Every interaction makes your
entire system smarter
Most outreach tools treat replies as an endpoint. Ours treats them as training data. Every email reply and call transcript is analyzed by AI, aggregated into actionable insights, and turned into concrete suggestions that improve your AI agents, refine your personas, and shape your company strategy.
Total Replies
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Calls Analyzed
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Avg Interest
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Positive Rate
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Top Intent
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"We don't have room in Q2 budget for new tools"
"We're locked into a 12-month contract with X"
"Check back after our Series B closes"
Budget approved, decision timeline mentioned
Outreach.io, Apollo, Salesloft
Prospect spoke more than rep
Prospects use pricing as an email brush-off but rarely raise it on calls
Prospects who book calls are significantly more engaged
Every reply and call is classified
by AI the moment it arrives
When a prospect responds to any campaign email, the system automatically analyzes their reply across 9 dimensions. When they join a call, the transcript is analyzed too -- with additional dimensions like buying signals and competitive mentions that only surface in conversation.
9 dimensions extracted from every reply
Analyzed automatically the moment a reply or call transcript arrives
Sentiment
Positive, negative, neutral, or mixed
Intent
Interested, objection, question, meeting request, referral, and more
Interest level
1-5 score of how engaged the prospect is
Objections raised
Specific pushback extracted verbatim
Questions asked
What information the prospect is seeking
Topics mentioned
Key themes and subjects in the reply
What worked
What about your original email resonated
What didn’t work
What fell flat or triggered a negative response
Summary
AI-generated 1-2 sentence summary of the reply
Zoom in on a campaign or zoom
out to your entire market
Insights aren't just campaign-level. Every reply analysis rolls up into pre-computed aggregations at four scopes -- so you can compare products, evaluate agents, drill into campaigns, or see the big picture.
Product level
How do prospects respond when you pitch a specific product? Compare reception across your product line.
Agent level
How is each AI sales rep performing? Which agent’s tone and approach gets the best results?
Campaign level
What’s working in a specific campaign? Drill into objections and engagement for each outreach effort.
Company level
The full picture across all campaigns, agents, and products. Your overall market reception at a glance.
Your calls are intelligence too
When a prospect books a call, the transcript is fetched from Google Meet and analyzed by AI -- extracting dimensions that only surface in live conversation. Call insights merge with email insights to give you the complete picture.
Call-specific dimensions beyond email analysis
Extracted from every analyzed call transcript
Buying signals
Indicators of purchase intent -- budget mentions, timeline discussions, stakeholder involvement
Competitive mentions
Which competitors came up, what was said about them, and how your offering compared
Prospect talk ratio
How much the prospect spoke vs. listened -- a strong indicator of engagement
Key moments
Critical turning points in the conversation: objections overcome, interest spikes, or concerns raised
Next steps
What was agreed upon: follow-up meetings, demos, trials, or introductions to decision-makers
Pricing discussed
Whether pricing came up and how the prospect reacted -- crucial for deal qualification
Email vs. call divergences
Prospects don't always say the same things in email as they do on a call. The system surfaces where sentiment or objections diverge between channels -- revealing what people really think and helping you tailor your approach to each medium.
Intelligence that doesn't just sit
in a dashboard
This is what makes the system compound. Aggregated insights from emails and calls are synthesized by an agentic AI, then turned into concrete suggestions that improve three parts of your system simultaneously.
Data flows in from emails & calls
Every reply is classified across 9 dimensions. Every call transcript is analyzed for buying signals, objections, and key moments. Aggregations update in real time.
Agentic AI synthesizes patterns
When enough new data accumulates, an AI agent uses tools to search, verify, and cluster patterns across all your data into qualitative intelligence.
AI agent suggestions
Field-level suggestions to improve your agent's outreach instructions, reply handling, follow-up sequences, and objection responses -- with before/after diffs and reasoning.
Persona suggestions
Refine existing personas, improve search queries, discover entirely new personas from market signals, or deprecate underperforming ones -- all driven by real data.
Company strategy suggestions
High-level strategic directions based on what the market is telling you: positioning adjustments, product fit signals, messaging refinements, geographic opportunities.
The compounding effect
Campaign 1 generates replies and calls. Those become insights. Insights generate suggestions that improve your agents, personas, and strategy. Campaign 2 runs with better targeting, better emails, and better reply handling. Its data compounds on top. The longer you use it, the smarter everything gets.
Not just charts. Actual intelligence
you can act on.
A pie chart showing 42% positive sentiment doesn't tell you what to do differently. The AI goes deeper -- clustering raw data from both emails and calls into themes, explaining why patterns exist, and giving you evidence-based recommendations grounded in real interaction data.
Objection themes
Raw objections clustered into semantic themes with frequency, reply counts, and real example quotes.
Question themes
What information prospects are seeking, grouped by underlying need -- not just keyword matching.
What resonates
Messaging patterns that generate positive engagement, with explanations of why they work.
What falls flat
Patterns that trigger negative responses or disengagement, with explanations of why they fail.
Market signals
Non-obvious cross-cutting patterns: differences between initial vs. follow-up responses, emerging trends, correlations.
Call-specific insights
Buying signals, competitive mentions, and key moments extracted exclusively from call transcripts -- patterns visible only in live conversations.
Email vs. call divergences
Where email sentiment or objections differ meaningfully from what prospects say on calls -- revealing what people really think.
Executive summary
A 2-4 sentence synthesis of the most important takeaways -- actionable and evidence-based.
From reply to smarter email in four steps
Step 01
Prospect replies or books a call
A reply is detected in real time via your connected Gmail. When a prospect books a meeting, the call transcript is fetched from Google Meet automatically.
Step 02
AI classifies across 9+ dimensions
Sentiment, intent, interest level, objections, questions, what worked, what didn’t -- plus call-specific dimensions like buying signals, competitive mentions, and prospect talk ratio.
Step 03
Insights aggregate and AI synthesizes
Each analysis updates aggregations at product, agent, campaign, and company scopes. An agentic AI synthesizes qualitative themes across all your data.
Step 04
Suggestions surface across your system
Intelligence drives concrete suggestions: improve your AI agent’s email instructions, refine persona targeting, discover new personas, and shape company strategy.
Everything the intelligence system can do
Real-time classification
Every reply and call transcript is analyzed the moment it arrives. No batching, no delays -- insights update continuously.
9-dimension analysis
Sentiment, intent, interest level, objections, questions, topics, what worked, what didn’t, and a summary -- for both emails and calls.
Call transcript analysis
Google Meet transcripts are fetched and analyzed with AI -- extracting buying signals, competitive mentions, key moments, and next steps.
4-scope aggregation
Insights roll up at product, agent, campaign, and company levels -- so you can zoom in or out.
AI-powered theme clustering
Raw objections and questions are clustered into semantic themes with frequency and real quotes.
Self-learning feedback loop
Insights feed directly into email generation prompts, making every campaign smarter than the last.
AI-generated suggestions
Intelligence drives concrete suggestions for your AI agents, personas, and company strategy -- not just dashboards.
Per-persona performance
Track how each persona converts: sentiment, interest, top objections, what resonates -- with AI-assessed performance tiers.
Evidence-based insights
Every theme includes reply counts, example quotes, and confidence levels. No speculation.