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    Wachat · Chat Labels

    Labels that actually drive the queue

    Color-coded labels with regex and AI-powered auto-tagging, per-label SLAs, routing rules and bulk filters. Stop asking agents to remember to tag — let the rules engine do it the moment the message lands, then report on every label as a first-class metric.

    • Regex + AI auto-tagging on inbound
    • Per-label SLAs and escalation rules
    • Bulk re-label and snooze by filter
    • Label-level dashboards and exports
    Feature signature
    SabNode . Wachat
    Chat Labels

    Color-coded labels, auto-tagging rules and per-label SLA timers.

    Live
    94%
    Of conversations auto-tagged within 2 seconds of arrival
    12
    Average number of active labels per mid-sized customer team
    0.2%
    Mis-tag rate after AI classifier reaches steady-state training
    The problem

    Tags rot when humans have to remember them

    Most teams set up labels in their first week of using a support tool — Refund, Bug, Feature Request, VIP, Billing, Sales — and within a month nobody is applying them consistently. The senior agent tags everything; the juniors forget; the analytics dashboard becomes useless because half the conversations are untagged or wrongly tagged. By month three, leadership is asking "how many refund requests did we get last month?" and the honest answer is "we don't really know — maybe 200, maybe 600".

    The second failure mode is labels with no consequence. A conversation gets tagged "VIP" but nothing changes — same queue, same SLA, same agent. Labels become a write-only descriptor instead of a control mechanism. Operators eventually stop trusting the system and rebuild routing logic in their head, defeating the entire point of having tags.

    Wachat's chat labels are designed so that tagging happens automatically and labels actively drive behaviour. A label is a first-class object with a colour, an icon, optional SLA overrides, optional routing rules, optional auto-replies, and analytics. The label "Refund" might have a 30-minute SLA, route to the finance team, trigger an internal note to the assignee, and roll up into a weekly executive report — all without an agent ever clicking "Add label".

    What it is

    Chat Labels, in depth.

    Wachat labels are workspace-scoped objects with a name, colour (12-step palette), icon, description and parent (for hierarchical labels like "Refund > Damaged" and "Refund > Wrong size"). Any conversation can have any number of labels, applied manually by an agent, automatically by a rule, or by the AI tagger. The label panel in the inbox shows active labels as removable chips with their colours; the queue filter supports any combination of include/exclude labels for precision triage.

    Auto-tagging runs on every inbound message. Rules can use exact keyword match, regex (full PCRE), customer attribute conditions (e.g. ltv > 50000), order conditions (e.g. order total > 10k), channel filters, or AI-based intent classification. A typical setup might be: regex /refund|return|money back/i tags "Refund"; regex /not working|broken|error/i tags "Bug Report"; AI intent "complaint" tags "Escalation"; CRM lookup "ltv > 50000" tags "VIP". Multiple rules can apply to one message; the conversation accumulates the union of all matched labels.

    Each label can carry its own behaviour. A "VIP" label can override the default SLA from 30 minutes to 10 minutes, force assignment to a senior agent pool, post a Slack alert and pin the conversation to the top of every queue. A "Refund" label can route to the finance team, attach the company's refund policy as an internal note, and require a senior agent's approval before resolution. The behaviour is configured per label in the admin UI — no flow needed for most cases.

    Analytics treat every label as a measurable dimension. The labels dashboard shows volume, average response time, average resolution time, CSAT and revenue impact per label per week. Export by label to CSV for board reports, slice by label inside the analytics module, or use labels as audience criteria in Broadcasts ("send to all contacts with the Bug-Report label closed in the last 30 days"). Labels become the spine of the company's operational view of its customer conversations.

    Capabilities

    Everything you get with Chat Labels.

    7 capabilities
    01

    Color-coded label library

    Create unlimited labels with name, colour, icon, parent and description. The 12-step palette is colourblind-safe, and labels render as chips in the queue, the conversation header and inside reports for instant visual scan.

    02

    Regex auto-tagging

    PCRE-flavoured regex rules run on inbound message text. Case-insensitive, multi-line and lookahead supported. Test rules against historical conversations in the rule editor before publishing — see how many would have matched in the last 30 days.

    03

    AI intent classification

    A trained classifier maps inbound messages to intents (complaint, refund, sales-enquiry, technical-issue, praise, spam) with confidence scores. High-confidence matches tag automatically; low-confidence ones queue for human review with the suggested label.

    04

    Per-label SLAs

    Override the team or channel SLA when a label is applied. VIP gets 10 minutes; Refund gets 30 minutes; Bug Report gets 4 hours. The most aggressive applicable SLA wins, so a VIP refund still triggers the 10-minute clock.

    05

    Label-driven routing

    When a label is applied, optionally re-assign the conversation to a specific team or agent. A "Refund" label routes to finance; "Billing" routes to accounting; "Bug Report" routes to the on-call engineer. Routing fires once per label application to avoid loops.

    06

    Bulk operations

    Filter the queue by any label combination and apply bulk actions: re-assign, re-label, snooze, send a broadcast template to all linked contacts, or export. Useful for backlog cleanup and for running a campaign targeted at a specific customer state.

    07

    Label analytics

    Per-label dashboards: volume, response time, resolution time, CSAT, revenue and refund amount linked. Compare labels week over week, segment by channel, or drill into individual conversations from any cell of the dashboard.

    Use cases

    Built for the way teams actually work.

    E-commerceCase 01

    D2C refund taxonomy

    A skincare brand has parent label "Refund" with children "Damaged", "Wrong Size", "Allergic Reaction", "Changed Mind", "Late Delivery". Auto-tagging routes "Allergic Reaction" to a senior agent with a 15-minute SLA and a templated apology. Monthly board report shows refund volume by reason without anyone manually classifying anything.

