Pulse AI.

The problem

Enterprises spend billions on customer engagement across push, email, SMS, WhatsApp, in-app messaging, and loyalty programs — but most systems optimize campaigns, not customer outcomes. Platforms like Braze, MoEngage, Salesforce, and WebEngage are rule-based orchestration tools: they send, but they don't decide whether a message should be sent at all.

The result is wasted spend, communication fatigue, opt-outs, and margin erosion. Lifecycle PMs can't tell which message drives opt-outs, whether suppressing a comm loses revenue, or that a customer with a poorly settled claim is being blasted with marketing. As one PM put it: "I'm rebuilding the same lifecycle in Braze every quarter, and when leadership asks if our 9am send drove revenue I'm guessing."

The solution

Pulse AI (Pulse Customer Intelligence) is a customer behavioral intelligence layer that sits in the white space between orchestration and customer-level decisioning. Rather than optimizing campaigns, it optimizes customer outcomes — recommending the highest-value intervention for each individual user.

For every user, Pulse recommends send, suppress, wait, best channel, or incentive-vs-no-incentive, backed by suppression intelligence and behavioral state scores for purchase intent, churn risk, and fatigue. Each recommendation carries a predicted impact score, confidence level, expected incremental value, and a plain-language reasoning summary. The MVP focuses on intelligence, not automation: a human stays in the loop to execute or reject.

How it works

Pulse builds behavioral intelligence across five layers — semantic event understanding, behavioral state intelligence, intervention prediction, suppression intelligence, and a continuous feedback loop. It is deliberately cost-aware: the runtime hierarchy is raw event → taxonomy lookup → embeddings/rules → LLM only when unresolved, so models are called for ambiguity rather than for every user.

Models are assigned by task: gpt-5.1 for event taxonomy classification and Voice-of-Customer reasoning, gpt-5-mini for runtime fallback and NBA explanation, and gpt-4o-transcribe for audio. All LLM outputs are strict JSON with confidence scores. Customer protection rules (quiet hours, NPS thresholds, open support tickets) and business limits override any recommendation. A golden suite of 50 synthetic scenarios reached a 98% executable pass rate, with 100% hard-constraint compliance.

Who it's for

Pulse is B2B, targeting enterprises across ecommerce, fintech, travel, subscription, and food delivery with high communication volumes and existing CRM systems. Within the account, the economic buyer is the VP of Growth, the primary user is the Lifecycle PM, the technical buyer is data/martech, and legal/security/compliance approves.

Primary users are CRM and lifecycle marketing leaders responsible for retention and engagement; their goals are more revenue through better targeting, reduced marketing spend, and higher customer LTV. Revenue is a subscription fee plus usage-based pricing per 1,000 MAUs scored.

Why it matters

The stakes are wasted marketing budget, customer churn from over-contact, and the inability to prove which sends actually drive incremental revenue. Pulse targets enterprise marketplaces growing at a projected 20–50% over five years, with no established competitor occupying the customer-level decision-intelligence layer.

Rollout is deliberate. Each new client goes through an exhaustive data-connection setup and a 7-day observe period to solve the cold-start problem, then a narrow pilot (one journey, 5,000–50,000 users, a control-vs-test comparison) before 100% rollout. Pulse starts in recommendation mode; autonomous execution is unlocked only once enterprise confidence is established. The defensible moat is behavioral intelligence and longitudinal memory, not commoditized copy generation or orchestration.

At a glance

Project

Pulse AI

Built by

Vijeta Marwah

One-liner

Pulse AI is a decision intelligence layer for lifecycle and growth product managers at high-volume consumer businesses.