The problem it solves
Most customer experience teams are chasing shipments they can’t see. A delivery slips, the driver doesn’t mark it failed until the end of the run, the customer calls the contact centre, a CX agent pulls up four systems, apologises, promises a re-attempt — and by then the SLA is already broken. Multiply that by 10,000 shipments a day and the entire CX function becomes a reactive escalation desk.
The real pain isn’t ticket volume. It’s the silent failures: the misaddressed parcels that get marked “customer not available” without a call, the appointment windows missed because no one confirmed the slot, the NDR (non-delivery report) that should have been a simple reschedule but ended in an RTO. Every one of those is a CX moment that didn’t need to happen — and a margin leak that compounds into churn.
What it is
Clara is the Customer Experience Agent inside AgentFleet — the component that takes ownership of every post-order interaction on behalf of the operator. She isn’t a chatbot, and she isn’t a macro on a ticketing tool. Clara is an agentic layer that watches every shipment event in real time, predicts the CX outcome before the customer does, and takes action: calls the customer on WhatsApp or voice, confirms the delivery window, reschedules with the driver, closes the loop with the merchant, and only escalates to a human when confidence drops below a configurable threshold.
What’s new-age about Clara is that she’s event-driven and multilingual by default, sitting on top of Atlas — the Autonomous Control Tower so she sees the same signal stream a human dispatcher would. She speaks the language of shipments (bags, hubs, attempts, COD, POD) and the language of customers (ETA, promised slot, address). That fluency is why autonomous resolution rates move from baseline to 85%+ within a quarter of deployment.
Core capabilities
| Capability | What it does |
|---|---|
| Proactive ETA communication | Clara sends WhatsApp/SMS/voice updates on milestones — out for delivery, delayed, arriving in 30 min — tied to real telemetry from the driver app, not static estimates. |
| NDR rescue flows | When a shipment is at risk of non-delivery (driver flags address issue, customer not reachable, access code missing), Clara runs a pre-defined rescue cascade: WhatsApp confirmation → voice agent → re-attempt booking — before the driver leaves the area. |
| 17+ auto-detected incident types | Clara subscribes to Atlas’s incident stream — missed appointment, geofence breach, cold-chain excursion, long halt, reattempt failed, OTP mismatch, etc. — and triggers the right CX play for each. |
| Address intelligence handoff | Low-confidence addresses from AI Address Intelligence are routed to Clara’s voice/WhatsApp agent for real-time correction with the customer. |
| Appointment delivery confirmation | For B2B and pharma, Clara confirms the slot 24h and 2h before arrival, reschedules proactively on driver delay, and documents the confirmation in the audit trail. |
| Multilingual voice + chat | Native English, Hindi, Arabic, Spanish, Portuguese, French, Dutch, Bahasa — with tone controls (formal for B2B, casual for D2C). |
| Escalation routing | Confidence-tiered: high-confidence resolutions are auto-closed; medium routes to a CX associate with context pre-loaded; low bypasses Clara entirely. |
| WISMO deflection | “Where is my order?” queries resolved from live tracking data without a human touching the ticket — typical deflection of 30–40%. |
| COD & payment coordination | Clara confirms COD amount with the customer pre-arrival, offers UPI/QR alternatives, reduces cash handling at door. |
| Merchant & 3PL comms | For marketplace and 3PL contexts, Clara closes the loop back to the seller (order delayed, reason, next action) — not just the end customer. |
| Feedback loop to operations | Root causes — wrong slot, bad address, driver ETA drift — flow back into the operational system so planning and routing improve over time. |
| Audit-grade logging | Every Clara interaction is captured with timestamp, transcript, sentiment, outcome, and tied to the shipment record. |
How it works
Clara is built as a component of AgentFleet, consuming the same event bus that Atlas uses for incident detection. The mechanics pair a real-time event ingestion layer with a policy engine that decides — per customer, per SLA tier, per incident type — what action Clara should take next.
driver app, WMS, carrier APIs] --> B[Atlas event bus] B --> C[Incident detector
17+ types] C --> D[Clara policy engine
tier + SLA + customer rules] D --> E[WhatsApp agent] D --> F[Voice agent
multilingual] D --> G[Re-attempt booker] D --> H[CX associate queue
if confidence low] E & F & G --> I[Audit trail + CRM sync]
The diagram above is the component map. Below is the sequence for a typical NDR rescue — the single most common path Clara runs in production.
In practice, 60–70% of NDR paths resolve on the WhatsApp leg alone. A further 15–20% resolve on the voice agent fallback. The residual 10–15% — where the customer is genuinely unreachable, the address is fundamentally wrong, or the scenario requires human judgement — is where Clara escalates, with full context pre-loaded into the CX associate’s screen.
Why this is different from “AI chatbots”
Most vendors promising autonomous CX resolution are selling a better auto-responder bolted onto a ticket queue. Clara is the opposite of that. She does not live in the helpdesk; she lives in the event stream. She does not wait for a customer to complain; she detects the signal that will cause a complaint and pre-empts it. She does not answer from a knowledge base; she answers from the actual state of the actual shipment — the driver’s current location, the scan history, the POD, the accessorial flags.
The practical consequence is that Clara’s resolution rate is bounded by shipment-data coverage, not by training data. In regions where the driver app is fully deployed, where the address intelligence layer is active, and where the event bus carries the full signal, autonomous resolution climbs past 85%. In regions with partial coverage, it climbs as coverage climbs — a predictable curve rather than a generative black box.
Proven outcomes
| Customer type & scale | Outcome |
|---|---|
| