AI · OmniBrain
OmniBrain AI: signals your teams can trust—and defend
Commerce AI fails when nobody can explain a decision. OmniBrain is built for operational and financial accountability: scores and recommendations grounded in your transactions, inventory, and channel economics, with human gates, policy layers, and audit trails.
At a glance
What enterprises validate before turning automation loose on customers or margin.
- Explainable scores—not black-box “AI said no”
- Human approval thresholds by margin, channel, and region
- Features grounded in live OMS, inventory, and settlement data
- Rollout toggles: shadow mode → limited channels → full production
- Hooks for finance & ops review before automation acts
- Designed to pair with Ads & Growth and OMS modules
Core capabilities
Three engines most brands activate first
Each engine reads the same canonical commerce graph Support Master maintains for OMS and growth—so you are not reconciling “AI stock” to “real stock.” Depth of each feature expands with your historical data and labeled outcomes.
RTO & delivery-risk intelligence
Score orders before costly shipment using pincode reliability, SKU return history, cohort patterns, and payment context. Risk tiers route work to manual review, alternative carriers, or partial prepayment flows—reducing leakage without punishing loyal geographies. Every score carries drivers operators can inspect (“high return rate on adjacent SKUs,” “new customer + high basket variance”) so CX and risk teams align on the same narrative during escalation.
Pricing & promo guardrails
Surfacing elasticity-aware nudges inside the guardrails you define: minimum margins after marketplace fees, promo blackout windows, and MAP or partner constraints. Recommendations land in merchandising and growth workspaces—not a separate spreadsheet—so “suggested discount” is always reconcilable to inventory depth and upcoming replenishment. Experiments can run as controlled holds with automatic rollback if contribution margin drifts beyond tolerance.
Demand forecasting you can replenish against
Baselines blend historical velocity, seasonality, and channel-specific lifts; scenario overlays help plan events and flash windows. Outputs tie to ATP and purchase recommendations with explicit lead-time assumptions—so buyers see not only “how many” but “by when” given supplier calendars. When forecasts miss, variance feeds back into the model lineage so the next cycle is less hand-wavy.
How it runs
From raw events to governed actions
OmniBrain is deliberately boring where it matters: idempotent writes, explicit approvals, and measurement hooks so you can answer “what did we automate last Tuesday?” without a forensic project.
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1
Ingest
Pull structured signals from orders, returns, stock movements, settlements, and campaign metadata already flowing through Support Master—no duplicate “AI data lake” unless you want one.
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2
Score & explain
Models produce ranked outputs with driver summaries suitable for ops review, not only dashboards charting a composite number.
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3
Policy gate
Business rules decide when a recommendation becomes an action: block, flag, auto-approve band, or route to a queue with SLA.
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4
Act & log
Accepted actions write back through the same APIs users trust—inventory reservations, price lists, or task creation—each with an audit record.
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Measure & tune
Lift, error rates, and downside incidents are tracked by cohort so leaders know when to widen automation or tighten thresholds.
Governance
Automation with adult supervision
Retailers and marketplaces get burned when “the model” moves price or inventory without recoverable controls. OmniBrain assumes regulators, finance reviewers, and angry customers exist—and designs for that reality.
Shadow & canary rollout
Compare model decisions to human baseline without customer impact; graduate channels only when error metrics meet your bar.
Role-based visibility
Merchandising sees pricing context; ops sees fulfillment risk; finance sees margin impact—same underlying score, different sanctioned surfaces.
Versioning & lineage
Know which model revision produced a decision during a dispute or audit window—critical for regulated or public-market readiness.
Manual override with reason codes
Teach the system when humans disagree, so future reviews inherit institutional judgment rather than one-off spreadsheet edits.
Readiness
What good adoption looks like on day zero
The best AI rollouts are boring projects: clean entities, honest KPIs, and operators who own queues. OmniBrain highlights gaps early instead of training on silent garbage.
Data hygiene
Clean SKU mapping, consistent return reasons, and settlement alignment materially improve signal quality—OmniBrain surfaces gaps before you automate.
Defined KPIs
Whether your north star is RTO %, contribution margin, or in-stock on A-list SKUs, thresholds should be explicit so automation is measurable.
Workflow readiness
Queues, SLAs, and empowered owners prevent “model output” from sitting unread during peak; AI amplifies process, it does not replace ownership.
Works with the rest of the OS
OmniBrain shines when Ads & growth and OMS already feed a shared operational picture. Connectivity to settlements and returns improves RTO models; inventory depth constrains pricing experiments; campaign calendars explain demand spikes that naive forecasts misread as trend.
Who it serves
One brain, different accountable surfaces
Chief commercial officer
Confidence that promos and pricing moves won’t silently erode margin across channels.
Operations director
Fewer surprise RTO waves and clearer triage when risk spikes by region or SKU family.
Demand planning
Forecasts tied to inventory and lead time—not slides that operations ignore on Monday.
Growth & performance marketing
Spend and creative tests bounded by operational truth so campaigns stop overselling thin inventory.
FAQ
Common questions from IT and commercial leads
Do we need a separate ML platform?
No for most deployments. OmniBrain runs on Support Master operational data and APIs. If you already operate a centralized ML stack, we can discuss export patterns—but many teams start fully inside the product.
Can we start in shadow mode only?
Yes. Compare predictions and recommended actions to human decisions; promote automation when precision and business impact match your policy.
How do you handle explainability?
Outputs emphasize ranked drivers and cohort context suitable for ops review. The goal is defensible decisions—not a single opaque score.
What about PII and data residency?
Design sessions cover field minimization, retention, and regional deployment options aligned to your security review—not a one-size “cloud only” assumption.
See OmniBrain on your riskiest SKUs and corridors
Bring a sample of orders, returns, and margin data—we’ll map how signals would flow and which guardrails we’d recommend first.