RevSage.ai gives your reps three things no other tool does: a deep behavioral profile for every contact, a cohort-matched projection for every deal, and a recommended next move with the evidence behind it.
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.
Capillary Technologies (Anant Sharma, COO) · Health: 0.71 · Win Prob: 0.61 · Active Deal
Send InVideo case study to Anant. Expected value: +$20,520 (26 hours remaining)
One profile. Four perfect approaches.
To: Anant (D/C-Blend + INTJ) · Tone: Architecturally robust, logic-first
Subject: Anant, the 3-system audit you mentioned
Anant,
Looking at Capillary'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: Anant (D/C-Blend + INTJ) · Tone: Efficient, concept-first
Anant - Impressive move into the COO seat at Capillary.
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 Anant
To: Anant (D/C-Blend + INTJ) · Tone: Structured, data-heavy summary
Anant,
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 projects what's about to happen, and tells you what to do about it, with cohort-matched evidence for every claim."
The Buyer Psychology Profile (BPP) is constructed in real-time from 147 signal types across your entire revenue stack.
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. Next best action, 6 parallel recommendations, 4 anti-actions (do-not-do), and deferred recommendations (wait-for-trigger).
Buyer profiles. Gives you a buyer profile. Tells you what to do with it, and tracks whether it worked.
Outbound at volume. Apollo wins volume. Clay wins flexibility. Wins depth for the deals where every touch matters.
Call recording. They analyze what happened. Decides what to do next.
Sequencing. They execute the sequence. Decides which sequence to run for each buyer.
Generic AI. A generic LLM has no idea what closed your last 67 deals. We do.