Nooks
AI sales assistant with a parallel dialer and virtual salesfloor.
AI agent for sales conversation intelligence and automatic CRM updates
Attention is a strong conversation-intelligence play, and its automatic CRM updates plus real-time coaching are more actionable than plain call recorders. For mid-market and enterprise sales orgs drowning in manual CRM entry, that automation can pay for itself. The downsides are opaque, sales-led pricing and enterprise-level cost that shut out smaller teams. Evaluate it if you have a sizable rep team and a Salesforce-centric workflow.
Attention is an AI conversation-intelligence platform that records sales calls and meetings, then automates follow-ups, next steps, coaching, and CRM updates for sales teams.
Attention is an AI platform for sales conversation intelligence. It records and analyzes every customer touchpoint, then turns those conversations into structured outcomes: auto-drafted follow-up emails, extracted next steps, real-time coaching prompts, and, notably, automatic CRM field updates so reps spend less time on data entry. The pitch is to compress the busywork between calls and to enforce methodology and messaging consistency across a team. The platform emphasizes actionability over passive note-taking. Beyond transcription and summaries, it can trigger playbook adherence checks, flag risks, and push updates into systems like Salesforce, making it a hub between conversations and pipeline data. This positions Attention against both call-recording tools and broader revenue-intelligence suites. Attention does not publish pricing and sells through a demo-and-quote process, with reported deals landing in the mid-five-figures to low-six-figures annually depending on team size and call volume. That makes it an enterprise and mid-market fit rather than an SMB tool. Buyers who need transparent, self-serve pricing or lightweight note-taking will find it heavier and pricier than necessary.
Attention is an enterprise AI conversation-intelligence platform that automates CRM updates, follow-ups, and coaching from sales calls, sold via custom quotes.
Attention is an AI sales-technology company focused on conversation intelligence and automation. It positions itself as an action layer over sales conversations, not just a recorder, emphasizing CRM automation and coaching.
The company sells to mid-market and enterprise sales organizations through a sales-led motion, competing with call-recording and revenue-intelligence tools by leaning into automation and playbook enforcement.
Attention captures calls, emails, and meetings, then generates summaries, next steps, and coaching insights, and automatically updates CRM fields. It can draft follow-up emails, flag risks, and check adherence to sales methodology.
It integrates with CRMs like Salesforce and meeting tools, and offers an API for custom workflows. The emphasis throughout is on reducing manual work and enforcing consistency.
The core market is mid-market and enterprise sales teams and revenue operations that want to automate CRM hygiene and scale coaching. It is not aimed at small teams or casual note-takers.
Account executives and SDRs on calls.
VPs of sales, revenue operations, and sales enablement leaders.
CRM admins and sales-ops analysts.
A mid-market or enterprise sales org with a sizable rep team and a Salesforce-centric process seeking CRM automation and coaching.
Funding details are not detailed here; verify any investment information with primary sources.
It turns conversations into action: auto-drafted follow-ups, extracted next steps, coaching prompts, and automatic CRM field updates.
Yes. Attention integrates with CRMs like Salesforce and can automatically update fields based on call content.
Generally no. It is enterprise-focused with quote-based pricing that is hard to justify for small teams.
Pricing is custom and sales-led, with no public plans; reported deals typically run from the mid-five figures to low-six figures annually.
Yes. It provides real-time and post-call coaching insights and checks adherence to your sales methodology.
Side-by-side pages for pricing, features, and best-fit use cases.
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