Sales teams don't have a shortage of software that promises to "use AI." What's rarer is a tool that actually decides the next action and does the work, rather than summarizing a call and handing it back to a rep. This list covers the platforms built around that distinction, plus where each one is strongest.
We build one of the products on this list — Appy — so weigh that entry's placement accordingly.
| Tool | Best for | Core focus | Pricing |
|---|---|---|---|
| Oliv | Enterprise revenue teams with scattered context across many GTM tools | Context graph + proactive agents across the revenue lifecycle | Custom / GTM engineering engagement |
| getReece | Deal-execution decisions inside an existing revenue stack | Deal awareness, next-action decisions, CRM execution | Not public |
| zig.ai | Relationship-driven sales teams wanting drafted follow-up on autopilot | Structured account/contact data + approval-gated drafts | Custom |
| Relevance AI | Building a custom outbound/SDR agent workforce | Agent-building platform, pre-built SDR templates | Enterprise/custom |
| Appy | Prospecting, pipeline, and competitive intel as coordinated named specialists | Full sales function: Piper, Alexander, Scout | Flat fee, no per-seat pricing |
Oliv's core bet is that the hard problem in AI sales tooling isn't the model, it's the scattered context sitting across Gong, Salesforce, Gainsight, and a dozen other tools. It builds an "Object Graph" connecting every conversation and activity to the right account, then layers always-on agents (prospecting, outreach, inbound qualification, lead routing, expansion signal detection) on top, built and maintained with an embedded GTM engineering team.
Where Oliv wins: for a revenue org already running on a sprawling, disconnected stack, Oliv's context-unification work is a genuinely hard problem solved well, and the embedded GTM engineering model means you're not building the agent logic yourself. The economics pitch — replacing per-point-tool licenses with AI-native equivalents at meaningfully lower cost — is a real consideration for a team paying for many single-purpose tools today.
Where it's limited: this is an enterprise engagement, not a self-serve Slack install — you're working with Oliv's GTM engineers to connect your stack and learn your process, which is a longer runway than a plug-and-play coworker.
Pricing: Not public; positioned as an enterprise deployment with embedded engineering support.
getReece (formerly Jointflows) reads what's actually happening in a deal — calls, notes, CRM changes, inbox activity — decides the next action, and executes it inside the tools the team already uses: drafting follow-ups, updating the CRM, scheduling, triggering handovers. Its pitch is explicitly against tools that only observe or analyze (CRMs record, Gong analyzes) without deciding or acting.
Where getReece wins: the specific focus on deal execution — not just surfacing a risk signal but drafting the follow-up and updating the record — is a sharper, narrower claim than most "AI sales copilot" positioning, and the pattern-matching against a knowledge graph of prior deals (what signals preceded deals that died at legal, for example) is a genuinely useful layer if it holds up in practice.
Where it's limited: it's focused on deals already in motion inside your CRM — not prospecting, not competitive intelligence, not outbound sequencing. You'd need something else to cover the top of the funnel.
Pricing: Not public.
zig.ai builds one structured, governed record of every account and conversation, then has its assistants draft follow-ups, qualify and route inbound leads, and turn a scanned business card or badge into a queued contact and follow-up — all staged for a rep's one-tap approval before anything sends.
Where zig.ai wins: the approval-gated execution model (drafts wait for a tap, nothing sends unsupervised) is a sensible default for relationship-heavy sales motions where a wrong tone or premature outreach actually costs a deal. The badge-scan-to-queued-follow-up flow is a nice, specific feature for event-heavy sales teams.
Where it's limited: positioning is explicitly for "relationship-driven industries" at a range of sizes, but pricing and feature depth aren't public, making it harder to evaluate against alternatives without a direct conversation.
Pricing: Not public; stated as plan-based without per-seat charges, per their site.
Relevance AI is developer-facing infrastructure for building and governing agents, and its sales-specific angle includes pre-built templates (a Research & Enricher agent, an Outbound Prospector, a Meeting Scheduler) that a technical team can clone, customize, and chain into an outbound workflow.
Where Relevance AI wins: if you have engineering or RevOps resources and want full control over the exact logic, evaluation, and autonomy level of your outbound agents, the template library plus build-your-own flexibility is a strong starting point, and the published cost-per-task and eval-pass-rate tracking is a genuinely useful transparency feature for proving ROI.
Where it's limited: it's a platform you build on, not a coworker you install and start directing. The setup and ongoing maintenance burden is real, and it assumes a different kind of buyer than a sales leader who wants a rep-equivalent agent working by this afternoon.
Pricing: Enterprise/custom, scoped to usage and agent count.
Appy's sales function is three named specialists: Piper (Business Development) researches a prospect and picks the angle before writing outreach — cold emails, follow-up sequences, LinkedIn openers — built around the specific account rather than a template with a name swapped in. Alexander (Account Executive) works the deals already in motion: qualification against BANT or MEDDIC, discovery prep, renewal tracking, QBR materials — drafting every proposal for review and never auto-sending or inventing a number that isn't in your system. Scout (Competitive Intelligence) builds fresh battle cards and feature matrices for whatever named competitor is actually in the deal, every time, rather than handing over a static deck from last quarter.
Where Appy wins: when a deal comes in against a named competitor, the three specialists hand off automatically — Scout's research shapes Piper's outreach angle, and what Alexander learns in discovery tells Scout which comparison actually matters for that account. That shared-context coordination across prospecting, deal execution, and competitive intel is a different shape of output than three separate point tools. Runs natively in Slack and Microsoft Teams, flat fee, no per-seat pricing.
Where it's limited: Appy doesn't build the kind of cross-tool context graph Oliv does for enterprise stacks with a dozen disconnected GTM tools, and it isn't a build-your-own platform the way Relevance AI is for a team wanting full custom agent logic. If your core problem is specifically unifying a sprawling enterprise stack or building bespoke agent workflows, those are better single fits.
Pricing: Flat workspace fee, no per-seat charge; $100 in free credits to start, no card required.
A copilot answers questions and summarizes — call recaps, deal health commentary — and leaves the action to the rep. An agent, as getReece's own positioning puts it plainly, decides the next action and does the work: drafts the follow-up, updates the CRM field, schedules the meeting. Ask any vendor directly whether their tool updates systems of record on its own (with approval) or only describes what should happen next.
No credible vendor on this list claims that, and the honest ones are explicit about it. Appy's Alexander, for instance, drafts every outreach and proposal for human review and never auto-sends. The realistic claim across this category is removing the research, drafting, and CRM-hygiene labor that eats a rep's day — not replacing the judgment and relationship work that closes a deal.
As current as the deal itself, which is the gap most sales teams have — a battle card built once and reused for a year misses product changes, pricing shifts, and new entrants. Tools built around fresh, per-deal research (Scout, in Appy's case) rather than a static deck are solving a real, specific problem here; ask any vendor whether competitive content is generated fresh per request or pulled from a cached file.
Depends on how scattered your actual stack is. If your reps are already manually cross-referencing five tools to understand an account before every call, a unification layer solves a real, measurable time cost. If your stack is simpler — one CRM, one outreach tool — that problem may not exist yet, and a lighter-weight agent working directly in your existing tools (rather than a new context layer on top of them) is a faster path to value.
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