count grows past what pgvector serves comfortably, we move to a dedicated vector database on purpose, sized to the workload, instead of discovering the limit during a launch.
Fast because it was measured, not guessed
We read the real query plans, find the queries that actually cost you, and fix those, instead of optimizing on a hunch. The slow path is found with data, not folklore, so the effort goes where it changes a number the user can feel, not where it looked clever in review. The plan tells the truth: it shows whether the database is seeking on an index or scanning the whole table, where it is sorting in memory versus spilling to disk, and which join is doing the real work. We also hunt the queries that hide, like the clean ORM call that quietly fires one query per row in a loop, because the slowest page is often the one that looks like a single line of code. You cannot tune what you have not looked at, so we look first.
The store chosen for the access pattern, not the conference talk
The right database is the one that fits how you read and write, not the one that was loudest at the last conference. A relational database with proper indexes and joins handles the overwhelming majority of products, and it does it with decades of tooling and guarantees behind it. A document store earns its place when the data is genuinely nested and read as a whole. A key-value store fits a cache or a session table. A vector store fits similarity search. We choose by the access pattern: the shape of the reads, the consistency you need, and how the data grows. Picking a trendy store that does not match your queries is how teams end up rebuilding their data layer twice, once to adopt the hype and once to undo it.
"You can rewrite every other layer of your product over a weekend. The data layer keeps the receipts. So we get the shape right before there are a million rows betting on it."
What you get
A data layer that holds up.
Designed, migrated, and documented so it stays fast and stays sane as the data grows past the demo.
- A schema designed around your real queries, modeled to survive growth instead of just today's feature
- Indexes matched to your actual access patterns, so common reads stay fast at scale
- Migration tooling and a workflow that changes the schema without taking the product offline
- Vector storage and retrieval (pgvector or a dedicated vector database) set up beside your relational data
- Query optimization on the paths that actually cost you, backed by real query plans
- Automated backups and a tested restore, because a backup you have never restored is a hope, not a plan
- The schema, the migrations, and the documentation, so any developer can work with it safely
Have a database that was fine until it was not? Tell us the query that got slow and the table that got big, and we will tell you what is actually going on.
Invoke usIs this the right call
When this fits.
Good fit
- Your queries were fast at launch and now crawl as the data has grown
- You are starting a product and want the schema designed right before it fills with real data
- You are building AI features and need vector search working alongside your existing data
- Migrations scare you, because the last one took the product down
Wrong call
- You have a handful of records and a spreadsheet would genuinely do. Do not build a database for that yet.
- Your schema is solid and you just need one new column added. That is a small change, not a redesign.
- You do not yet know what data you are storing or why. Start with Discovery and figure out the shape first.
Deployment and scale
Backed up, monitored, and ready to grow.
A database that is not backed up is a single bad day away from ending the company, so backups are not optional and neither is testing them. We set up automated backups and actually restore one, because a backup you have never restored is a guess. You get a known, tested path back from disaster, not a checkbox.
Performance is monitored, not assumed. Slow queries are logged, the heaviest ones surface in the metrics, and you find out a query is degrading as the data grows before it becomes the reason a page times out. The database tells you it is struggling instead of just struggling silently.
Growth is planned for. Read replicas when reads outpace one machine, connection pooling so a traffic spike does not exhaust the database, and partitioning when a table gets genuinely huge. We add the scaling pieces when the data calls for them, not as premature complexity on day one.
What we settle before we begin: what data you are actually storing, the queries that have to be fast, and how much the data is expected to grow. Everything else follows from those three.
Tell us what your data looks like, and where it hurts.
Describe the product, the data it stores, and the queries that have gotten slow or the migrations you are dreading. We will tell you what the data layer should look like to stay fast and survive growth, and the shortest honest path to fixing what is there now.