We are not an AI company. We are database engineers who use AI tooling the right way — to move faster, surface patterns earlier, and give you better answers. The judgement is still ours.
The database world is changing. AI assistants are being asked to diagnose slow queries, review schemas, and suggest configuration changes. Most of them get it wrong — not because the models are bad, but because they lack grounded, accurate PostgreSQL knowledge at the point of inference. We're building the infrastructure to fix that, and offering it as part of our consulting practice now.
We set up and configure a PostgreSQL MCP (Model Context Protocol) server for your environment — letting AI assistants like Claude or Cursor connect directly to your database, read live query plans, and inspect pg_stat_statements.
The result: your AI assistant stops guessing about your database and starts working with actual evidence — the same query plans and statistics we'd look at ourselves, not a general impression of what a database "usually" looks like.
Generic AI models give generic database answers — often wrong for your specific PostgreSQL version, cloud provider, or workload. We build a retrieval-augmented generation layer grounded in accurate PostgreSQL documentation, your own runbooks, and curated expert knowledge.
Your team gets an assistant that actually knows your stack — not one that hallucinates a pg_tune-style recommendation that happens to be wrong for your major version or your managed-cloud provider's specific limitations.
We combine our manual diagnosis with AI-powered pattern matching across your query history, wait events, and configuration drift. This lets us surface problems faster — not replace the engineer who knows what to do with them.
Think of it as a force multiplier: the same expert depth, with better visibility across your entire workload at once — useful when the volume of queries or the number of instances makes manual review alone too slow.
Every AI-assisted recommendation is reviewed by one of us before it reaches you. We don't relay a model's output as-is — if we wouldn't sign off on it ourselves, it doesn't go out under our name.
We've spent time benchmarking AI recommendations against real workloads. Some of it holds up; a meaningful amount doesn't, especially anything touching write-path parameters, replication behaviour, or version-specific quirks. We tell you which is which rather than presenting all of it with equal confidence.
Any AI tooling we set up against your database defaults to read-only, scoped access. If a change needs to happen, a person makes that call and applies it — not an autonomous process against your production system.
Whether it's setting up an MCP server for your own team's AI tools or getting a second opinion on something an assistant already suggested, we're happy to talk it through.
Talk to us →