A reasoning layer over your CRM and call recordings that ranks the accounts and contacts each rep should work today, drafts the outreach, and leaves the sending to your own stack.
What is
Actively.ai
Ramp went from 50 SDRs to 90 and from 50 AEs to more than 100 in a single year, and Actively.ai is the layer it put underneath that growth. The product reads your CRM and your call recordings, then tells each rep which accounts and contacts to work today and why. Custom reasoning models sit on top of Salesforce fields, Gong transcripts and whatever third-party data you already pay for, and they rank accounts and people by how likely each one is to move. Every account gets its own agent that keeps working between logins, so the ranking refreshes with the account rather than once a quarter. The output lands in an Agent Inbox as prepared work, meaning a ranked contact, a research brief, a why now line and a drafted email with the agent's reasoning attached. Your rep edits it and sends from their own stack, because Actively.ai carries no sequencer, no mailboxes and no contact database. We would only put a client on Actively.ai when the daily bottleneck is choosing which account to work and their Salesforce holds enough deal history to be worth reading, since the models learn from that record alone. Nebor has not run it, and for a smaller team we would build the same prioritisation inside Clay.
Integrations and the modern GTM stack
Actively.ai reads a small number of systems deeply instead of connecting to everything, and the two that carry the product are Salesforce and Gong. The rest of this list is about getting the agents' output back in front of a rep, since none of it moves an email.
Salesforce | The core dependency. Actively.ai pulls pipeline history, activity and object data through subprocessors, trains the account and contact ranking on it, and pushes next best actions back into CRM views. How much history your Salesforce holds is the ceiling on what the agents can say about an account. |
Gong | Call transcripts feed the reasoning models alongside the CRM record. Both the Ramp and Greenhouse case studies name transcripts as an input, and that is how the agents pick up the language of a real deal instead of scoring field values on their own. |
Slack | Agent output and alerts land in Slack channels through the API platform, and the Agent Inbox page lists Slack as one of the surfaces where completed work shows up. This is the delivery layer for teams that do not want reps living in another tool. |
ChatGPT, Claude and Cowork | The MCP server exposes Per-Account Agents to the AI tools reps already open, so a question about an account arrives with the agent's memory, research and account context already loaded. Actively.ai names these three clients specifically. |
API platform | Direct access to per-account agent memory, decisions and strategy for internal AI and RevOps teams, meant for embedding recommendations into internal dashboards, custom apps and CRM screens. No public API reference exists, so the surface area gets scoped in the sales conversation. |
Third-party data you already buy | Actively.ai holds no contact database and reasons over vendor data you already own. Ramp's case study names third-party vendor data as an input alongside Salesforce and Gong, so your intent and enrichment contracts stay exactly where they are and get read rather than replaced. |
What is missing | No sequencer and no mailbox layer, so Outreach, Salesloft, Instantly and Smartlead sit downstream of the drafted email with a manual handoff in between. No published HubSpot depth, no email or phone verification, no Zapier or n8n app, and no self-serve signup, so every connection is scoped in an onboarding that reviewers describe as multi-week. |
Pro’s
Each ranked contact arrives with the research brief, the why now line and a draft already attached, so a rep skips the twenty minutes of tab-hopping that used to sit in front of every touch.
The models learn from your own closed-won deals and call transcripts, so the ranking reflects how your deals really happen instead of a generic fit score sold to everyone in your market.
Every recommendation carries the reasoning behind it, so a rep who disagrees can read the account facts that produced the ranking and argue with them rather than ignoring a number nobody can explain.
The published results are specific and attached to named logos, with Greenhouse reporting $12.8M of qualified pipeline and 54% more pipeline per rep, Ramp reporting 23% higher win rates on sourced opportunities, and Justworks reporting 3.7x higher closed-won rates on recommended contacts.
New hire ramp is where the case studies agree most, with Greenhouse taking time to first activity from 16 days to 8 and onboarding from 3.5 weeks to two, because the research layer teaches the patterns a new rep has not learned yet.
The API platform and MCP server push the same account intelligence into Salesforce views, Slack alerts and the AI tools your reps already open, so the work does not sit in yet another tab nobody visits.
Cons
Pricing is custom and annual with no public tier, no free trial and no self-serve entry, and third-party trackers put entry contracts in the mid five figures, so you sign for a year before you learn whether the ranking beats your reps' own judgment.
The engine runs on your Salesforce, and reviewers report stale records and a multi-week onboarding before the output is worth reading, so a team whose pipeline data is messy pays for a model it cannot feed.
Actively.ai finds and verifies no emails or phone numbers, so a data vendor still sits underneath it and every bad contact record flows straight into the prioritised list your reps work.
Nothing gets sent from inside the product, so the moment an account heats up a rep moves the work into Outreach, Salesloft or your sequencer by hand, and that handoff eats the timing the ranking just bought you.
The only integrations Actively.ai names in public are Salesforce, Gong, Slack and the MCP clients, and we found no documented HubSpot depth or published API reference, so a HubSpot shop should press hard on that in the demo.
Pricing
Actively platform
Custom Annual contract, quote only, demo required
Per-Account Agents across the accounts you assign
Agent Inbox with ranked, prepared work and reasoning
Assistant that returns finished deliverables on request
Salesforce records and Gong transcripts as source data
Onboarding scoped with sales, RevOps and IT
Watchtower
Custom Listed as its own product, no separate price published
Live agent view of the territory
Champion gone dark, usage drop and competitor mentions
Missed follow-up flags on open deals
Rep execution patterns and coaching leverage
API platform and MCP server
Custom Aimed at internal AI and RevOps teams
Direct access to agent memory, decisions and strategy
Next best actions inside CRM views and Slack alerts
MCP access from ChatGPT, Claude and Cowork
Embedding into internal dashboards and custom apps
How it fits your stack
Alternatives
At a glance
Per-Account Agents that run continuously
Reads Salesforce records and Gong transcripts
Ranked accounts and contacts with reasoning shown
Agent Inbox of prepared, drafted work
Watchtower for pipeline risk and coaching
MCP server for ChatGPT, Claude and Cowork
SOC 2 Type II, GDPR and CCPA
Custom annual pricing, no self-serve plan
Read some of our client stories
GTM Workflows
Learn how to use GTM tools in workflows
Guides and tutorials to get the most from your workflows
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