The answer was in your data
weeks ago. You're reading it now.

When your numbers live in different tools, getting to a clear answer takes too long — and the next meeting starts from scratch. Venti pulls your connected data into one ranked plan you approve first, with a record of what you chose and what you skipped.

Bloom Studio · e-commerce · $180k/mo ad spend
ROAS dropped 50%.
You have 4 open tabs and no answer.
Revenue / day
$2,840
 
ROAS
3.8x
 
CAC
$62
 
CPM
$18
 
14 days of wasted spend: est. $14,000+ — your team has 5 theories, 0 plans
Manual diagnosis attempt
Campaign
ROAS
Status
Prospecting
1.4x ↓
?
EOFY Promo
1.2x ↓↓
?
Retargeting
3.4x
OK
Email CVR
— missing
“Checked Ads Manager. Checked Klaviyo. Checked GA4. Still don’t know which one broke.”
3h 42m
and still no root cause
Each day without an answer
costs ~$500 in wasted spend.
Connected to Meta · Klaviyo · Shopify · GA4
Workflow canvas · you approved once
Connected tools execute the fix automatically
The ranked plan runs as a workflow. Each step calls the right API. You watch, not work.
Step 1 · Pause high-frequency ad sets
Ads Manager API · Completed in 2.1s
Step 2 · Rotate in fresh creative set
Creative sync · Completed in 4.8s
Step 3 · Repair welcome email flow
Email platform · Running…
Recovery trajectory: +$7,000 / week
2 / 3 steps complete · every action logged with timestamp and diff
Watcher layer · runs automatically after execution
It watches the fix. So you don’t have to.
Outcome tracking confirms what moved. Failed actions are retried automatically with root cause logged.
Worked
Creative rotation: CPM down 19% in 6h
Sustained · ROAS recovering on track
Went wrong
Email patch hit schema mismatch — auto-flagged
Retrying with corrected payload in 4 min
Auto-retry
Corrected payloadKlaviyo flow re-triggeredVerify email CVR uplift within 60 min
Verified after run · tracked in-product
Revenue recovered. Problem gone. Loop closed.
Revenue / day
$3,240
↑ +76% vs low point
ROAS
3.1x
↑ from 1.9x
CAC
$58
↓ from $84
Time to fix
4.2s
vs 3h 42m manual
Every outcome is stored. The next run starts smarter — tighter baselines, better signal weights, faster diagnosis.
ROAS3.8x
Time saved8h / run
Recovery+76%
Diagnosis4 sec
Built for e-commerce operators running paid media + email
Your data already had the answer.
Now so do you.
Connect your stack. Get the diagnosis. Approve the fix. Watch it run.
Try the demo yourself →

A real example

Your CAC has been climbing
for six weeks. Nobody agrees why.

When CAC drifts, the painful part is often the weeks of back-and-forth — not the chart. Every tool tells a different story, so nobody agrees what to do next.

Example scenario — illustrative timeline and numbers; real runs depend on which integrations are connected, which feeds are enabled, and your approval settings.

📍 The situation: You're a growing e-commerce brand. CAC climbed 34% over 6 weeks. ROAS is down. Your team has five different theories.
Without Venti
1
Week 1–2
CAC starts creeping up. Still within normal variance. Nobody flags it.
2
Week 3–4
ROAS drops noticeably. The team checks Meta, Google, and email dashboards — separately. Each tells a different story.
"Could be iOS attribution." "Maybe seasonality?" "Competitor move?"
3
Week 5
A meeting is called. The team aligns on a theory. A budget decision is made.
The theory is wrong. The actual cause was a competitor bidding aggressively on core keywords 5 weeks ago.
4
Week 6+
The wrong fix was applied. CAC keeps climbing. The team starts the diagnosis over.
No record of what was tried, why, or what it actually did to the numbers.
With Venti
Day 3
Competitor ad spend up 41% on your core keywords
Example: external signal when that feed is connected and enabled — not every run includes every source
Detected
Day 3
Competitor spend ↑ → CPM up 18% → CPC rising → CAC inflating
Illustrative: a ranked, likely explanation — corroboration depends on data in the run
Likely cause
Day 4
4 candidate responses generated and ranked by predicted impact
Example scores: impact / risk / fatigue — ranked heuristically, not a guarantee of business outcome
Evaluated
Day 4
Email re-engagement launched to lapsed 90-day customers
After plan review: can run through your connected tools when you approve (or auto-execute if your workspace allows it)
Executed
Day 18
CAC down 22% from peak. Outcome logged vs. predicted delta.
Example: learning from outcomes when execution and measurement complete
Outcome
Example: signal → plan ready for review vs. weeks of debate
Execution runs after approval unless your org opts for auto-run
Fast
signal → plan (example)
The problem was never lacking data.
It was nobody connecting the dots fast enough.
Every team has dashboards. The ones that move faster connect cause and effect in one place, act before the window closes, and leave a clear record when the same issue shows up again.
222%
Rise in e-commerce customer acquisition costs since 2013
Built to expand

Starting with e-commerce.
Expanding to every business model.

