The data engine to grow revenue
Trusted by the world's boldest global brands. Inspired by our customers. Built with evidence. See how they run it
The same signal, 13–20 days earlier
Real client backtest · decision signals
Growth team lead · SHEIN
Tripo
CapCut“The new-angle proposal cited our own measured ER — first rec that came with its evidence.”
Head of Content Marketing · CapCut
+41%New-angle content ER
“First system that puts VOC and paid data on one evidence table.”
Head of Overseas Marketing · Tineco
“Store POS, e-commerce and inventory on one timeline — anyone can come ask for the answer.”
Head of Global Operations · MINISO
-33%Paid weighted CPM · reallocation model
5.4×Measured cost gap between two plays
“Competitor moves and customer voice from the other timezone, visible the same day.”
Global Marketing Director · Insta360
“Demand signals two weeks before the drop — restocking finally ran ahead of the hype.”
Head of Overseas Retail · POP MART
One engine,
five intelligence layers
Sales, ads, inventory, creators, VOC — every source lands in one canonical schema on day one, aligned to a single timeline. From that moment, the agent answers any question, any time.
Statistical detectors sweep the full history and each day's increment. Thresholds self-calibrate to the brand's own distribution. Where data is missing they stay silent rather than invent a number.
For every signal the agent builds an evidence chain three ways: internal, external, combined. Where the evidence doesn't settle it, the report says so instead of forcing a conclusion.
An investigation ends in an action with an acceptance test attached, delivered by the Panoverse ops team. Not a recommendation left sitting in your inbox.
When the measurement window closes, the result is graded automatically. Hits and misses both go into the outcome ledger. The next proposal cites your own measured results.
Q: German-site GMV dipped last week — traffic or conversion?
VOC theme "pet hair tangling" up 3.2× in two weeks, 58% negative
Mentions went 20 → 64 in two weeks (×3.2), 58% negative. Competitor content on the same theme runs 2.1× category median ER, while our live creatives say nothing about it.
- 0 of 16 live creatives mention tangling
- 43 comment questions about it, no official reply
- Creator angles for May aren't locked, so the window is open
- Competitor Miralux: 3 posts, median ER 4.1% (category 1.9%)
- Category search interest +71% over 8 weeks: real demand, not noise
- Vantex hasn't followed; the first-mover window is still open
- Pain is real, the ceiling is market-proven, and we say nothing. All three hold at once
- Expected value of changing angle clearly beats staying put
- Risk is over-exposing one angle; the two-week window contains it
| ID | Metric / item | Value |
|---|---|---|
| ev_h1 | "Hair tangling" two-week mentions | 20 → 64 (×3.2) |
| ev_h2 | Mention account spread | 41 accounts · two platforms |
| ev_h3 | Our live creative audit | 0 of 16 related |
| ev_h4 | Competitor ER, same theme | median 4.1% (avg 1.9%) |
To date · cumulative gain of measured vs baseline
What's happening today? What should we run tomorrow?
Layer 1 · Lay the Plumbing
Data foundation: plumbing for your data, coverage for the global market you can't see
Both data planes land in one canonical schema on Day-0. Campaign, platform, SKU and calendar align to a single timeline, with the full history backfilled. Overseas collection and labeling run on our own pipeline. Nothing to set up on your side.
What plugs in
Triple Whale
Klaviyo
BrazeCommerce orders, inventory and support tickets arrive by platform export or API. OAuth stays read-only and can be revoked at any time.
Read-only OAuth · every available year backfilled on Day-0
Collection: daily snapshots, diffed on ingest
Labeling: 12,400 comments tagged for sentiment + theme
Layer 2 · Intelligence
Intelligence layer: statistics do the math, the agent does the reasoning
A strict division of labor, because a general-purpose model should not be watching your dashboard. Numbers come only from computation; language only explains and orchestrates. Every number passes evidence-grounding, and hallucinated citations are stripped by machine.
VOC theme "pet hair tangling" up 3.2× in two weeks, 58% negative
Layer 3 · Execution
Execution layer: from strategy to delivery
An investigation ends in an action with an acceptance test attached. The Panoverse ops team delivers it, and it's graded automatically when the window closes. Hit or miss, it goes on the record as it happened.
Ships with a measurement contract
Metric, baseline window, measurement window and confound guards are all fixed before you confirm. Nobody decides what counted after the fact.
The Panoverse ops team does the work
Creator briefs, budget reallocation, new creative angles, renewals and supply, on-site alignment. We are the delivery team, not a reseller of advice.
Backfilled when the window closes
Measured results enter the outcome ledger and become the citation for the next proposal. Native platform write-back (audiences, workflows) comes next.
Results · From Day-0
What you see on Day-0:
No model writes the Day-0 audit. It's computed deterministically from your history: backfill, sweep, findings, open hypotheses. Day-0 is not an empty dashboard. Below is the real Day-0 for demo brand Lumora.
- Top-tier creator content runs $2.19 CPM, long tail averages $10.53;
- of 228 partnerships, 16 creators drive 63% of views at 31% of cost;
- concentrating spend is the first-priority action.
- 12 live creatives range $1.16 – $3.94 CPM;
- the 5 highest-CPM take 46% of budget for 21% of impressions;
- reallocating on measured CPM models a 33% drop in weighted CPM.
COMPARISON
One engine, instead of a patchwork of departments
What building it yourself actually looks like: every business line grows its own analytics, IT kicks off a governance and data-platform project and then wires in agents, and execution gathers everyone's input in meetings before anyone can call it. Every step costs money. Every step leaks time.
In-house reality: A data-platform project measured in quarters
In-house reality: Findings by luck, attribution by meeting
In-house reality: Insight to action measured in weeks
| Panoverse Intelligence Engine | In-house data team | |
|---|---|---|
| Data integration | One canonical schema on Day-0, full history backfilled, business definitions encoded into a system that keeps running | A data-platform project measured in quarters; every line builds its own tables, definitions never reconcile, handover loses the context |
| Overseas market data | Collection + labeling pipeline included (competitors / VOC / creator library), zero setup | No off-the-shelf source; collecting it yourself means owning crawlers, labeling and compliance. Most teams start and shelve it |
| Detection & investigation | Detectors sweep everything daily, the agent builds evidence chains, evidence-grounding gates the output | Analysts each watch their own dashboard; findings by luck, attribution by meeting |
| Insight to execution | Proposals ship with a measurement contract and go straight to the Panoverse ops team | Execution gathers input in meetings before calling it; insight to action measured in weeks |
| Result verification | Auto-backfilled when the window closes; the outcome ledger compounds month over month | Whoever executed writes the report, definitions differ by author, the organization learns nothing |
| Cost shape | Subscription, scales with use | Several headcount + platform project budget + the coordination cost nobody budgets for |
Onboarding & Security
Three steps to onboard.
Your data stays protected the whole way.
Your data is your most sensitive asset. The security here rests on architecture, not on promises.
STEP 1
Read-only OAuth
The platforms' own consent screens, about 10 minutes. Read scopes only: we never request write access.
STEP 2
History backfilled same day
Every available year lands in the canonical schema and the detectors sweep it immediately.
STEP 3
Day-0 audit + first actions
You get the audit and the first proposals with measurement contracts the same day.
Four hard constraints, written into the architecture
Scopes are read-only. Access can be revoked at the platform in one click; revoking deletes the corresponding data. The exit cost is zero.
Every brand gets its own data space and access tokens, invisible to every other brand. That's product architecture, not internal policy.
Encrypted in transit and at rest. We pull only the aggregates and content fields the analysis needs, never personal consumer data.
Your data is yours: never sold, never handed to third parties, never used across brands, never used to serve another client.