The engineering behind it
The pitch is simple. The hard part is everything that has to be true for that one input box to give a trustworthy answer.
Data quality before anything else
Aggregating different sources means dealing with duplicates, locations written in dozens of formats, and contradictory information. The value of the platform is the coherence of the catalogue, and that is built with systematic checks, not with aggregation alone.
Trustworthy generated content
When you run language models over data that goes public, the risk is not that they get something wrong, it is that they get it wrong in a believable way. Every piece of automatically generated information passes a verification step before it becomes visible. It is the same discipline we write about in how to reduce AI hallucinations.
Performance measured, not guessed
One section of the platform took over a minute to load. Profiling the real request path showed the cause was not where anyone would have looked first. Fixed at the source: over thirty times faster.
Operational continuity
Encrypted backups with automated restore verification, scheduled jobs with tracked outcomes, and no dependence on a single external provider for critical functions. The catalogue refreshes week over week with little to no manual work.
The stack
Built and shipped as one full-stack product, from the AI matching engine down to the infrastructure it runs on.
Next.js 16
React 19
TypeScript
PostgreSQL
Docker
Cloudflare
Stripe
The kind of system we build
Galviq brings together an AI matching engine, a production data pipeline and a full-stack product. It is the same work we do when we build AI agents, retrieval systems and custom LLMs, with the same discipline about keeping generated output trustworthy. If you have a product like this in mind, we can build it.