Sales, support, and compliance are just three examples — the same mechanism applies to onboarding, collections, renewals, internal support, surveys, recruiting, or any conversational function on the team, customer-facing or internal. We help identify where an AI agent belongs, or how to improve one that already exists.
These are illustrative — not the only functions we cover. If a person is already resolving it over chat, email, or forms, we can likely clone it.
Guides activation and setup steps, and confirms when the customer is ready to use the product.
Reminds about due dates, negotiates payment plans, and confirms agreements — with the same judgment the collections team already uses.
Detects an upcoming renewal, offers the right plan, and handles cancellation objections.
Resolves the team's own requests — access, licenses, internal policies — before they reach a person.
Collects NPS or CSAT after an interaction, and digs into negative responses before closing them out.
Screens candidates with the right questions, schedules interviews, and keeps candidates updated at every stage.
Books, reschedules, and confirms appointments directly on the team's calendar, in any industry.
Completes a request or registration end to end in a conversation, instead of a long form.
Answers process or policy questions from the same source the team already uses — no digging through five documents.
We don't start from a template built for one specific function. We start from how the best person on the team already resolves that particular task, and clone exactly that — whether it's onboarding, collections, or anything else.
We capture how the best person on the team actually resolves the task — tone, judgment and all — regardless of which function it is.
WhatsApp, email, web chat, or your own app — the same assistant, the same context, on whichever channel is already in use.
No setup costs, no hidden integration fees — pricing aligned to results from day one.
LLM security, agent security, client control, and infrastructure aren't the same layer — each protects something different, and the agent leaves a trail of everything it does along the way.
A language model can make the wrong call — delete something it shouldn't, invent data that doesn't exist (like a bank account number), or let spend spiral out of control. Guardrails validate what the agent is about to answer before it answers, automatically or manually, covering hallucinations and prompt injection.
We can host the guardrail we use today so your security team can test it directly, instead of taking our word for it.
The logic that decides what information to bring into each conversation lives in code, not in the model — it's not a guardrail, it's the default architecture. A deterministic agent can't cross data between users or reach beyond what belongs to that conversation.
Every integration goes through a middleware layer the client configures — StudioChat can't see the data the client chooses not to share. Control stays on the client's side, by design.
Encryption in transit and at rest, retention configurable by the client, two-factor authentication for every user, and full traceability of every change made to an assistant — versioned, with a record of who did it.
The most common fear is that the agent does something and no one finds out. That's why every answer comes with its reasoning and its citations — no black box acting without leaving a trace.
"We managed to cut ticket handling time down to just one minute, without losing the judgment the team already used to solve it."
Any function that's conversational, moves documents or information, or includes a step where a person is involved — for example: onboarding, collections, renewals, internal IT or HR support, surveys, recruiting, scheduling, and data collection. Actual coverage depends on what the team already handles today, not a fixed list.
It helps, but isn't required. We start from how the best person already handles the task today — documented process or not — and build and refine from there.
Yes. The same assistant can cover several functions at once — for example, sales and onboarding, or support and satisfaction surveys — if that's how the team already runs it.
Outcome-based: you pay per resolved task, not per seat, not for setup.
Yes — Takenos, a LatAm digital banking platform, proves the mechanism in production: the same assistant resolves 70% of its support autonomously, keeping the judgment the team already applied. The same approach applies to any other conversational function.