Live now
E-commerce
CAC, ROAS, margins, inventory, fulfilment — plus external feeds when connected
Coming soon
SaaS
MRR churn, expansion revenue, activation drop-off, pricing signals
Coming soon
Coaching & Services
Lead quality, programme completion, client retention, pipeline health

How it works

Three steps from signal
to closed loop.

From a signal to a picture you can review: the same steps each time your numbers move, and a written trail of what you approved and what you did not.

Step 01

It watches everything
that moves your numbers

Connected to your internal systems — orders, ad spend, support volume, payment data — and, when those feeds are connected and enabled, macro and market sources such as interest rates, freight indices, FX, and competitor-style signals.

Coverage depends on integrations and optional external feeds — not every source runs on every tenant. When a configured feed moves, it can surface alongside your KPIs for context.

Live signals · right now
Asia-Pacific Freight Index
External · Freightos
+18.4% ↑
USD / SGD Exchange Rate
External · OpenExchange
–2.1% ↓
Your Customer Acquisition Cost
Internal · CRM
+9.3% ↑
Checkout Conversion Rate
Internal · Analytics
–0.4% ↓
Competitor Ad Spend Velocity
External · SimilarWeb
+31% ↑
⚠ Anomaly detected — freight cost divergence from 90-day baseline
Step 02

It tells you why,
not just what

Most tools tell you a metric dropped. Venti builds a ranked, likely explanation — hypotheses and context from your data and ontology — from what moved to what it may mean for your numbers.

Example narrative: freight up → supplier costs → margin pressure over time. That kind of chain is the best current explanation, not a guaranteed forecast — useful for prioritization and review, not a promise of precision.

Causal trace · freight spike scenario
ExternalAsia-Pacific freight index +18% above 90-day baseline
AnalyseSupplier cost passthrough expected in 4–6 weeks (historical pattern)
MarginInput cost increase → gross margin compression estimated 6–9%
CompetitorCompetitor ad spend +31% this month — bidding pressure on core keywords
LinkCombined: ROAS compression likely 12–15% if no action within 3 weeks
Action neededReallocate budget to owned channels + adjust pricing tier ahead of cost increase
Step 03

It executes the best response,
then learns from it

Generates candidate responses and scores them against impact, risk, fatigue, relevance to the diagnosis, feasibility, and your constraints — then drafts a workflow plan. By default, a human reviews and approves before anything executes in your stack (email, CRM, ads). Optional auto-execution is available where your policy allows it.

When runs complete end-to-end, outcomes can be compared to expectations so the system can learn over time — coverage depends on execution and measurement being enabled.

Candidate evaluation · selecting best response
#1
Shift 20% of paid budget to email re-engagement
Impact: High · Risk: Low · Fatigue: Normal
94
Top ranked
#2
Increase pricing tier before cost increase lands
Impact: Medium · Risk: Medium · Fatigue: Low
71
#3
Pause bottom-quartile ad campaigns immediately
Impact: Medium · Risk: High · Fatigue: High
58
#4
Launch retention campaign to existing high-LTV accounts
Impact: High · Risk: Low · Fatigue: Normal
68
<90s
From a flagged signal to a plan you can review quickly
50+
Signals monitored per business in real time
4–6
Several options compared side by side before any action
1
Record per run of what you chose and what happened — within what your workspace logs
1
Dashboard — you only need ours. No manual thresholds to configure.

Platform capabilities

Four layers.
One loop.

Each layer leads to the next: spot that something moved, explain what most likely caused it, compare options side by side, then — when your tools and your rules allow — run the steps and learn from what happened. One clear order — not another screen you scroll alone.

01

Autonomous
Monitoring

Connected to your CRM, finance tools, support desk, and product analytics where you've connected them. When a KPI moves outside its expected range — e.g. churn or payment failure signals — it flags anomalies and surfaces likely drivers from the data and ontology. Baselines and integrations are required; not a replacement for thoughtful setup.

