The domain where a confident wrong answer causes real damage
GRADOS supports an organisation covering around 82 schools. The same procedural questions arrive constantly, in Spanish, about systems and statutory filings with real legal consequences if answered wrong.
A support agent that answers those questions is easy to build and dangerous to deploy. Payroll and statutory filing is precisely the domain where a confident wrong answer costs someone money or puts them out of compliance.
The requirement wasn't an agent that answers everything. It was an agent that answers what it should and refuses what it shouldn't.
Grounded, classified, and designed to escalate
Grounded
Answers come from a curated knowledge base of internal procedural documentation, not from model recall. Sources are returned with every response.
Classified
Each question is classified before it is answered. The classification, the confidence and the outcome are all logged.
Escalating by design
Hands off to a person on low confidence, personal data, complaints, or requests for an action rather than an answer. Certain trigger phrases escalate every time, regardless of what the model's confidence says.
Aware of context
Business hours, holidays and out-of-hours fallbacks are handled explicitly, so nobody gets an instant answer at 2am implying a human is standing by.
Never talks over a human
Once a conversation is handed to a person, the agent stops. It does not auto-reply into a live handoff.
The handoff rules aren't a fallback. They're a boundary.
They are not there for when the model fails. They're a designed boundary around the class of questions that should never be automated at all, no matter how confident the model is.
Personal data requests. Complaints. Anything asking the system to do something rather than explain something. Those escalate on category, not on confidence.
It learns on a schedule
The agent runs a recurring review of its own work. On each cycle it reads back the period's conversations and reports on itself.
How many conversations it handled, and how many it escalated
Which answers ran below the confidence threshold
Which questions retrieved no usable source at all — the clearest signal of a genuine coverage gap
Performance by question classification, and coverage by source document
It also detects corrections. When an operator edits a draft before it goes out, the system flags it two ways: when the proportion of changed text crosses a threshold, or when a critical token changes. A small edit to an important value counts as much as a heavy rewrite, because a changed figure matters more than a reworded sentence.
Then it drafts the knowledge it thinks is missing, as proposed question-and-answer entries aimed at the gaps it found.
And then it stops. Drafts land in a review queue alongside the low-confidence cases. A person approves, edits or rejects each one. Nothing reaches the knowledge base without human sign-off.
A system that quietly rewrites its own knowledge is a system nobody can audit. This one improves continuously and still leaves a person in charge of what it is allowed to know. The agent proposes. It does not edit itself.
WhatsApp, properly
Deployed on WhatsApp and as an embeddable web widget. For Spanish-speaking users, WhatsApp is not an alternative channel. It's the primary one.
The integration is a proper Meta Graph API implementation with signature verification, and it carries the same handoff rules as every other channel.
Any support function where being wrong is expensive
Regulated industries, financial services, healthcare administration, compliance, HR. The differentiator is not answer quality. It's the documented, auditable boundary around what the system is permitted to answer at all.
Need an agent that refuses well?
Most of the value is in what it declines to answer. Let's talk about where your boundary sits.