Actively AI
GTM superintelligence platform that trains a custom reasoning model per company and deploys persistent Per Account Agents to monitor signals, prioritize accounts, research, and draft outreach around the clock across the entire market, so revenue runs on intelligence rather than human capacity.
Actively AI, based in New York, is building what it calls GTM superintelligence, an AI foundation that reasons about a company's entire market to decide which accounts to engage, when, and how, rather than simply automating outreach.
Founded in 2022 by former Stanford AI researchers Mihir Garimella, its chief executive, and Anshul Gupta, it raised a forty five million dollar Series B in April 2026 co led by TCV and First Harmonic, with Bain Capital Ventures and First Round Capital, bringing total funding to sixty eight million dollars.
Its bet is that while coding, support, and legal have been transformed by AI, sales, the most expensive function in most companies, has not, and that the next winning revenue orgs will run on models that outperform human led decisions.
Rather than another point tool bolted onto the stack, Actively introduces a new primitive: a persistent Per Account Agent for every account, operating around the clock across a company's entire total addressable market with full context on what is happening in the sales organization. It trains a custom reasoning model for each company, on par with its top sellers, that analyzes millions of signals including call transcripts, notes, custom Salesforce fields, and the web to form hypotheses about the best prospects, the right timing, and the plays needed to win.
The agents monitor signals, prioritize accounts, research and prep meetings, surface risks and growth opportunities before anyone looks for them, and draft tailored outreach, then present finished work ready to act on. Because intelligence lives at the account level, it compounds over time and gets smarter every quarter, and teams can embed that agent work into any tool, workflow, or internal application and ship new use cases in days.
Actively is built for strategic, multi segment revenue teams and is deployed at companies like Ramp, Ironclad, Attentive, and Samsara, where its agents span sales, account development, revenue operations, and customer success across a thousand person go to market org. It runs as a cloud platform, trains its own reasoning models rather than offering a choice of provider, and gathers signals from the web rather than driving a browser to operate applications.
For an enterprise revenue team that wants an always on intelligence layer to cover its whole market and reason like its best sellers, Actively is a strong and differentiated fit; a small team wanting a simple sequence bot, a self serve tool, or a self hosted, model flexible platform will find it an enterprise superintelligence rather than a point solution.
Vendor details
Canonical URL
https://www.actively.ai
Category
GTM / revenue agent
Subcategory
GTM superintelligence with per account AI agents
Funding status
Independent, based in New York with a new San Francisco office, founded in 2022 by former Stanford AI researchers Mihir Garimella, chief executive, and Anshul Gupta. Actively raised a $5 million seed led by First Round Capital and a $17.5 million Series A led by Bain Capital Ventures, followed by a $45 million Series B in April 2026 co led by TCV and First Harmonic, with Bain Capital Ventures, First Round Capital, and Alkeon, bringing total funding to $68 million. Its customers include Ramp, Ironclad, Attentive, and Samsara.
Company status
independent
Use cases & customers
Primary use cases
Target customers
Deployment options
Integrations
Reads and writes across the revenue stack through secure subprocessors connecting to Salesforce and other sales tools, drawing on CRM records and custom Salesforce fields, call transcripts, notes and the web to maintain continuously updated context across the total addressable market. Four named product surfaces carry the work: Agent Inbox for agent completed work, Assistant for on demand briefs and assets, Watchtower for pipeline risk and opportunity, and an API Platform for embedding agent output into any tool or internal application. An Actively MCP server connects Per-Account Agents to Claude, ChatGPT and Cowork so customer built agents can read account research and strategy directly.
In practice
Your reps can only actively work a fraction of their book, so most accounts go uncovered and opportunities slip. Actively runs a persistent agent on every account, monitoring signals and surfacing the ones truly ready to engage.
Rep turnover keeps resetting hard won account knowledge, and new hires ramp slowly. Actively holds context at the account level so intelligence compounds over time and survives turnover, and new reps inherit research and reasoning from day one.
