RevSage.ai runs live root-cause analysis on every deal, reading even what's unsaid, then hands your reps the ammunition to win: hyperpersonalized artifacts built for each person on the buying committee.
We sit on top of your existing tools to tell your reps what to do next.
Every recommendation comes with explicit confidence intervals, cohort evidence, and the inferences and signals that produced it.
Acme Technologies (John Smith, COO) · Health: 0.71 · Win Prob: 0.61 · Active Deal
Send InVideo case study to John. Expected value: +$20,520 (26 hours remaining)
One read. Four perfect artifacts.
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]
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?
Brief for Rep: Discovery Call with John
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:
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.
"Most tools surface what happened. RevSage.ai runs live root-cause analysis on every deal, flags the risk before it kills the deal, and hands you the ammunition to change the outcome."
RevSage's live root-cause analysis runs in real time on 147 signal types across your entire revenue stack, combining behavioral, voice, and computer vision signals, even what's unsaid.
Stable, deterministic · Daily. Person identity, career trajectory, company context, network, contactability, compliance.
Unified schema · Real-time. 147 signal types captured across 21 channels (online meetings, phone calls, email threads, LinkedIn, CRM events, document engagement).
Decay-weighted · Event-driven. 142 patterns across 14 categories including timing & rhythm, decision-making style, and cross-modal patterns.
Behavioral modeling · Real-time inference. 102 inferences including DISC, MBTI, Big Five, communication authenticity, and topic-conditional personality.
Per-deal snapshot · Sub-minute. Buying committee state, engagement state, objection state, commitment state, active risk flags, and inflection windows.
Cohort-matched causal estimation · Every 12 hrs. Not RL. Causal effect estimation with explicit confidence intervals. Interventions and anti-actions ranked by expected value.
Action layer · Every 4 hrs. The top play, plus 6 parallel recommendations, 4 anti-actions (do-not-do), and deferred recommendations (wait-for-trigger).
Buyer profiles. Gives you a buyer profile. Diagnoses the whole deal, and generates the artifact that answers the risk.
Outbound at volume. Apollo wins volume. Clay wins flexibility. Wins depth on committee deals where every touch matters.
Call recording. They tell you what happened on the call. Flags why the deal is at risk, and hands you the ammunition.
Sequencing. They execute the sequence. Decides what each stakeholder needs to see next, and generates it.
Generic AI. A generic LLM has no idea what closed your last 67 deals. We do.