Every unanswered message, is a sale going cold.
The best salesperson and support agent, cloned across every channel — before, during, and after the purchase.
Every task in the conversation, handled with AI — not just after the sale.
From the product question that decides the sale to the technician visit that closes the ticket — it all gets handled with AI across any channel, instead of getting lost between tools.
Order and shipping status
Pulls live carrier and OMS data to answer 'where's my order' instantly — no ticket, no wait.
Returns and exchanges
Walks the customer through eligibility, generates the label, and confirms next steps — the same flow the best CX agent already runs.
Electronic invoicing
Handles invoice requests end to end, adapted to each country's requirements — SAT in Mexico, DIAN in Colombia, SUNAT in Peru, AFIP in Argentina, and more.
WhatsApp, Instagram, MercadoLibre, and more
Replies wherever customers already are — WhatsApp, Instagram DM, MercadoLibre, Amazon, and other platforms — not just in a widget bolted onto checkout.
Upselling and cross-sell
Suggests the right add-on, warranty, or upgrade in the same conversation — the way the best salesperson already does, not a generic recommendations widget.
Technical product questions
Cross-references the spec sheet, floor plan, or photo a customer shares to answer with a concrete measurement or spec — not generic product copy.
Pre-qualification for big purchases
For higher-consideration products, scores budget, use case, and urgency before handing the contact to sales — the way the best salesperson already does.
Technical support triage
Asks the right diagnostic questions for a broken or malfunctioning product, resolves what it can, and hands off what it can't with the data already attached.
In-person support coordination
Books technician visits, in-store pickups, and warranty appointments — checking real availability instead of leaving the customer to call and coordinate.
Peak season spikes
Absorbs any peak-season spike without a seasonal hiring plan.
It's not another generic storefront widget. The best talent, cloned before peak season hits.
Most AI tools for ecommerce are born locked to a single platform — Shopify, Tiendanube, VTEX, or another — and the AI layer added on top almost always comes from a provider built for the US, in English, that stops at order-status questions. During peak season, the team's capacity multiplies without needing to spend more right at that moment — or rebuild everything once it's over.
Trained on real playbooks, not a generic FAQ.
We capture how the best talent already sells, resolves, and follows up — tone included — not a scripted bot flow.
Owns the whole journey, not just one step.
Upselling, follow-up, invoicing, technical support, and in-person coordination — the same scope Avera runs for post-sale. It can be a single assistant with a skill trained for each situation, or separate agents for sales, support, and post-sale, depending on how the operation is set up.
Every task handled with AI, on every channel where people already buy.
WhatsApp, Instagram, MercadoLibre, Amazon, email, and web chat — the same assistant, the same context, on whichever platform the conversation is already happening on, so no query gets lost by channel.
Security at every layer, from the model to the client's data.
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 protective layer around the model.
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.
The same guardrail running in production, available to test.
We can host the guardrail we use today so your security team can test it directly, instead of taking our word for it.
The model never sees another user's data.
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.
The client decides what information reaches StudioChat.
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.
The standards you'd expect from a cloud platform.
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 agent explains what it does, why, and where each fact comes from.
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.
"A post-sale assistant that handles the entire journey after purchase — billing, order tracking, shipping, and returns — end to end."
What an ecommerce team actually asks.
What tasks can it handle?
Practically any conversational task in the buying journey: answering product questions, qualifying and closing sales, providing support and resolving post-sale issues, managing returns, issuing invoices, booking a technician visit, or scheduling a store visit — following the same rules the team already uses. It can be a single assistant for the whole journey, or split into separate agents by stage.
What channels can the assistant talk and work on?
Practically any channel where conversations with customers already happen — for example WhatsApp, Instagram, email, web chat, or voice, among others. The same assistant, with the same context, with no need for a different version per channel.
Can the assistant answer questions inside marketplaces, like MercadoLibre or Amazon?
Yes. StudioChat connects to marketplace messaging — MercadoLibre, Amazon, and others — in addition to WhatsApp, Instagram DM, email, and web chat, so product or post-sale questions get answered by the same assistant, no matter where the purchase happened.
Can it integrate with our current platforms and systems, or do we have to switch?
We integrate with whatever's already in use — OMS, CRM, catalog, invoicing, even custom-built systems — with no need to migrate or replace anything. The amount of work depends on the case: platforms with documented APIs connect directly; more closed or custom-built systems may need a bit of custom development, scoped before starting. Invoicing, for example, adapts to each country's requirements (SAT in Mexico, DIAN in Colombia, SUNAT in Peru, AFIP in Argentina, and more). And it's not limited to text: if the knowledge base has images or floor plans, the assistant reads them — if a customer asks 'will this stove fit in a 60x50 cm space?', it can cross-reference the product spec sheet with the floor plan or photo shared to answer with a concrete measurement, not a generic response.
How is this different from a traditional rules-based or decision-tree chatbot?
A rules-based chatbot follows a fixed set of options and gets stuck the moment a question doesn't fit. StudioChat clones how the best talent actually resolves a conversation — tone, judgment, and exceptions included — and executes the task in the company's systems instead of just showing a menu of options.
Does it only answer questions, or can it be proactive too?
It can be proactive: following up on an abandoned cart, flagging a shipping delay before the customer asks, re-engaging a lead that went cold, or checking in after delivery. It doesn't wait to be written to before acting.
Can it charge or close a sale within the same conversation?
Yes. Integrated with the payment gateway or checkout, it can generate a payment link or complete the order within the conversation — not just redirect the customer to the website.
Can it handle hard questions, or just simple FAQ cases?
It's trained on the real cases and exceptions the best talent already handles, not just a simple FAQ — and it answers from the company's real data instead of making things up. When a query falls outside its scope, it hands off to a person with full context already loaded, instead of improvising a response.