AI Intelligence and Assistance

Context-aware AI for ecommerce customer support.

SignalBX combines each eligible inbound message with bounded recent customer and agent history plus linked order, shipment, payment, and refund records for context-aware AI intelligence. It produces structured intent, priority, escalation risk, routing, and action items; authorized users can then separately draft or translate an editable reply.

Start your workspace Explore the signals

Intent, sentiment, and language

Classify what the customer wants, the tone behind the message, and the language used.

Priority and escalation

Separate routine work from deadlines, churn, chargeback, and legal risk.

Conversation continuity

Use recent customer and agent messages to recognize follow-ups, repeated requests, and unresolved issues.

Stage and routing

Place the current message in the conversation and guide it to the right department.

Summary and actions

Condense the request and surface concrete operational next steps.

Entities and demand signals

Extract referenced details, flag PII, and capture buying signals and objections.

Commerce-aware analysis

Interpret the message against linked order, shipment, payment, and refund records when available.

Drafting and translation

Prepare an editable reply or translate support text while preserving meaning and operational details.

One structured signal record

Turn a message and its context into an operational brief.

The ecommerce schema gives every eligible message the same predictable shape while bounded conversation history and linked commerce records help the model interpret what the customer means now.

Message intelligence signals Illustrative output · ecommerce-v2 · context aware

“My replacement still hasn’t arrived and I need it before Friday. Please help.”

IntentComplaintIssue with a replacement

PriorityHighDelivery deadline stated

Escalation riskHighUnresolved delivery issue

RouteFulfillmentDelivery ownership

Summary

The customer is waiting for a replacement and needs delivery before Friday.

SentimentNegativeCustomer is worried and frustrated

Conversation stageFollow-upRefers to an unresolved issue

Response styleEmpatheticAcknowledge urgency before resolving

Keywords

replacementhasn’t arrivedbefore Friday

Action items

Check the replacement shipment status
Confirm whether delivery before Friday is possible
Share the latest delivery update with the customer

Queue-ready triage

See urgency, ownership, and context before opening the thread.

Intelligence uses the current message, prior customer and agent turns, and available commerce records to prepare a structured view for prioritization and routing. Assistance remains a separate, explicitly invoked step that returns editable text to an authorized user.

Recognize follow-ups, repeated contacts, and unresolved customer issues
Route sales, support, billing, fulfillment, returns, or legal work
Ground next steps in available order, shipment, payment, and refund facts

Context-aware message triageCurrent message + conversation + commerce

Inbound customerEmail · just received

High priority

Customer: I’m following up on the order we discussed yesterday.

Agent: I’m checking the payment and delivery status for you.

I was charged twice for the same order and still don’t know when it will arrive.

Structured triageAI intelligence + commerce records

Intent: Complaint
Priority: High
Escalation: High
Route: Billing
Payment context: Two captured records
Shipment context: No recent tracking event

Assistance with a human checkpoint

Draft from the conversation. Translate without changing the meaning.

After context-aware AI analysis has clarified the issue, authorized users can separately request an editable reply using recent conversation history, an optional instruction, tone, and language—or translate supplied support text into a chosen target language.

AI-assisted reply workspace Illustrative output · review required

Draft replyRecent conversation context

My replacement still hasn’t arrived and I need an update before Friday.

I’m sorry your replacement has not arrived. I understand you need an update before Friday.

ToneEmpathetic

LanguageCustomer’s language

Translate replyTarget · Spanish

Source text

I’m sorry your order has not arrived. I understand you need an update before Friday.

Translated text

Lamento que tu pedido no haya llegado. Entiendo que necesitas una actualización antes del viernes.

Meaning and tone preserved
Names, URLs, identifiers, and numbers preserved
No explanations or new content added

ContextBounded recent messages

TonesProfessional · friendly · empathetic · concise

AccessExplicit AI-assist permission

AuditContent-free usage metadata and hashes

Asynchronous by design

Enrich the message after ingestion, not in the webhook path.

SignalBX claims eligible inbound messages in leased batches, assembles bounded context, selects the tenant’s domain prompt, persists the structured result, and then makes it available to conversation activity and analytics.

Eligible inputInbound text, caption, or transcription

Claimed

Reference contextRecent conversation + linked commerce records

Bounded

Structured analysisSchema-versioned JSON signals

Validated as JSON

Operational resultMessage, activity, and analytics context

Persisted

Predictable by design

One signal shape. Every eligible message.

The ecommerce prompt requires a complete JSON result with stable top-level fields, explicit confidence values, no nulls, and schema and prompt versions.

intent priority escalationRisk routing actionItems

JSONBStructured AI signal record · selected fields

{
  "intent": {
    "value": "complaint",
    "confidence": 0.94
  },
  "priority": {
    "level": "high",
    "reason": "Possible duplicate charge",
    "confidence": 0.93
  },
  "escalationRisk": {
    "level": "high",
    "indicators": ["duplicate charge"],
    "confidence": 0.90
  },
  "routing": {
    "department": "billing",
    "confidence": 0.96
  },
  "actionItems": [
    "review duplicate charge",
    "share delivery update"
  ],
  "promptVersion": "ecommerce-v2"
}

From message to operating picture

See individual signals and the patterns across the queue.

