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AI email response automation in the sender's language

Inbound email answered from your own data

For Senetic we automated inbound sales email across 151 countries, with each reply drafted from live catalogues in the customer's language.

Book a free consultation How we build agents
sales-inbox

  1. Sales leadYour team

    A buyer asks which switches fit their rack and when they could ship. Can the email agent take it?

  2. Email agentAgent

    1. inbox.read question written in German
    2. catalogue.search models sold in that country
    3. crm.get_account past orders and terms

    Reply drafted in German with two compatible models. It names a delivery date, so it needs a sign-off.

    Needs a personReply promises a delivery dateDraft in German for a returning buyer
    Approved by Sales rep

  3. Email agentAgent

    1. inbox.send_reply from the shared sales inbox
    2. crm.log_activity reply and products offered

    Sent from the shared inbox and logged on the account with the products offered.

Example data. An agent at work in a team channel, with a person approving the step you choose.
Mixam: conversion to a final sale from about 20% to about 40%
Where agents help

Email piles up while the answers sit in your systems.

Agents look up the facts and draft each reply, and a person approves what commits you.

  1. 01

    Where it stallsReplies wait until someone has time to look things up.

    What we do An agent reads each email as it arrives, checks the catalogue and the account, and drafts the reply.

  2. 02

    Where it stallsCustomers write in languages your team does not speak.

    What we do The agent replies in the sender's language and keeps product names exactly as your catalogue lists them.

  3. 03

    Where it stallsOne wrong promise costs more than a slow reply.

    What we do Replies that commit you to a price, stock level or date wait for a person. That rule lives in code.

What we build

What we build for your inbox

Whatever comes in, every project is built from the same parts below, set up for your files and your systems.

Inboxes we handle

  • Customer service
  • Sales enquiries
  • Orders and changes
  • Billing questions
  • Partner and supplier mail
  • Support tickets by email
  • Booking requests
  • Shared team inboxes

How we build it

  • Replies grounded in your own data

    Drafts built from live product data, policies and past replies, traced to their source.

  • Sorting and routing every email

    Each message is sorted by intent and language, and anything outside scope goes to the right person.

  • Inbox, CRM and catalogue connections

    Read and send access to your mail server, CRM and product data, with the permissions you set.

  • Testing on your past threads

    Draft quality checked on real past emails before launch, and every live reply logged.

Not seeing your case? Bring the inbox your team spends most time on and we start from that.

Tell us about your workflow
How we build agents

Agents that work as part of your team

They work in the tools your team already uses, take on whole tasks and stop for a person where you decide.

  1. Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
  2. Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
  3. Approvals log: each action that waited for a person, with who asked, who decided and when.
Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
Approvals log: each action that waited for a person, with who asked, who decided and when.

Screens from the workspace our agents run in, with example data.

Where it runs

Teams and industries that use it

Each page shows where agents fit in that team or industry.

Engineering in the open

We help build what agentic AI runs on

Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.

Shaping agentic AI

Agentic AI Foundation

Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.

Read the announcement

Open source

We build open-source agent tooling that other engineering teams install

10M+ PyPI downloads
4,000+ GitHub stars
33 public repositories

Framework contributors

When the framework breaks, we fix it at the source

We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.

Research

Our engineers and PhDs publish peer-reviewed research

Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.

Track record

Frontier AI teams now build on what we shipped first

Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.

Case studies
All case studies
Engagement models

Ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Start with one workflow

We map one workflow with your experts and agree a success threshold before any build.

Best whenYou want the success test agreed before you commit to a build.

Book a free consultation
  1. Process redesign and build Best whenYou want one team accountable for the outcome.
FAQ

AI email response automation, answered

What is AI email response automation? +
AI email response automation uses an agent to read incoming email, work out what the sender needs, look up the answer in your catalogue, policies and CRM, and draft a reply in the sender's language. Routine replies can go out on their own. Anything that commits you to a price, stock level or date waits for a person to approve it.
Which emails should an AI agent answer first? +
Start with a shared inbox that gets the same kinds of questions every day and where the answer already exists in your systems: product availability, compatibility, order status, return rules. Leave complaints and contract talks with your team at first. Pick one inbox, agree an accuracy threshold with the people who answer it today and test the agent on their past threads.
Have you built AI email automation before? +
Yes. Senetic's sales inbox in Outlook took product questions in every language the company sells in, and staff searched thousands of products by country to answer them. We put retrieval over the live catalogues on that path, so a reply comes back in real time, in the customer's language, across 151 countries. For ARIJ Network we built an agent that answers only from its own materials, in English or Arabic.
Can an AI agent send email replies without a person checking them? +
Only for the types of email you choose. Most teams start with the agent drafting and a person sending. Once the drafts pass your threshold on real threads, routine replies can go out on their own. Price, stock and date commitments stay behind an approval, and the agent treats instructions written inside an incoming email as text to answer and does not act on them.
How do you know it works before the full build? +
We agree a success threshold with your team before the build and test the agent on your own cases in a Proof of Value. It measures accuracy and running costs, and the build goes ahead only when the threshold is met. Corrections from your team go back into the test set, so every later version is checked against them.
Who owns the system, and which models does it run on? +
You own the code, prompts, evaluation sets and rules by contract. The agents run in your environment on hosted or open-weight models. The model sits behind an interface in code, so you can change it when accuracy, speed or your hosting rules change, without rebuilding the agent.
Start with one workflow

Test one workflow on your own data

A Proof of Value shows accuracy and running costs before the full build.

Book a free consultationRead case studies
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