Your team stitches together a dozen tools and still gets shallow personalization. Actively trains a reasoning model on your data, forms real hypotheses about timing and plays, and delivers finished, tailored outreach ready to send.
Sources & related URLs
Research sources
Agentic Index coverage score
8.0 / 14 capabilities · 57%
| Integrations & Tool Calling | Full |
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Reads and writes across the revenue stack through secure subprocessors connecting to Salesforce and other sales tools, drawing on CRM records and custom Salesforce fields, call transcripts and notes and the web, and pushes prioritized accounts and drafted outreach back into the systems reps use; the API Platform embeds agent work into any tool, workflow or internal application. Sourceactively.ai/products/api-platform and actively.ai/securityread 2026-08-30 |
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| Workflow Orchestration | Full |
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Every account gets a dedicated long running agent operating 24/7/365 across the full prospect and customer lifecycle, chaining account research, prioritization, meeting preparation, risk surfacing and outreach drafting, and running simultaneously across SDR, AE, AM and leadership functions rather than as a single sequential pipeline. Sourceactively.airead 2026-08-30 |
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| Knowledge Grounding & RAG | Full |
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A custom reasoning model is trained per customer on that company's own business, products and GTM strategy, and per account agents synthesize CRM records and custom Salesforce fields, call transcripts, notes and the web into a maintained profile of every account covering needs, decision makers and likely buying criteria across the entire TAM. Sourceactively.ai and Series A launch postread 2026-08-30 |
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| Human Oversight & Guardrails | Full |
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Agent Inbox is the shipped review surface where agent completed work lands prioritized and prepared for a person to act on, and the product is positioned throughout as guiding the team on what to do next and helping them do it rather than acting unattended; drafted outreach is presented for the rep to send. Sourceactively.ai/products/agent-inbox and actively.airead 2026-08-30 |
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| Security, Identity & Governance | Partial |
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Security page documents a SOC 2 Type II covering the Security trust services category, audited annually and available on request, plus GDPR compliance with a DPA for EU customers, CCPA, AES-256 at rest in Google BigQuery, TLS in transit, documented server hardening, tracked infrastructure changes and a published vulnerability disclosure policy; no SSO, SAML, RBAC, customer facing audit log or retention configuration is documented. Sourceactively.ai/securityread 2026-08-30 |
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| Observability & Auditability | Partial |
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Agent Inbox surfaces agent completed work prioritized and prepared, and Watchtower surfaces pipeline risks and growth opportunities before anyone looks for them; this is visibility into what the agents produced and how the pipeline is moving, not per agent execution tracing or an audit trail reconstructing why a conclusion was reached. Sourceactively.ai/products/watchtower and products/agent-inboxread 2026-08-30 |
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| Memory & State Persistence | Partial |
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Each account carries a dedicated long running agent holding its context, the MCP page states that the agents maintain persistent memory, and the API Platform gives the customer direct access to per-account agent memory, decisions and strategy. Memory is scoped per account and written by the agents, and the API gives a read path, but no lifetime, purge or deletion path is documented. The improving reasoning loops named in the same MCP sentence are a learning claim rather than memory. Sourceactively.ai/products/mcp-serverread 2026-10-01 |
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| Deployment & Data Residency | Not documented |
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Delivered as a cloud platform with all customer data hosted in Google Cloud facilities and stored in Google BigQuery; no customer selectable region, VPC, on premises or self host option is documented on the security page or anywhere on the site. Sourceactively.ai/securityread 2026-08-30 |
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| Prebuilt Agents, Templates & Packs | Partial |
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The Per-Account Agent is a single core primitive instantiated for every account, with role oriented configurations for CRO, AE, SDR, sales leader and internal AI teams and published workflow examples; the model is custom trained per customer with forward deployed engineers rather than shipping a library of prebuilt agents or installable templates. Sourceactively.airead 2026-08-30 |
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| Triggers & Channel Coverage | Full |