SignalBX aggregates allow-listed AI intelligence fields into labeled panels, including universal support signals, conversation-aware triage, and ecommerce-specific demand indicators.

Universal signals

Priority, escalation risk, conversation stage, routing, and response style.

Every domain

Message context

Top keywords, action items, entity types, and PII indicators.

Operational detail

Ecommerce demand

Buying signals and objections found across inbound conversations.

Domain specific

Intent and sentiment

Distribution views that show what customers need and how they feel.

Queue overview

Messages become actionable

The signals behind everyday ecommerce conversations.

Purchase intent

“Is this available in blue?”

Identify product interest, stock or variant questions, and readiness to buy.

Sales · friendly response

Order status

“Where is my delivery?”

Recognize shipment and tracking questions and direct them to fulfillment.

Fulfillment · concise response

Complaint

“The item arrived damaged.”

Raise priority, capture the issue, and suggest an apologetic or empathetic response.

Support · escalation awareness

Return or refund

“I want my money back.”

Detect refund intent, retention risk, and the operational next step.

Returns · action item

Payment issue

“Why was I charged twice?”

Surface duplicate-charge risk and route the conversation to billing.

Billing · high escalation risk

Negotiation

“Can you offer a bulk discount?”

Capture the buying signal and price objection without losing the sales context.

Sales · buying signal + objection

Intelligence and assistance you can operate on

Grounded, permissioned, and accountable.

Intelligence stays tied to the inbound message and its bounded reference context, while drafting and translation are separate requests with explicit access, quotas, validation, and content-free audit records.

Untrusted input boundaries: The domain prompt treats message text as customer data and instructs the model never to follow commands inside it.
Bounded conversation context: Recent customer and agent messages are supplied in chronological order to identify continuity, repeated requests, unresolved issues, and escalation.
Bounded commerce context: Linked customer, order, shipment, payment, and refund records use allow-listed fields, configurable lookback and record limits, and a total character budget.
Tenant controls: Only active tenants with AI analysis enabled are eligible, and each tenant selects its analysis domain.
Leased batch claims: Database claims, expiry, retry timing, and attempt limits support safe asynchronous processing across workers.
Sensitive-data guidance: PII categories are flagged, while summaries and action items are instructed not to repeat sensitive values.
Versioned persistence: The raw enrichment object is stored with schema and prompt versions for downstream message and analytics views.
Grounded analysis: The model is told that context may be incomplete or stale, must not invent missing commerce facts, and should keep current-message extraction separate from reference context.
Grounded drafts: Drafting uses bounded recent conversation text and instructs the model not to invent facts, promises, actions, or policies.
Controlled assistance: AI-assist requires permission, enforces principal and tenant request limits, validates bounded output, and stores hashes and usage metadata instead of generated content.

AI intelligence and assistance questions

What teams need to know before enabling analysis and generation.

Which messages are eligible for AI intelligence?

When AI intelligence processing is configured and enabled, SignalBX processes non-deleted inbound messages with usable text, caption, or transcription for active tenants that have AI analysis enabled. Outbound messages are not analyzed by this background process.

What does ecommerce AI intelligence extract?

It includes intent, sentiment, language, keywords, summary, priority, escalation risk, conversation stage, action items, entities, routing, response style, PII indicators, buying signals, and objections.

What context does ecommerce customer support AI use?

For each eligible inbound message, SignalBX can supply bounded prior customer and agent messages plus linked customer, order, shipment, payment, and refund records. The current message remains the primary input, and context is treated as read-only reference data that may be incomplete or stale.

How do intelligence and assistance work together?

AI intelligence stores structured signals for operational use. Drafting and translation are separate on-demand operations that return editable text to the authorized caller; neither operation sends a message automatically.

How is the analysis ecommerce-aware?

The tenant’s AI analysis domain selects the ecommerce intelligence rules for intent, risk, routing, conversation stage, buying signals, and objections across stores, marketplaces, D2C brands, retail, delivery, subscriptions, bookings, and digital products. When a contact can be resolved safely, linked commerce records help interpret order-status, delivery, payment, and refund questions.

How does SignalBX handle sensitive information?

The ecommerce schema flags whether PII is present and records categories such as personal, financial, medical, credential, and legal data. Its instructions prohibit sensitive values from being repeated in summaries or action items.

Can teams report on AI intelligence signals?

Yes. Tenant-scoped analytics aggregate intent, sentiment, universal signal panels, contextual fields, and ecommerce buying signals and objections through allow-listed JSON paths.

What controls are available when drafting a reply?

A draft request can include an optional instruction, one of the supported professional, friendly, empathetic, or concise tones, and a requested language. SignalBX automatically supplies bounded recent text and caption context from the conversation.

What does translation preserve?

The translation operation is instructed to preserve meaning, tone, names, URLs, identifiers, numbers, and line breaks while adding no explanations or new content.

Does the AI-assist audit retain prompts or generated replies?

No. Audit rows retain operational metadata such as operation, actor, model, character counts, context count, tone, language, status, and input or output hashes—not the prompt, conversation text, draft, or translation.

Every message, understood in context

Bring the conversation and commerce story together.

Turn inbound ecommerce conversations into structured urgency, ownership, and action using recent customer and agent messages plus available order, shipment, payment, and refund context—then help the team draft or translate an editable response.

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