Lavender
Real-time AI email coach for B2B reply rates
- Overview
- Lavender scores sales emails in real time against a dataset of billions of B2B emails and suggests specific fixes to subject lines, length, tone, and personalization. Paid tiers add prospect research, communication-style matching, and team coaching dashboards. In 2026, Lavender also launched Ora, an autonomous AI sales agent that researches prospects and drafts personalized cold emails independently.
- Why it matters
- Lavender is one of the few sales-email tools with a genuine data moat — its scoring model is trained on billions of real B2B emails and improves as usage grows. With the launch of Ora, it is expanding from a coaching layer into the AI SDR market, which changes the competitive comparison set from writing aids to full outbound automation platforms like Amplemarket or AI SDR agents. Teams evaluating it in 2026 should note that the core Email Coach and Ora are priced and sold separately, and the Teams tier ($69/user/mo) compounds quickly at scale. The main lock-in risk is workflow dependency — reps trained on Lavender's scoring rubric can struggle to calibrate quality without it.
- In practice
- Most teams deploy Lavender on the Starter or Pro tier for individual reps, using the real-time score as a pre-send gate — reps are coached not to send below a target score, which is especially effective for onboarding new SDRs. Sales managers on the Teams plan use the coaching dashboard to identify underperforming reps and run targeted coaching without manually reviewing every draft. The honest limitation is that Lavender is a coaching overlay, not a sequencer: it does not send email, so it must be paired with a separate tool (Outreach, Salesloft, Instantly, etc.), adding to total stack cost per seat.
- Best for
- SDRs and sales managers who want data-driven, in-workflow email coaching to measurably improve cold email reply rates
- Pricing
- Free (5 emails/mo); Starter $29/mo; Individual Pro $49/mo; Teams $69/user/mo; Enterprise custom (~$89+/user/mo). Annual billing saves ~20%. Ora AI agent add-on at $500/agent/mo.
Key features
- Real-time email scoring with actionable rewrite suggestions
- Prospect research and communication-style matching in sidebar
- Team coaching dashboards with rep-level analytics
- Gmail and Outlook integration via Chrome extension
- Ora autonomous AI sales agent for prospect research and email drafting
Quick answers
- What is Lavender?
- Lavender scores sales emails in real time against a dataset of billions of B2B emails and suggests specific fixes to subject lines, length, tone, and personalization. Paid tiers add prospect research, communication-style matching, and team coaching dashboards. In 2026, Lavender also launched Ora, an autonomous AI sales agent that researches prospects and drafts personalized cold emails independently.
- Why does Lavender matter?
- Lavender is one of the few sales-email tools with a genuine data moat — its scoring model is trained on billions of real B2B emails and improves as usage grows. With the launch of Ora, it is expanding from a coaching layer into the AI SDR market, which changes the competitive comparison set from writing aids to full outbound automation platforms like Amplemarket or AI SDR agents. Teams evaluating it in 2026 should note that the core Email Coach and Ora are priced and sold separately, and the Teams tier ($69/user/mo) compounds quickly at scale. The main lock-in risk is workflow dependency — reps trained on Lavender's scoring rubric can struggle to calibrate quality without it.
- What is Lavender best for?
- SDRs and sales managers who want data-driven, in-workflow email coaching to measurably improve cold email reply rates
- How much does Lavender cost?
- Free (5 emails/mo); Starter $29/mo; Individual Pro $49/mo; Teams $69/user/mo; Enterprise custom (~$89+/user/mo). Annual billing saves ~20%. Ora AI agent add-on at $500/agent/mo.
Know the terms
Glossary entries that explain what Lavender is built on.
Data flywheel
A self-reinforcing loop where user interactions generate data that improves the model, which attracts more users, generating more data. Data flywheels are among the strongest moats in AI.
AI-first GTM
A go-to-market strategy where the product's core value proposition, distribution channels, and growth loops are built around AI capabilities from day one, rather than adding AI features to an existing product.
Vertical AI
An AI product purpose-built for a specific industry, such as legal, healthcare, or finance. Vertical AI startups compete on domain expertise and data moats rather than raw model capability.
Co-pilot
An AI assistant that works alongside a human user within an existing workflow, providing suggestions, automating sub-tasks, and augmenting productivity while keeping the human in control of final decisions.
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