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AI agents for sales that keep deals moving

Agents for the work between sales calls

We build agents that answer buyers, draft quotes and keep the CRM current, with a person approving the steps you choose.

Book a free consultation How we build agents
sales-desk

  1. Sales repYour team

    A returning buyer wants a quote for hardcover catalogues by Friday. Can the quote agent take it?

  2. Quote agentAgent

    1. inbox.read request and attachments
    2. crm.get_account past orders and terms
    3. catalogue.match paper, binding and finish
    4. pricing.apply_rules list price and discount

    The draft quote is ready. The buyer asked for a discount above your limit, so it needs a sign-off first.

    Needs a personDiscount above your limitDraft quote for the returning buyer
    Approved by Sales lead

  3. CRM agentAgent

    1. crm.update_deal stage, quote and next step

    Quote sent from the rep's mailbox. The deal is updated and a follow-up is set for Tuesday.

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

Deals slow down in the work around them.

Agents take that work over and hand back the steps that need a person.

  1. 01

    Where it stallsReps spend more time on admin than on selling.

    What we do Agents log calls and emails, update the CRM and prepare each follow-up for the rep to send.

  2. 02

    Where it stallsLeads go cold before anyone replies.

    What we do An agent answers inbound requests as they arrive and passes qualified ones to a rep.

  3. 03

    Where it stallsQuotes and proposals take days to go out.

    What we do Agents draft quotes and proposals from your catalogue and past deals. Price rules stay in tested code.

What we build

What we build for sales teams

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

Sales work we take on

  • Inbound emails
  • Product questions
  • Quotes
  • Orders
  • Lead qualification
  • Meeting prep
  • CRM updates
  • Follow-ups

How we build it

  • Inbound email and product questions

    Answers drafted from your catalogue and past replies, in the buyer's language.

  • Quote and order agents

    They read the request, ask for missing details and fill the quote for a rep to send.

  • CRM, inbox and team chat connections

    Read and write access to your CRM, email and chat, with the permissions you set.

  • Testing before release, monitoring after

    Replies and quotes checked on your own cases before launch and logged once live.

Not seeing your case? Bring the sales task that keeps your team from selling 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.

Industries

Industries we build agents for

Each page shows where agents fit in that industry and what we have built there.

All industries
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

Questions about AI agents for sales

Where do AI agents for sales help first? +
AI agents for sales pay off first on work that waits: inbound product email nobody owns, quotes that queue behind one pricing expert and orders that stall on missing details. We go through your process with your sales lead, pick one of those steps and agree a success threshold before anything is built.
Will agents talk to our customers on their own? +
Only where you decide they should. Most teams start with agents that draft and a rep who sends. Anything that commits you to a price, a date or a term goes to a person for approval, and that rule is written into the system. Agents read from and write to your CRM with the permissions you set.
What have you built for sales teams? +
Three published cases. Mixam's product advisor handles over 96% of web-to-print orders end to end. For an engraved die manufacturer, pricing went from about 30 minutes to a few clicks, and time of work fell 72% across pricing and database operations. Senetic answers inbound sales email in real time across 151 countries. We also build lead scoring and proposal drafting agents, and we have no published case for either yet.
How do you keep prices and discounts correct? +
Price lists, discount limits and approval rules stay in tested code. The agent reads the request and picks the options, and the code calculates the price. Every quote is logged with its inputs, so your team can see why it came out the way it did.
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