Ingestion & baselinesKPI anomaly detectionHypothesis & diagnosisMulti-platform
Live workspace · KPI monitor
Customer Acquisition Cost
Internal · CRM
+34% ↑
ROAS · All Channels
Internal · Ad Accounts
–22% ↓
Competitor Ad Spend Velocity
External · SimilarWeb
+41% ↑
Checkout Conversion Rate
Internal · Analytics
–4.2% ↓
Anomaly detected — CAC above baseline. Example: may correlate with competitor signals when those feeds are available; diagnosis is ranked, not infallible.
02

Ontology
Context

Knows how your business category works — not just your internal data, but how external events translate into business impact. If a logistics disruption hits your supply chain, the system already understands it will affect inventory lead times, then fulfilment rates, then customer satisfaction — in that order. That domain knowledge is built in, not configured.

Causal graph3-layer OWL modelVertical contextSemantic enrichment
Knowledge layer · causal model
Global Event Detected
Competitor entered core keyword space · confidence 87%
E-Commerce Domain · Paid AcquisitionL2 Domain
CPM rising → CPC increasing → CAC inflating
Search impression share dropping on branded terms
Vertical: Fashion & ApparelL3 Vertical
High price sensitivity — discount elasticity 1.4×
Email retention outperforms paid re-acquisition in this vertical
Likely read: Competitor pressure may be driving CAC; owned-channel re-engagement can be a strong response for this vertical — ranked for review, not guaranteed.
03

Platform
Automation

Can execute actions through the tools you connect — e.g. pausing an ad set, triggering email, updating CRM — after a generated plan and, by default, human approval. Same stack you already use; audit-style logging of what was proposed and what ran when execution is enabled.

Native connectorsParallel executionConstraint enforcementFull audit trail
Execution workspace · live run
HubSpot Connected
Gmail Connected
Sheets Connected
Meta Ads Connected
Segment lapsed customers (90-day window)
0.4s
Pause bottom-quartile Meta ad sets
1.1s
Launch re-engagement email sequence via Gmail
running…
Log outcome to Google Sheet + CRM note
queued
Audit log
Triggered byVenti · Competitor pressure event
Approved byHuman review (default) or policy auto-approve
Contacts in scope2,847 · fatigue check: ok
04

Multi-solution
Evaluation

Before execution, it generates candidate responses and scores them against impact, risk, fatigue, relevance to the diagnosis, feasibility, and your constraints — heuristic scores, not guaranteed ROI — then ranks options and documents reasoning so you can see what was considered and why one path ranked first.

Candidate generationComposite scoringRisk-adjusted rankingDecision explanation
Evaluation workspace · ranked candidates
#1
Shift budget to email re-engagement — lapsed 90-day
Impact
Risk
Fatigue
94
Selected
#2
Raise pricing tier ahead of input cost increase
Impact
Risk
Fatigue
71
Ranked 2
#3
Pause bottom-quartile ad campaigns immediately
Impact
Risk
Fatigue
58
Ranked 3
⚡ Example: plan ready for approval — then runs via connected tools if approvedOutcomes when execution and learning complete
Illustrative outcome
e.g. CAC improvement · learning over time when measured

Why this exists

An intelligence layer —
not a chat window, not a pixel product.

AI inside each ad or analytics product only sees that product. Venti stays connected across your stack, shows how the pieces fit together, and spells out what to try next — in plain language your finance team or leadership can read. It does not replace how you measure.

Two types of operators. Both leaving money on the table.

If you know your numbers cold
“I checked Meta, Klaviyo, and GA4. Three hours later, I still don't have a root cause.”

The bottleneck isn't skill — it's stitching platforms fast enough to see the hidden link.

If the dashboards feel overwhelming
“I know revenue is down. I just don't know why — or what to do about it.”

The bottleneck isn't effort — it's a clear read across tools when every surface tells a different story.

FAQ

Those copilots are built to help you work inside each platform's garden — fast reports, anomalies, suggestions on their data. Venti is for the gap they can't close: when Ads, your store, and other spend tell different stories, and you need one ranked read of what moved and what to do next. Same stack, different job — “drive the business” vs “drive the ad account.”

You have two paths

Keep reconciling by hand —
or own the story in one layer.

When Meta, Google, and your store disagree, the cost is time in meetings and rework — not a lack of charts. Early access is for teams who want one place to agree on what happened, what to do next, and what you decided not to do — with preferred pricing for teams who help shape the product.

Request early access