Know it works before you ship. Know it still works after.
Most AI projects fail on the question nobody can answer: is it good enough, and is it still? We build evaluation suites from your real cases, red-team the system, put guardrails around it and watch it in production, so every change is measured and nothing regresses quietly.
Zingaro AI builds evaluation, red-teaming and guardrail systems for AI applications: task-based eval suites, LLM-as-judge with human calibration, adversarial testing, input and output guardrails, and production observability with drift and regression alerts.
You get
- An eval suite your team can run
- A red-team report with fixes applied
- Guardrails in production
- Dashboards and alerts
Built with
- Task-based evals
- Calibrated LLM judges
- Adversarial testing
- Tracing and sampling
- Drift alerts
Evals, red-teaming and guardrails, end to end.
Evaluation suites
Task sets from your real cases, scored by people and by models calibrated against them, run before every change.
Red-teaming
Adversarial testing for prompt injection, data leakage, unsafe actions and off-policy answers.
Guardrails
Input and output checks, tool permissions, refusal rules and rate limits that fit the risk of each action.
Observability
Traces for every request, quality sampling in production, cost and latency dashboards.
Drift and regression alerts
Know when the data changed, the model changed or the quality dropped, before your customers do.
Governance
The evidence a regulator, an auditor or a board asks for, produced as a by-product of running well.
From one painful job to a system in production.
Models and agents handle the volume. People handle the edges. You always know which did what.
- 01
Discover.
One or two weeks with your team. The jobs listed, sized and ranked. A number on the first one.
- 02
Build the pilot.
Fixed scope, fixed fee, 4 to 6 weeks. Real output on your real data, measured against the number.
- 03
Ship it.
Into production, inside your boundary if the rules require, with evals gating every release.
- 04
Run it, or hand it over.
We operate it with people on the queue and a weekly report, or your team takes it with the runbooks.
Your data does not have to leave the building.
On your servers
Air-gapped where required.
We install on machines you own, inside your network. Where the rules demand it, the system runs with no outbound connection and updates are carried in by hand. Your team keeps the keys.
Data stays inside your network
Your private cloud
We deploy into your account.
We deploy into your own cloud account, in your region, under your access controls. The data stays in your account. We get the access you grant, and nothing more.
Data stays inside your account
Ours
Managed, fastest to start.
We run it on infrastructure we operate. The right choice when the data is allowed to leave and you want the pilot running this month.
Managed by us, on our terms
In banking, insurance and healthcare, the rules decide where the data sits. We built for that first.
How each option worksWhat comes back, and how we measure it.
Deliverables
- An eval suite your team can run
- A red-team report with fixes applied
- Guardrails in production
- Dashboards and alerts
Measured by
- Pass rate on the eval suite
- Regressions caught before release
- Guardrail interventions and their causes
- Time to detect a quality drop
Real figures come from your pilot. We do not publish invented ones.
What people ask about evals.
Can we use LLM-as-judge?
Yes, once it is calibrated against human judgements on your cases. Uncalibrated judges measure the wrong thing confidently.
Do you work on systems you did not build?
Yes. Evals and guardrails are often the first thing we do on an existing system.
What about prompt injection?
It is on every red-team list. Tool permissions, input isolation and output checks are how it is contained, not a single filter.
Bring us the evals job you keep postponing.
Twenty minutes is enough to say whether we can take it.
A pilot starts within 5 working days of agreed scope · Nothing upfront · No seat licences