AI Models
AI for Sales

Pipeline that builds itself.

Research accounts, personalise outreach, prep every call and follow up automatically — so reps spend their time selling.

Sales assistant
Prep me for a discovery call with a mid-size logistics company’s ops director.
Likely priorities: on-time delivery, labour costs, visibility. Open with their recent expansion, then ask how they forecast capacity. Three objections to expect…
Ask anything…
The challenge

What holds sales teams back

  • Research eats selling time

    Reps spend hours on account research before every conversation.

  • Generic outreach

    Templated messages get ignored; personal ones don’t scale.

  • Leaky follow-up

    Notes and next steps slip through the cracks after busy calls.

Use cases

Six ways to put AI to work, today

Pipeline that builds itself and follows up for you.

  • Account briefs

    One-page summaries of any prospect, ready before the call.

  • Personalised outreach

    Messages tailored to role, company and recent news.

  • Call prep

    Likely objections, discovery questions and talking points.

  • Meeting notes

    Clean summaries with next steps and owners.

  • Follow-up agent

    Drafts timely follow-ups for rep approval.

  • Deal reviews

    Spot risks and gaps across your pipeline.

How it works

From first prompt to everyday workflow

  1. 01

    Connect context

    Give agents your ICP, playbook and product facts.

  2. 02

    Research

    Agents brief reps on each account automatically.

  3. 03

    Engage

    Draft personalised outreach for approval.

  4. 04

    Follow through

    Summarise calls and schedule next steps.

Agents & skills

Agents built for sales

Give an agent a goal and it plans the steps, uses the skills you grant and reports back — with approvals wherever you want a human in the loop.

  • Account briefs: One-page summaries of any prospect, ready before the call.
  • Personalised outreach: Messages tailored to role, company and recent news.
  • Call prep: Likely objections, discovery questions and talking points.
Explore agents
Agent run · live
  1. Understand

    Intent & context

    Done
  2. Plan

    Break into steps

    Done
  3. Act

    Run skills & tools

    Running
  4. Deliver

    Verified result

    Queued
Outcomes

What changes when you switch it on

  • Outcome 1

    More time selling

    Hand research and admin to agents and focus on conversations.

  • Outcome 2

    Higher response quality

    Every message is relevant to the person reading it.

  • Outcome 3

    Nothing falls through

    Follow-ups and next steps happen on time, every time.

Under the hood

Foundational models, plus proprietary architecture

Every leading open model family, made sharper by the layer we built around them.

  • Routing

    The best model for each request, automatically.

  • Memory

    Your sales context, remembered securely.

  • Skills

    Search, read, write and call your own tools.

  • Agents

    Goal-driven workers with guardrails.

Security posture

All controls healthy

Continuously monitored

  • Encryption in transit & at rest
  • SSO & scoped API keys
  • Audit logs for every action
  • Your data never trains shared models
Security & control

Enterprise-grade from day one

  • Your data is never used to train shared models
  • Encryption in transit and at rest
  • Roles, scoped API keys and approval steps
  • Audit logs, SSO and private deployment options on higher plans
FAQ

Sales questions, answered

Will it send emails on its own?

Only if you allow it. By default, outreach is drafted for rep approval.

Can it learn our playbook?

Yes. Add your methodology, personas and product notes to memory.

Does it work with our CRM?

Use built-in HTTP skills or the API to read and write records in your stack.

Is prospect data kept private?

Yes. Your data stays in your workspace and is never used to train shared models.