Solutions · Retail & CPG
Category answers in minutes,
not a week.
Category managers wait a week for a BI ticket. Promotions get graded by gut feel. Shrinkage only surfaces at the quarterly review. Quadrazene puts conversational analytics in the hands of the person who owns the decision, with forecasts that show their own uncertainty and loss signals that arrive before period close.
Today
What we see in the field.
BI tickets come back stale
A category manager needs to know why a SKU is trending down in a region. The analyst queue is a week out. By the time the answer lands, the promo window has closed.
Promotion effectiveness lives in a spreadsheet
Post-event analysis happens in Excel, weeks after the promotion ended. Markdowns that looked right at the time quietly drained margin; the next promo repeats the mistake.
Shrinkage surfaces at quarterly review
Store-level shrinkage patterns are in the data. But they only surface when someone builds the report. Between reviews, the pattern keeps running.
Demand forecasts don't show their work
The forecast number arrives without confidence bands or visible assumptions. No one knows how much to trust it for a buying decision, so they override it anyway.
Card data governance is a separate project
PCI-DSS scoping is treated as a compliance exercise, not a data-platform feature. The controls that restrict where card data flows don't live close to the analytics stack.
Cross-category analytics require IT coordination
A question that spans categories, regions, and time periods requires pulling from multiple systems and waiting on data-engineering time that was already allocated to something else.
Regulatory context
The frameworks you already operate against.
Quadrazene's classification cap and audit ledger are framework-shaped. Specific control mappings available under NDA.
PCI-DSS (card data in scope)
Classification cap pins Systems tagged with cardholder data to on-prem routing. Hosted LLM calls are denied before any prompt is built. Trust Events log every gate decision.
CCPA / CPRA
Consumer privacy rights, data minimization, and right to erasure. Scope-delete + Trust Events support the data-subject obligations.
GDPR (EU operations)
Lawful basis, purpose limitation, data-subject rights. Same scope-delete pattern, same immutable disclosure log.
CPFR / retailer data-sharing agreements
Supplier data (demand signals, forecast inputs) governed at the System level. Classification enforced before it flows to any model.
State breach-notification laws
Immutable Trust Events and hash-chained Records support the timeline obligations if a notification is required.
Internal SoD policies
Governance Engine evaluates SoD on purchasing, vendor master, and markdown-authorization workflows. Every finding writes an audit row.
How the four Engines compose
Insights leads. Advisory forecasts. Governance keeps card data in its lane.
Category managers, store operators, and finance teams ask questions in plain English and get cited, charted answers. Follow-up questions carry pre-validated SQL so the second click skips the LLM round-trip.
Example Skills: Category sell-through drilldown, regional comp analysis, promotion lift vs baseline, SKU velocity by store cluster, shrinkage by department.
Demand forecasts with confidence bands and visible assumptions. Promotion effectiveness scores before the event closes. Replenishment recommendations that show which signals drove them.
Example Skills: Demand forecast with confidence bands, promotion-lift prediction, markdown-timing advisor, supplier fill-rate score, seasonal-inventory signal.
PCI-DSS-scoped card data stays on-prem. SoD on purchasing and vendor-master workflows. Loss-prevention policies evaluated on every transaction; findings escalate into tracked Action Items.
Example Skills: Card-data classification enforcement, vendor-master SoD, markdown-authorization audit, loss-pattern flag, policy-change before→after diff.
Replenishment orders, promotion setup, vendor communication, and markdown execution—all with HITL on the consequential ones. The Action writes to the system of record and leaves an audit row.
Example Skills: Raise replenishment order, file vendor claim, trigger markdown, route promotion approval, send supplier scorecard.
See it on real surfaces
Two walkthroughs that show the retail shape.
Question → chart → follow-up → drill
A category manager asks why a SKU is trending down. Insights returns a cited chart, pre-validates a follow-up query (no second LLM round-trip), and lets them drill to store level—all in one session.
Start walkthrough →Forecast with confidence bands (demand-forecast shape)
The predict-slip walkthrough shows the advisory pattern. The same structure—model run, KPI tiles, narrative, driver waterfall, Action Item—reuses directly for demand forecasts and promotion-lift predictions.
Start walkthrough →Platform surfaces that matter most
Where the retail work actually happens.
Reactor · Insights
Category managers and analysts ask questions and get cited, chartable answers.
Advisory · forecasts
Demand forecasts, promotion scores, and replenishment recommendations with visible confidence.
Governance
PCI-DSS-shaped policies. Loss-prevention rules. Editable inline with before→after audit diffs.
Trust Layer
Classification cap denies card-data prompts routed to hosted models before the prompt is built.
Inbox · HITL
Markdown authorizations, vendor claims, and replenishment approvals route here.
Records
Hash-chained provenance for every Reaction. Re-verifiable NDJSON pack for audit.
Security & compliance posture
The questions IT and compliance will ask.
Card data never touches a hosted LLM
Systems tagged with cardholder data have the classification cap set to deny hosted-model calls. The denial fires before any prompt is built. Every denial is a Trust Event in the immutable log.
Customer-installable
Run on your VM, your Kubernetes, or air-gapped. POS, loyalty, and warehouse credentials never leave your boundary.
Content filters at every gate
Card-number patterns, PII, and custom regex patterns are filtered at the wire. Block, redact, or warn per pattern.
BYOK with FIPS-validated KMS
Per-tenant DEKs wrapped by a CMK in your KMS. Key-shred on offboarding.
Hash-chained audit
Every markdown authorization, every vendor-master change, every policy evaluation is one immutable row. The auditor receives NDJSON + signed anchors.
IdP and SSO
Your Entra, Okta, or AD FS is the source of truth. SCIM 2.0 provisioning lands category managers in the right tenant role within minutes.
What changes
Our gut feel for where the wins land.
Qualitative reads from the demos we've run. The shape of the change, not the size. We won't quote customer numbers we haven't measured.
Category drill-downs take minutes, not days
The category manager asks the question and gets a cited, charted answer in the same session. No ticket, no wait, no stale report.
Forecasts ship with confidence bands
The demand number arrives with its own uncertainty shown. Buyers know how much to trust it before they decide whether to override.
Loss-prevention signals surface in-line
Shrinkage patterns are evaluated on every transaction, not at the quarterly review. The signal arrives when corrective action is still cheap.
Promotion effectiveness is measurable before the window closes
In-flight promotion lift compares against the pre-event forecast. Markdowns that are leaking margin surface while there is still time to adjust.
Card data governance moves to the data platform
The classification cap enforces PCI-DSS scoping at the model layer, not in a compliance document. Auditors see the denial log, not a policy attestation.
Analysts shift from report building to decision support
Pre-validated follow-up SQL means the second question skips the LLM round-trip. Analyst time shifts toward interpretation, not query writing.
Where to start
Our recommended first phase for a retail pilot.
- 1.Connect one System: your data warehouse or a category analytics DB (read-only first). Tag its classification.
- 2.Run the Insights walkthrough on a real category question. Show a category manager the cited chart and a follow-up drill.
- 3.Bond the Advisory Engine. Run a demand forecast on one SKU cluster with confidence bands visible.
- 4.Tag any PCI-scoped System. Watch the Trust Layer deny a test prompt that tries to route it to a hosted model. Show the denial event to compliance.
- 5.Add one Governance policy (loss-pattern or markdown SoD). Watch the first finding land in the Inbox.
See it on your category data.
Bring a real category question or a real promotion. We'll run the Insights walkthrough against your sample and show the advisory pattern on the same data.