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Actively AI logoActively AI

Research Comparison

Updated

Markdown

Summary

Per-Account AI agents that work 24/7 to autonomously research accounts, build pipeline, and guide enterprise go-to-market teams.

Funding

68

Founded

2020

Team

80

actively.ai

Critical Notes

- Challenger to legacy CRMs like Salesforce: shifts from human-led data entry to Intelligence-Led Revenue - Dedicated AI agent per account researches, drafts outreach, flags pipeline risks around the clock - $45M Series B in April 2026 at $250M valuation - Ramp attributes tens of millions in new revenue to the system

Key Features

- Per-Account Agents - Agent Inbox - AI Sales Assistant - Watchtower pipeline monitoring - API Platform - MCP Server integration - CRM and call recording integrations

Details

Enterprise AI for go-to-market teams. Deploys a dedicated AI agent per account to research, identify opportunities, and advance next steps. Raised ~$68M: $5M seed, $17.5M Series A (Bain Capital Ventures), $45M Series B (April 2026, co-led by TCV and First Harmonic, $250M valuation). Founded by former Stanford AI researchers Mihir Garimella and Anshul Gupta. ~36,000 monthly visits; ~20,500 LinkedIn followers. Sales-led, demo-gated, no free trial or PLG. Targets CROs, VPs of Sales, RevOps. Customers: Samsara, Ramp, Verkada. Closed-source; API Platform and MCP Server for extensibility.

Signalscompared to Common Room

1st Party

  • Deep Salesforce + Gong integration (pipeline history, call transcripts, participant metadata); auto-enroll/pause contacts in Gong Engage; custom models trained on closed-won patterns
  • No HubSpot, Marketo, website visitor de-anonymization, data warehouse product usage, chat, CS tools, ad platforms, or SEP integrations beyond Gong Engage

2nd Party

  • unsure
  • No open source, community, social, reviews, events, or commerce signals

3rd Party

  • News signals (job moves, funding events, company updates)
  • Not a data provider; no technographics, job listings, or intent vendor integrations; separate waterfall enrichment needed

Custom

  • API Platform; MCP Server for embedding agent context in custom apps
  • Export-oriented; no documented CSV, Sheets, Zapier, or webhook ingest; needs engineering resources

1st Party: Deep Salesforce and Gong integration (CRM pipeline history, contact activity, opportunity stages, call transcripts, participant metadata, deal coaching signals). Trains custom models on closed-won patterns. Auto-assigns, enrolls, or pauses contacts in Gong Engage. 2nd Party: unsure. 3rd Party: External news (job moves, funding, company updates). Not a data provider; separate waterfall enrichment needed. Custom: API Platform and MCP Server let engineering teams embed agent context in proprietary workflows and applications.

Identitycompared to Common Room

Enrichment

  • Internal entity extraction from unstructured 1st-party data (calls, emails, RFPs)
  • No verified emails, phones, firmographics, or prospect database; needs ZoomInfo/Apollo/waterfall tools

AI Powered

  • ML entity extraction and relationship mapping from transcripts and documents; ML signal-to-account matching
  • No AI matching of handles or profiles across sources; no net-new contact identities

Identity

  • Maps 3rd-party signals to existing CRM accounts (account-level)
  • No person-level identity resolution; relies on existing CRM and 3rd-party tools

Identity: No native person-level identity resolution; relies on existing CRM and 3rd-party enrichment. Account-level mapping of messy 3rd-party signals to CRM accounts. Enrichment: None natively; no verified emails, direct dials, mobile, or B2B data. Needs ZoomInfo, Apollo, or waterfall providers. Internal enrichment extracts entities and context from unstructured calls, emails, RFPs. AI Powered: AI entity extraction and relationship mapping from transcripts, documents, intent signals, made queryable for Per-Account Agents.

Automationcompared to Common Room

Combine

  • Deep Reasoning Engine merges CRM, Gong, and external news into continuous account profile
  • Limited to ingested sources; no user-controllable segments or workflows

Score

  • ML model trained on customer closed-won history; dynamic account ranking
  • Black box; no rules-based or custom scoring; account-level only; needs historical data

AI Message

  • Assistant drafts emails, talking points, meeting briefs from agent research
  • Does not send; no sequencer or multi-channel

AI Research

  • Dedicated agent per account across TAM, 24/7 background research; conversational Assistant for ad-hoc questions
  • Bounded by narrow signal set; custom prompt authoring undocumented

Workbench: Agent Inbox, 5-10 prioritized accounts daily with research, flag reason, next actions. Workflow: No native outreach sequences; Gong Engage auto-enroll/unassign; API for custom multi-step logic. Alerts: Watchtower tracks account universe; flags champion going dark, usage drop, missed follow-up, competitive mentions. Combine: Deep Reasoning Engine merges CRM, Gong, and external signals. Score: ML trained on closed-won outcomes learns ICP and deal patterns. AI Message: Assistant drafts emails, talking points, meeting briefs; does not send. AI Research: Per-Account Agent for every TAM account, 24/7; conversational Assistant.

Supportcompared to Common Room

Systems

  • Agent Product Managers and Forward-Deployed Engineers embedded in multi-week onboarding; shared Slack channels; email and contact forms; Vanta trust portal
  • No self-serve ticketing or help desk

Content

  • API/MCP developer docs; video library, webinars, blog
  • No public knowledge base

Community

  • None
  • No forums, templates, or peer support

...

Status: Watch

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