One system to understand how your company sells.

The system

Five questions, in order.

Understand what is happening. Identify what deserves attention. Act. Learn from what happened. Make what works part of how the company sells.

1. Understand What is happening in the company's GTM motion? Conversation intelligence, CRM context, Buyer and persona intelligence
2. Identify Which patterns, risks, behaviors, and opportunities deserve attention? Behavioral analysis, Deal-risk detection, Voice signals (experimental)
3. Act What should the founder, AE, manager, or GTM leader do next? Next-best-action, Email, LinkedIn, and WhatsApp context, Meeting support
4. Learn What happened after the intervention? Deal trajectory analysis (Being validated)
5. Keep What should become part of the company's repeatable GTM system? Repeatable workflows, Onboarding context for new hires (Being validated)

Conceptual diagram. Layers 01 to 03 draw on capabilities RevSage has built. Layers 04 and 05 are the part of the system we are validating with customers.

Supporting capabilities

What sits underneath.

These are components of the system, not separate products to buy one by one.

• Conversation intelligence Analyzes recorded sales conversations to surface topics, objections, and how each conversation moved.
• CRM context Brings deal, stage, and outcome context to the analysis so conversations are read against what happened next.
• Buyer and persona intelligence (PersonaSage) Builds directional profiles of buyers and buying committees from public and conversational data. Directional, not a verdict on a person.
• Behavioral analysis Looks at how stakeholders and sellers behave across a deal, such as who engages, who goes quiet, and what changes.
• Deal-risk detection Flags deals whose pattern resembles ones that stalled, so a person can look closer.
• Voice signals (VoiceSage) Tone and pacing signals from call audio, treated as an experimental input rather than a conclusion.
• Next-best-action Suggests what a founder, AE, or manager might do next, with the reasoning behind it.
• Email, LinkedIn, and WhatsApp context (EmailSage, LinkedInSage, WhatsAppSage) Keeps written and messaging context alongside calls so follow-ups reflect the whole conversation.
• Meeting support (MeetSage) Notes and context around meetings, available before and after the call.

Channel-tuned artifacts

One read of the buyer. A different artifact for each channel.

The same analysis becomes an email, a LinkedIn message, a discovery brief, or a post-meeting follow-up, each tuned to the channel and the person.

Email

Sample: To: John (D/C-Blend + INTJ) · Tone: Architecturally robust, logic-first Subject: John, the 3-system audit you mentioned

John,

Looking at Acme's loyalty and martech stack, the operational drag of stitching reports across three systems usually hits a wall around Q2 planning.

We built a new causal-inference architecture that holds accuracy at scale and unifies those reporting layers without the ETL overhead. We ran the test against the InVideo dataset and found a 3.2x stabilization on forecast precision via cohort matching.

Are you open to reviewing the 2-page teardown on how we achieved this?

Best,
[Rep]

LinkedIn DM

Sample: To: John (D/C-Blend + INTJ) · Tone: Efficient, concept-first

John - Impressive move into the COO seat at Acme.

Quick question: with the new loyalty pipeline expansion, are your predictive models holding accuracy on the enterprise segment, or are you seeing the typical drift in reporting?

Discovery brief

Sample: Brief for rep: discovery call with John

• Role context: Stepped into COO recently. Accountable for cost-to-serve and time-to-insight across the martech suite.
• Patience for fluff: Zero. He values intellectual rigor above all else. Board reporting cadence is top of mind.
• Discovery Q1: "How are you currently handling the latency between the loyalty engine and the central reporting warehouse?"
• Discovery Q2: "When the models drifted last quarter, what was the downstream impact on the board's visibility?"
• Discovery Q3: "If we could collapse that ETL step entirely, how does that shift your team's resource allocation?"
• Playbook: "I'll get straight into the architecture to see if we match your infrastructure requirements." Show the architecture diagram. Avoid emotional storytelling.

Post-meeting

Sample: To: John (D/C-Blend + INTJ) · Tone: Structured, data-heavy summary

John,

Appreciate the rigorous discussion on the logic layer. Per your request on the cohort-matching mechanism:

• Attached: The technical deep dive on our causal inference engine.
• Attached: The security and data retention overview.
• API Rate Limits: Documented on page 4 of the architectural doc.

Agreed next step:

I will follow up strictly with Rohit (VP Eng) to validate the integration surface before we discuss commercial terms. Let me know if Thursday [Date Placeholder] works for that technical sync.

Samples written for an illustrative buyer to show how artifacts are tuned to channel and recipient. The names, company, and figures in the samples are examples.

Multimodal signals

An experiment, not our claim.

RevSage can analyze transcripts and CRM context, and, where it is legally and technically appropriate and consent is in place, voice and video signals as well.

We do not claim that facial expressions or voice reliably reveal what someone feels, and we do not lead with emotion AI. We treat these signals as inputs to test: compare what the analysis finds from transcripts and CRM data alone with what it finds when additional signals are included. Only if the extra signals clearly improve useful findings do they become something we would stand behind.

Under the hood

The data architecture, for the technical reader.

RevSage works from 147 signal types across your revenue stack, combining behavioral, voice, and computer vision signals. Voice and computer vision signals are experimental inputs, as described above. Effect estimates carry explicit confidence intervals and are estimates, not proof.

1. L0 Identity and context (Stable, deterministic, Daily) Person identity, career trajectory, company context, network, contactability, compliance.
2. L1 Raw signals (Unified schema, Real-time) 147 signal types captured across 21 channels (online meetings, phone calls, email threads, LinkedIn, CRM events, document engagement).
3. L2 Behavioral patterns (Decay-weighted, Event-driven) 142 patterns across 14 categories including timing and rhythm, decision-making style, and cross-modal patterns.
4. L3 Psychological inferences (Behavioral modeling, Real-time inference) 102 inferences including DISC, MBTI, Big Five, communication authenticity, and topic-conditional personality. Directional intelligence that sharpens over time.
5. L4 Contextual deal state (Per-deal snapshot, Sub-minute) Buying committee state, engagement state, objection state, commitment state, active risk flags, and inflection windows.
6. L4.5 Trajectory and forward projection (Cohort-matched causal estimation, Every 12 hrs) Not RL. Causal effect estimation with explicit confidence intervals. Interventions and anti-actions ranked by expected value.
7. L5 Recommendations (Action layer, Every 4 hrs) The top play, plus 6 parallel recommendations, 4 anti-actions (do-not-do), and deferred recommendations (wait-for-trigger).

How it fits

Built to work with what you already have.

• Starts from your data Recorded conversations and CRM context you choose to share, including exports.
• You control the data CSV-first control is available as an option, and zero data retention is available.
• Findings reach the team We agree with you where findings should appear so they show up in the workflow your team already uses.

Read the security page

See what RevSage would find in your GTM motion.

Start with the Founder GTM Diagnostic. If you want to see how the buyer-reading capability works first, you can try the Buyer Profiler on a single buyer.

Talk to us Try the Buyer Profiler