Technologies
An AI system is only as good as what it can look up. Most disappointing results are not a model problem — they are a data problem: the record was stale, the schema could not express the question, or the query was too slow to wait for.
From modelling a new schema to rescuing one that has outgrown its assumptions.
A data model built around the questions you need to ask, not just the forms you fill in.
Finding the queries costing you the most and making them stop.
The lookup layer behind grounded AI answers, built for accuracy over novelty.
Changing the shape of live data while the product keeps serving traffic.
Keeping systems that disagree in agreement.
Separating the questions analysts ask from the database serving your product.
Knowing your backups work before you need them.
Engineers who join your team rather than work around it.
Data problems compound quietly. By the time they are visible they are expensive.
Grounded AI answers are only as good as the passages fetched to ground them. Poor retrieval does not produce an error — it produces a confident answer built on the wrong document.
The model fit the first feature. Several features later every query works around it, and the migration that would fix it feels too risky to attempt.
Queries are fine at ten thousand rows and unusable at ten million. Nothing was wrong with the code — the access pattern simply stopped matching the index.
Chosen for what a project needs, not for novelty. Everything here is something we run in production and can support.
Six stages, each ending in something you can review. No long silences between kickoff and delivery.
01
We profile what you have — volumes, access patterns, slowest queries and where the data disagrees with itself — and agree what good looks like in numbers.
02
Schema, indexes, retention and the split between transactional and analytical workloads are decided up front rather than discovered under load.
03
The new shape proven against a realistic copy of your data, including the queries you care about most, before anything touches production.
04
Delivered in reversible steps. Expand, backfill, verify, then contract — with the product serving traffic throughout.
05
Row-level reconciliation, query plans re-checked at volume, and a rehearsed restore so the backup is known to work.
06
Monitoring on growth, slow queries and replication lag, plus a documented handover so your team can run it without us.
We are a software engineering team that works on AI, not an AI team learning to write software.
01
The same team builds the services and interfaces on top, so the schema is designed against how it will actually be queried.
02
Every step has a rollback that has been rehearsed. We do not ask you to accept a cutover with no way back.
03
Search quality is evaluated against a labelled question set, so improvements are demonstrated rather than asserted.
04
Performance work is validated against production-shaped data. Numbers from a seed database tell you nothing useful.
05
Standard engines, standard formats, no proprietary storage layer. Exporting everything is always a supported path.
06
Your repository, your migrations, your documentation. Nothing here requires us to stay.
Tell us what you are building or what is slowing down in what you have. We will come back with an honest view of the effort involved and where we would start.
