enteract.ai
Setup
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Connect your data

Connect your product, revenue and CRM systems. Enteract's agent reads them directly — no schemas to define. Connect at least three so it has enough signal to model your business.

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Tell us about your business

A few sentences on your product, how you make money, and what a healthy customer looks like. The agent combines this with your connected data to decide the value zones — so they fit your business, not a template.

Start from an example:

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What the agent proposed

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{{ zoneCountLabel }} value zones generated

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Value Score definition

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Score = {{ t.op }}{{ t.weightLabel }}·{{ t.key }}

A transparent starting formula, not a black box. You can fine-tune each weight later from any zone's drill-down.

Deriving zones from your data
and business description…

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Customers

What you can already see about your paying customers — no Enteract in this view yet. {{ custPayerLabel }}.

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At-risk payers · sorted by recency ratio

gap since last purchase ÷ their own typical gap
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Payer conversion

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Purchase frequency

How many times each payer has purchased — the shape behind your "average".

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Deeper patterns · full 180-day history, not scoped to the range selector above

Cohort revenue curves

Each line is one signup month, tracked from M0 — newer cohorts just haven't had time to grow yet.

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Whale curve

Cumulative revenue share vs. cumulative payer share — the dashed line is what "everyone spends the same" would look like.

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{{ custWhaleTop5Label }} of all revenue comes from your top 5% of payers.

Spend velocity

{{ custVelocityN }} payers, by trajectory

Comparing each payer's recent spend to their own earlier spend.
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Overview

Verified against holdout
{{ dashSavedLabel }} incentive budget freed

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Every incentive dollar, better utilized — vs the no-incentive holdout

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Waste avoided

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Your flat rule would have spent {{ wastedSub }}.

Cumulative savings

The gap between the flat rule and Enteract, growing over the test.

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Campaign scoreboard

1 live pilot · rest projected from the pilot rate
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{{ r.name }} live pilot within CI
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Deciding right now

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Campaigns

Your existing campaigns, pulled from your CRM. Keep your segments and triggers exactly as-is — duplicate one with an Enteract arm to A/B test the incentive value per user.

{{ campaignCount }} campaigns · CRM connected
{{ r.crmMark }} {{ r.name }} Enteract A/B {{ r.statusLabel }}

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{{ r.channel }} · {{ r.crmName }} Open A/B test Duplicate with Enteract

A/B test · {{ abName }}

The split

When {{ abTrigger }} fires, the CRM asks Enteract for the minimum value that will move each user. Same trigger, same audience — only the value differs.

Arm A · Control

Flat rule

Every user gets the identical value the current campaign sends.

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Arm B · Enteract

Decided per user

Distribution of the values Enteract assigned — the whole pitch in one chart.

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{{ zeroPctLabel }} sent nothing — they would convert anyway. Every one of them is spend the flat rule would have wasted.

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Guardrails

read-only

Value grid · {{ guardRange }}

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Budget cap

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Exploration rate

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Global holdout

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A/B test · {{ abName }}

Results

Verified against holdout

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{{ a.name }} {{ a.nLabel }} users

Conversion rate

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Cost / conversion

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Total spend

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Cumulative incentive spend

Same conversions, diverging cost — the gap between the arms is incentive budget freed.

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A/B test · {{ abName }} · Enteract arm

Why each value

Pick a user in the Enteract arm. The model's factor contributions sum to 100% — each pushed the value down (saves you money) or up.

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↓ pushed value down · saves you money pushed value up ↑
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A/B test · {{ abName }}

Live feed

Triggers firing in real time. Each decision shows the value Enteract sent and the single factor that mattered most.

Engine live
{{ r.valueLabel }} user {{ r.userLabel }} · top factor {{ r.factor }} {{ r.arrow }} explore sent

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Analytics

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Engine live

The Value Curve

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{{ popLabel }} users · median score {{ medianScore }} · zones designed by the agent from your business model — click one to inspect it

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0 · {{ firstZoneName }} Value Score — population density 100 · Churned

Read: {{ curveDiagnosis }}

{{ selZoneName }} — zone drill-down

{{ selZonePop }} users

Agent definition — {{ selZoneDesc }}

How this score is computed

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A transparent weighted formula — the defaults are Enteract's; drag any weight to make the definition your own. Boundaries between zones stay editable in Goals.

Score = {{ t.op }}{{ t.coef }}·{{ t.key }}
{{ t.label }} {{ t.coef }}={{ t.weightLabel }}

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Where this zone is heading — next 30 days

needs event history
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Lifecycle moves left → right only. Users can skip stages — {{ skipExample }} — but never move backward. Heat shows the share of this zone's {{ selZonePop }} users projected to land in each stage; arcs mark the dominant paths.

Decision distribution — intent × channel

Email Push In-app {{ r.label }} {{ c.val }}% No action {{ noActionPct }}% — deliberate silence

Share of today’s decisions in this zone. Guardrails from the active goal cap offers and total touches.

Who is in this zone — segment profile vs all users

partner-provided attributes
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Past decisions in this zone — one user at a time

Enteract decides per individual user, not per segment. Each call is logged with the user it was made for, the rationale, and that user's measured outcome — wins reinforced, mistakes corrected. That per-user feedback loop is how the policy learns and compounds ROI over time.

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Why — {{ d.rationale }}

Outcome

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Training signal

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Business impact

The boardroom numbers — what a champion takes to their own leadership to justify renewal.

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Boardroom KPIs

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Locked

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System health — engine metrics

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Value allocation efficiency

needs partner logs

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day 0 · {{ effStart }}% today · {{ effNow }}%
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Enteract policy (OPE estimate){{ effRecoVal }}
Estimated lift {{ effLift }} {{ effCI }}

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Off-policy estimate · reliability depends on action overlap ⓘ

What the agent is doing, per zone

live decisions

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{{ row.name }} {{ row.silent }}% silent
Email Push In-app Discount / offer No action

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Goals

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Zones — goals are built on these

Drag a boundary to redefine a zone. {{ zoneEditorHint }}

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Playbook templates no history needed

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{{ g.zoneName }} zone · Target: {{ g.target }}

AI-tuned
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Decision history — {{ g.zoneName }}

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Enteract decides one user at a time. Each per-user call this goal made is logged with the user, the rationale, and that user's outcome — reinforced when it worked, corrected when it didn't. This is the training signal behind the goal's ROI.

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Why — {{ d.rationale }}

Outcome

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Training signal

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New goal

Target: move Value Score toward Core. Completing a goal is an event — whether it shifts the score is defined by this playbook, not assumed.

Hard cap across all channels — the anti-fatigue guardrail.

AI goal optimization

A separate model tunes these parameters over time.

Pick a playbook template or create a custom goal.
Every goal starts in shadow mode.

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Ask Enteract

Ask a question about your business in plain language. The agent reads your live value curve, zones and decision logs, then reports back with grounded analysis and a next step.

What do you want to understand?

Pick a question the agent can answer from your data right now — or type your own below.
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enteract.ai

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Reading your curve, zones and decision logs…
Try:

Grounded in {{ company }}'s connected data · answers reflect the current scenario and vertical.

The plan — three horizons

Each move becomes a live goal with hard guardrails. Pick one to set it up.

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