> Actively builds custom reasoning engines that tell enterprise teams where to focus. Stalar holds the context and does the work. How the theses differ. Head to head # Stalar vs Actively AI Actively builds custom reasoning engines that tell enterprise teams where to focus. Stalar holds the context and does the work. How the theses differ. Hanna Hylander · Updated 19 August 2026 In short - Actively AI sells 'GTM superintelligence': custom reasoning engines over your CRM and data that decide which accounts and actions deserve attention. $22.5M raised, Ramp and Verkada among its users, explicitly positioned against the AI SDR wave. - Both companies agree on the core: AI in sales is only as good as the context it reasons over. Actively reasons over the data you already have; Stalar builds the context itself and then acts on it. - Enterprise with rich data wanting sharper prioritization: Actively. Team that wants the context captured and the work done, from research to outreach to CRM upkeep: Stalar. Actively AI took the contrarian lane in the AI sales wave and said the quiet part loudly: AI SDRs failed, activity is not the constraint, and what GTM teams need is superintelligence about where to focus. Its product is custom reasoning engines built over a customer’s existing CRM and data, deciding which accounts and actions deserve human attention. Bain Capital Ventures led its round; Ramp, Verkada and Attentive run it. Of everyone in the field, Actively is the company whose premise we agree with most, which makes the disagreement precise. ## The shared premise, and the fork Both companies believe the same sentence: the value of AI in sales is a function of the context it reasons over. A model with thin context produces generic outreach and bad prioritization no matter how clever the prompting. Where the products fork is what they do about it. **Actively reasons over the context you already have.** It assumes an enterprise whose CRM, product data and history are rich enough to mine, builds a custom reasoning engine over them, and returns focus: which accounts, which actions, in what order. Your team then does the work. **Stalar builds the context, then does the work.** Most teams’ context does not live in queryable systems; it lives in reps’ heads, calls and inboxes, next to [a CRM that trails reality](https://stalar.ai/blog/most-of-the-pipeline-is-fiction/). Stalar’s sales brain captures it: every prospect, conversation and touchpoint recorded and remembered, every interaction learned from, how you talk, what works, who buys. Agents then apply that context across the whole motion: researching accounts, drafting outreach in your voice, briefing meetings, keeping the CRM current, proposing the next move, with your reps approving each step. ## Side by side | | Actively AI | Stalar | | --- | --- | --- | | Core product | Custom reasoning engines for prioritization | Agentic workspace on a shared sales brain | | Context source | Your existing CRM and data | Built by the brain from your team’s interactions | | Output | Focus: which accounts, which actions | The work itself, proposed for approval | | Execution | Your team | Agents, human-approved | | Prospecting | Out of scope | Included, with verified contacts paid per found | | Built for | Data-rich enterprises | Working sales and revenue teams | ## When Actively is the better pick An enterprise with clean, deep data and a team that executes well has a prioritization problem, and Actively is purpose-built for it. If your CRM is trustworthy and your motion runs, a reasoning engine that ranks the next best account is leverage on top of strength. ## When Stalar is the better pick If the data underneath is the problem, prioritization inherits it: a reasoning engine over a stale CRM ranks fiction. Stalar starts one layer down, capturing the context as your team works and acting on it, so the intelligence and the execution come from the same place. The [approval-loop numbers](https://stalar.ai/blog/what-the-approval-loop-costs/) show what that looks like in production, and [the AI SDR guide](https://stalar.ai/best/ai-sdr-tools/) maps where both products sit in the wider field. ## Frequently asked questions - **What is the difference between Stalar and Actively AI?** Actively builds custom reasoning engines on top of an enterprise's existing CRM and data, answering which accounts and actions deserve focus. It prioritizes; your team executes. Stalar is the agentic workspace for sales and revenue teams: a sales brain records every prospect, conversation and touchpoint, learns from each interaction, and agents execute from that context, researching, drafting outreach in your voice, briefing meetings, keeping the CRM current, with your reps approving each step. - **Who is Actively AI for?** Enterprise go-to-market teams with substantial existing data: its public customers include Ramp, Verkada and Attentive, and its pitch is explicitly anti-AI-SDR, arguing the constraint is deciding where to focus rather than generating more activity. The reasoning engines are custom-built per customer. - **When is Actively the better choice?** When you are an enterprise whose CRM and data are already rich, whose team executes well, and whose constraint is prioritization: which of ten thousand accounts deserve the next hour. A custom reasoning engine over data you already trust is exactly that product. - **When is Stalar the better choice?** When the context itself is the gap: conversations unlogged, the CRM trailing reality, research living in reps' heads. Stalar's sales brain captures that context and its agents act on it end to end, so you get both the intelligence and the work, not a smarter to-do list on top of stale data. Related comparisons - [Head to head: Stalar vs Rox](https://stalar.ai/vs/rox/) - [Head to head: Stalar vs Reevo](https://stalar.ai/vs/reevo/) - [Buyer's guide: The best AI SDR tools in 2026](https://stalar.ai/best/ai-sdr-tools/) - [Jämförelse: Alternativ till Goava 2026](https://stalar.ai/sv/jamforelser/goava-alternativ/) --- Canonical: https://stalar.ai/vs/actively/ Site index for agents: https://stalar.ai/llms.txt