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Agents monitor signals continuously and run 24/7/365, with Watchtower proactively surfacing pipeline risks and opportunities before anyone looks for them; work is delivered into Agent Inbox and, via the MCP server, into the AI tools reps already use, and drafted outreach goes out by email. Sourceactively.ai/products/watchtower and products/mcp-serverread 2026-08-30 |
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| Model Flexibility & Routing | Not documented |
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Actively trains its own custom reasoning model per customer as the core differentiator; no model provider is named, no routing between models is described, and no customer or admin model selection surface exists. The MCP server connects the customer's chosen assistant to Actively rather than letting the customer choose the model powering Actively. Sourceactively.airead 2026-08-30 |
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| APIs, SDKs & MCP Extensibility | Partial |
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Two named products assert the surface: the Actively API gives teams direct access to per-account agent memory, decisions and strategy to embed in CRM views, Slack alerts, dashboards or custom applications, and Actively MCP connects Per-Account Agents to Claude, ChatGPT and Cowork. Neither page links a reference, developer portal or access path (both route to a demo or a solutions architect, and no developer docs are published), and the MCP endpoint, auth, tool list and core-object tools are unpublished. Sourceactively.ai/products/api-platformread 2026-10-01 |
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| Testing, Debugging & Optimization | Partial |
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The MCP page states that Actively agents maintain reasoning loops that improve over time, an asserted post deployment learning loop; its input, what it tunes and how the change is measured are not documented. Benchmarking against a customer's top sellers, reported revenue lift and tuning by forward deployed engineers are vendor side work, and no customer facing testing, evaluation, simulation or regression surface is documented. Sourceactively.ai/products/mcp-serverread 2026-10-01 |
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| Browser & Computer Use | Not documented |
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Agents gather signals from the web and act through API integrations into CRM and sales tooling; no capability to operate third party software through a browser or computer interface is documented on any product page. Sourceactively.airead 2026-08-30 |
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The Agentic Index coverage score grades every vendor Full, Partial or Not documented against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded
Recent platform changes
Actively AI now feeds web intent signals from a company's own website into its agents, which factor buying signals into account research.
Bears on: Agent capability
View sourceActively AI released Actively MCP, a Model Context Protocol server that connects its Per-Account Agents directly to external AI tools like ChatGPT, Claude, and Cowork. This integration allows custom agents to access real-time account research, strategy, and persistent memory without requiring manual context setting.
Bears on: MCP / tool calling / API
View sourcePricing
Not public; quoted through enterprise engagement, scaled to accounts, seats, and GTM functions
enterprise engagement scaled to accounts, seats, and functions across the GTM organization
What is public
No list pricing. Actively sells to enterprise revenue teams through direct engagement, but rates are not public.
Billing mechanics
Presumed enterprise engagement scaled to accounts covered, seats, and the go to market functions deployed, though rates are not disclosed.
Cost watchouts
Deployments often start with a tight use case and expand across sales, account development, revenue operations, and customer success, so cost grows as agents cover more of the go to market organization and market.
Variable cost rationale
Priced against accounts, seats, and functions, so cost grows as the platform covers more of the total addressable market and more of the go to market organization.
Additional watchouts
Confirm whether pricing is per account, per seat, or a platform subscription, and how it scales as coverage expands from a starting use case across the wider revenue organization.
Sales call required
Yes, required for paid access
Free / trial
Demo and pilot on request; no public free tier
Key ambiguities
No public rate is disclosed, and whether pricing is per account, per seat, or a platform subscription across functions is not clear.
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Alternatives to Actively AI
The closest documented capability profiles to Actively AI among GTM and revenue agents tracked by Agentic Index, ordered by similarity on the same 14 point evidence the rankings use. No vendor pays for placement.
- Alta8.0 / 14Adds documented Deployment & Data Residency
- Default9.0 / 14Adds documented Testing, Debugging & Optimization
- Instantly8.0 / 14Adds documented Testing, Debugging & Optimization
- Kleio9.0 / 14Adds documented Testing, Debugging & Optimization
- Lawmatics7.0 / 14Fuller documented coverage on APIs, SDKs & MCP Extensibility
- Loopio8.0 / 14Adds documented Testing, Debugging & Optimization
Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded