The world you operate in (and why it’s so hard)
If the stack looks like Meta Business Suite (Ads/Commerce/Events), Google (Ads, Merchant Center, Analytics), a WMS, delivery partners, payment gateways, and one or more storefronts (Shopify, custom web/app, agency-built), then “simple questions” are rarely simple.
- Marketing sees spend and conversions in Meta and Google, but “purchase” may be defined differently across pixels/SDKs and channels.
- Sales and business teams see orders in the storefront, but returns/cancellations live elsewhere, and payment settlement timing doesn’t match order timing.
- Ops sees inventory in WMS, but availability on the storefront depends on sync latency, backorders, substitutions, and carrier performance.
- Leadership asks for one story, but the data is naturally split across systems built for different purposes.
This is the daily friction: not a lack of data, but a lack of a single, trusted way to compute “the truth” across systems—quickly.
The problems that keep repeating (even in mature companies)
The “report request telephone game”
A VP asks: “Did performance drop because of Meta or because fulfillment got slower?”
By the time it travels down the chain, the request becomes: “Pull Meta ROAS, GA revenue, WMS stockouts, and delivery SLA for last week.”
Two days later, the answer travels back up—distorted:
- the dates are different (UTC vs local time vs platform time),
- “revenue” means three different things,
- returns were included in one report and excluded in another,
- and everyone debates numbers instead of deciding actions.
The “decision archaeology” trap
Someone inevitably asks:
- “When did we decide to exclude branded search from CAC?”
- “Is this ROAS based on 7-day click or 1-day view?”
- “Are we using gross revenue or net of refunds and cancellations?”
- “Which report did we use for the board deck last quarter?”
The issue isn’t intelligence—it’s missing lineage. Without proof of how a number was produced, the organization relies on memory, screenshots, and spreadsheets.
The “multi-platform mismatch” that kills trust
Common mismatches your teams recognize instantly:
- Meta shows purchases; Google Analytics shows fewer; Shopify shows orders; payment provider shows settlements; finance shows net revenue.
- Merchant Center disapprovals affect spend efficiency, but the root cause lives in product feeds and policy flags, not ad dashboards.
- Inventory “in stock” doesn’t mean “deliverable,” because carrier capacity, pin-code coverage, and SLA commitments exist outside the storefront.
- A campaign looks great on-platform, but cancellations spike due to delayed delivery or payment failures—so performance reviews become arguments.
The two bad choices: slow BI or untrustworthy AI
- Traditional BI is accurate but slow to adapt: dashboards answer yesterday’s questions, and new slices require analysts and tickets.
- Generic AI is fast but hard to trust: it may produce confident narratives without reconciling to the underlying systems, and repeating the same question can yield different answers.
Most teams end up with a third option: spreadsheets, screenshots, and Slack threads—fast enough to move, but fragile enough to break later.
Orion: the platform that turns questions into defensible outcomes
Orion is built to be the missing layer between business questions and your real data systems.
Instead of asking AI to “guess” answers, Orion asks AI to produce a precise, executable plan (code/query) and then runs that plan against your actual sources—securely, with auditability. The output is not just text; it includes structured data and proof of how it was computed.
What “proof” looks like in practice
Every Orion response can include:
- A plain-language summary (for business readers).
- A supporting table or dataset (so the answer is inspectable).
- The logic used (query/script) so teams can validate the method.
- A trace record (what ran, when, against which sources), so decisions don’t get lost in Slack.
This is how numbers become portable across the organization without being distorted.
How Orion fits your ecosystem (Meta + Google + Storefront + Ops)
Orion is designed for the real-world commerce stack where insights require stitching across platforms:
- Marketing systems: Meta Ads/Events/Commerce, Google Ads, Merchant Center, Analytics.
- Commerce systems: Shopify or custom storefront/app order systems.
- Operations systems: WMS, inventory sync services, delivery/carrier partners, returns systems.
- Payments: payment gateways, settlements, chargebacks/refunds.
Orion doesn’t replace these systems. It becomes the layer that can answer questions across them with consistent definitions and repeatable logic.
Two examples your teams will recognize
Example A: “Why did ROAS drop this week?”
In most companies, this becomes three parallel investigations:
- Marketing checks Meta and Google.
- Ops checks fulfillment and delivery delays.
- Finance flags refunds/cancellations.
Orion makes this a single, structured question that produces a defensible breakdown:
- It clarifies the measurement definition (attribution window, conversion event, net vs gross revenue).
- It pulls the relevant signals across systems (spend, clicks, purchases, cancellations, refund rate, delivery SLA breaches, stockouts).
- It returns an output you can act on: a channel-by-channel view and operational drivers that explain the business outcome.
Instead of “it might be X,” you get: “Here is the computed breakdown, and here is what changed.”
Example B: “Top products by region—and can we fulfill the demand?”
This is the classic cross-functional question:
- Marketing wants to push winners.
- Sales wants to plan offers.
- Ops needs to confirm inventory and deliverability.
Orion turns it into a results-ready output:
- Top products by region (from storefront/orders).
- Availability and stock risk (from WMS).
- Delivery feasibility/SLA signals (from carriers/partners).
- Payment failure/cancellation signals (from payment partners and order lifecycle).
The business value is immediate: you stop promoting products that can’t be delivered reliably, and you stop relying on manual reconciliation to find out.
Why Orion works when other AI tools don’t
Orion doesn’t “make up” numbers
The AI is used to translate intent into executable logic; the truth comes from running that logic on your systems. That’s why the same question produces the same answer when the data hasn’t changed.
Orion reduces misunderstandings before they spread
When a question is ambiguous (“revenue,” “this month,” “region,” “conversion”), Orion asks a short follow-up rather than silently choosing assumptions. That prevents the most common meeting failure: debating definitions after the report is already circulated.
Orion captures decisions so they don’t get lost
A huge part of operational speed is knowing what was decided and why. Orion’s auditability and traceability make it possible to attach:
- which definition was used,
- which systems contributed,
- what time range and filters applied,
- so teams don’t have to rediscover context every week.
The diagrams (how to interpret them as a buyer)
System architecture: why this is a platform, not a chatbot
The architecture diagram shows that Orion is not one monolithic assistant—it’s a managed system: context + routing + governed agents + secure execution + integrations. This is what enables you to add more use cases (marketing, ops, finance) without losing control.
Assurance pipeline: why it stays reliable as you scale
The assurance diagram shows a test pipeline that checks structure, governance, integration, failure-handling, evaluation, and health—so each new “agent” behaves like a deployable product component, not a prompt experiment.
Future improvements (positioned for buyers, not engineers)
To make Orion even more valuable for marketing/sales/business teams in multi-platform commerce environments, the roadmap focuses on eliminating remaining friction:
- Decision memory: Store “metric decisions” (e.g., attribution window, net revenue definition) as searchable, approvable objects—so teams stop re-litigating old choices.
- Cross-platform reconciliation packs: One-click bundles that show Meta/Google → Storefront → Payments → Returns reconciliation for a period, with clear reasons for gaps.
- Feed + policy diagnostics: First-class troubleshooting for Merchant Center disapprovals and feed health, linked directly to revenue impact and inventory reality.
- Fulfillment-aware marketing insights: Automatically warn when performance issues are driven by stockouts, delivery SLA breaches, or payment failures—before budget is shifted incorrectly.
- Role-based views: The same answer rendered differently for leaders (summary + drivers), managers (breakdowns + actions), and analysts (logic + evidence) with access controls.
- Budget guardrails: Department-level cost controls and “safe mode” for exploratory queries to keep spend predictable while adoption grows.
- Action-ready outputs: Push results into the tools teams already use—Slack, email, sheets, tasks—without losing traceability and definitions.