Capability

AI engineering

AI where it earns its place, ordinary code everywhere else.

The question is rarely whether a model can do something. It is whether the model is the right component, what it costs at volume, and how anyone checks the output. We build the parts around the model that decide whether it survives contact with production.

AI engineering

What makes this hard

A demo that works on ten examples behaves differently on ten thousand.
Cost scales with usage in a way nobody modelled before launch.
There is no way to tell a good answer from a confident one, so nothing can be trusted with a real decision.

How we approach it

Put the model in the right place
Use it for judgement and language. Use ordinary code for arithmetic, lookups and rules, which are cheaper and reproducible.
Evaluation before deployment
A test set that reflects your real inputs, and a number that says whether a change made things better.
Guardrails at the boundary
Validate what comes back against a closed contract, so a wrong answer is an error rather than something rendered to a user.

What you should expect

A system whose behaviour you can measure rather than vouch for.
A running cost that scales with the questions asked, not the data held.

Where this shows up

Guido

The agentic orchestrator the other products run on.

Guido is the workspace where the agents, tools and data behind every Trazup product live. Discovery, analysis, planning and reporting sit in one place, with the same permissions, datasets and audit trail underneath.