Whether you live in dashboards or feel overwhelmed by them — Venti reads your connected stack, finds what's actually wrong, ranks fixes by impact, and runs the plan after you approve. It doesn't replace your strategy; it gets you from noisy signals to one ranked plan you can approve — saving time and the cost of fixing the wrong thing. Signal to action in seconds, not hours.
“I checked Meta, Klaviyo, and GA4. Three hours later, I still don't have a root cause.”
You understand ROAS, CPM, and CAC. The problem isn't knowledge — it's throughput. Manually cross-referencing 6+ platforms to find a hidden correlation takes hours you don't have.
“I know revenue is down. I just don't know why — or what to do about it.”
You don't need to understand z-scores or attribution models. Venti reads your data, explains what's happening in plain English, and walks you through a ranked plan — you approve or skip any step.
A real example
The most common and most expensive problem in e-commerce right now — and the hardest to diagnose because every tool tells a different story.
Example scenario — illustrative timeline and numbers; real runs depend on which integrations are connected, which feeds are enabled, and your approval settings.
How it works
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.
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.
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.
Platform capabilities
Each capability feeds directly into the next — from detecting a signal, to understanding its cause, to executing a response, to learning from the outcome. Together they form one continuous loop.
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.
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.
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.
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.
Why this exists
More data doesn't fix blind spots. Clarity comes from seeing how your signals line up across tools — and what to do next. We help you get that story in one place, without replacing your measurement stack.
General-purpose AI chats are powerful for reasoning when you bring the context. Venti is built for what those sessions can't do by default: stay connected, keep pulling, and remember what “normal” looks like for your stack.
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're already spending hours reconciling tabs before every serious call — that's the hidden tax. Venti uses scheduled pulls, structured runs, and context (baselines, what's connected) so you're not re-exporting and re-prompting every week. The alternative isn't “no tool” — it's unpaid time staying one step behind the numbers.
No. We're not selling pixels or a new revenue source of truth. Venti sits on data you already have in connected tools — correlation, diagnosis, ranked options, approval — without replacing your measurement setup. We help you decide what to trust when platforms disagree.
Yes. With more sources wired in and more history, baselines and drift detection get sharper for your account. It's not magic — it's fewer blind spots as the system learns what “normal” means for you.
Here are three real-pattern types from a single e-commerce run. You could find each of these manually — the problem is finding all of them simultaneously, across six platforms, before the window closes.
Every major platform will ship its own AI. None of them will tell you which number to believe when Meta, Google, and your store disagree. Venti is early access: operators wiring real stacks, shaping what ships next — with preferred pricing for teams who help define it.
Request early access