    SaaSCase 02

    SaaS bug triage

    A B2B SaaS auto-tags "Bug Report" on messages matching error code regex like /\bE\d{4}\b/, then routes to the engineering on-call channel in Slack with the customer's plan tier and account ID. Bugs from enterprise accounts get a "P0" sub-label that triggers PagerDuty; everyone else gets a 24-hour SLA.

    HealthcareCase 03

    Healthcare emergency flagging

    A telemedicine platform auto-tags "Emergency" when messages match keywords like "chest pain", "bleeding", "unconscious" with high AI-classifier confidence. The label triggers an immediate call to the on-call doctor and a templated message to the patient with the nearest hospital from their location.

    Financial ServicesCase 04

    Financial services KYC

    A lending app auto-tags messages with attached documents as "KYC-Submitted" and routes to the KYC team. AI classifies whether the document is PAN, Aadhaar or bank statement, then sub-labels accordingly. The dashboard tracks KYC turnaround time and rejection reasons per document type.

    EducationCase 05

    Edtech VIP cohort

    An online MBA programme tags students from corporate-sponsored cohorts as "VIP-Corporate" via CRM attribute lookup on inbound. These get a 10-minute SLA, priority assignment to the senior counsellor, and quarterly satisfaction surveys auto-triggered by the label state.

    How it works

    From signup to first send in minutes.

    Chat Labels is included on every SabNode workspace. No separate billing, no extra setup, flip it on from your workspace settings.

    1. 01

      Design your taxonomy

      Plan 10-20 starter labels organised hierarchically. Wachat's setup wizard suggests common patterns by industry (e-commerce, SaaS, healthcare) which most teams adopt with minor tweaks.

    2. 02

      Build auto-tagging rules

      Open the rules editor. Write keyword, regex, AI-intent or attribute conditions. Test each rule against the last 30 days of conversations — Wachat shows how many would have matched and which.

    3. 03

      Wire label behaviours

      Per label, configure overrides: SLA, routing, internal notes, Slack alerts, auto-replies. A label without a behaviour is just a descriptor; a label with behaviours is a control surface.

    4. 04

      Backfill historical data

      Run auto-tagging rules across the entire conversation history (or last 90 days) in a background job. This produces meaningful analytics from day one instead of waiting weeks for forward-looking data.

    5. 05

      Iterate weekly

      Review the label analytics dashboard. Add new labels for emerging patterns, retire labels with under 10 hits per month, and tune rules with low precision. Treat the taxonomy as a living artefact.

    Plays well with

    Works with the tools you already ship on.

    Connect directly with your existing stack or leverage the Platform Core tools to extend capabilities natively.

    WhatsApp Cloud APIInstagram Graph APIGmail / Microsoft 365SlackPagerDutyGoogle SheetsHubSpot CRMZapier

    Platform Core Tools

    Enhance this feature with deep integrations into our core infrastructure. Connect via API, utilize webhooks, or embed directly using our SDKs.

    • Unified Dashboard Apps

      Manage all settings seamlessly within the core UI.

    • Developer APIs and Webhooks

      Extend functionality with custom automated workflows.

    Frequently asked

    Questions about Chat Labels.

    Can't find what you're looking for? Talk to our team.

    How many labels should a team have?
    Most healthy taxonomies sit between 15 and 40 active labels. Below 10 and you lose granularity; above 60 and the queue becomes visually overwhelming and analytics get noisy. Use hierarchical parent/child labels to expand without bloating — "Refund" with five children gives you both summary and detail views without cluttering the chip space in the conversation header.
    Can a conversation have multiple labels?
    Yes. A conversation can carry any number of labels simultaneously — for example, "VIP", "Refund", "Damaged" and "Hindi" all on the same thread. Each label's behaviour (SLA, routing) applies, with the most aggressive SLA winning if there is a conflict. The queue filter supports any boolean combination of include/exclude labels.
    Do regex rules support Unicode and Indic scripts?
    Yes. The regex engine is full UTF-8 and PCRE-compatible, so you can match Hindi, Tamil, Bengali and other Indic scripts directly. The rule tester previews matches in-script. Most teams build both English and regional-language rules for the same intent — for example, two rules tagging "Refund" matching English and Hindi keywords respectively.
    How accurate is the AI intent classifier?
    After 500-1000 labelled conversations in training, the classifier typically reaches 88-94% precision for common intents like refund, complaint, sales-enquiry. Lower-volume intents need more training data. The classifier outputs a confidence score, so you can set a high threshold (e.g. 0.85) for auto-apply and queue lower-confidence matches for human review with the suggestion attached.
    Can I use labels in Broadcasts and Flow Builder?
    Yes. Labels are first-class audience criteria in Broadcasts ("send to all contacts with an open Bug-Report label") and first-class triggers in Flow Builder ("when label 'VIP' is added, run the VIP onboarding sequence"). This makes labels the glue that connects inbox state to outbound communication and automation.
    What happens when a label is deleted?
    Wachat soft-deletes labels by default: the label disappears from the picker but stays attached to historical conversations and visible in analytics. You can hard-delete after a 30-day cooling period if needed. This prevents accidental destruction of historical reporting data when someone mistakenly deletes a long-used label.
    Can I export label analytics for board reports?
    Yes. Every label-level metric — volume, response time, resolution time, CSAT, linked revenue — exports to CSV, Google Sheets or directly to the SabNode Analytics module for dashboarding. Most teams set up a scheduled monthly export to a Google Sheet that the COO reviews in the first week of every month.
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    Wachat · Chat Labels

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