Evaluation Suites
Offline and ongoing evaluation that measures whether the AI does its job, so quality is tested rather than assumed, and regressions are caught before they ship.
Applied AI Engineering
AI aimed at a real problem, engineered to hold up under real use.
What this is
Applied AI engineering is the discipline of taking AI from an interesting capability to a dependable part of a system.
It is less about the model and more about everything around it: the evaluation that proves it works, the guardrails that keep it safe, the observability that shows what it did, and the cost control that keeps it viable at scale. Fintechy does this engineering so that AI in your business behaves the same on a bad day as it did in the demo.
What We Build
Guardrails and Safety
Prompt-injection defenses, output validators, and safety checks that keep the system inside its intended behavior even under unexpected input.
Observability and Tracing
Trace-level logging so you can see exactly why the AI produced a given result, which turns a black box into something you can debug and audit.
Model Selection and Cost Control
Model routing, caching, and latency-aware design, so the system stays fast and its economics stay predictable as usage grows.
How We Work
We define what good output means for your problem, build the evaluation to measure it, and wrap the system in guardrails and observability from the start. You get AI you can trust in production because you can see and test how it behaves.
Define good output
What good output means for your problem.
Build the evaluation
The evaluation to measure it.
Wrap it in guardrails
Guardrails and observability from the start.
Explore More
Have an AI idea that needs to survive real use. Let's engineer it properly.
Book a callcalendly.com/fintechy/30min
What we cover
The problem, and what good output means
How we would evaluate and guard it
What a first build would take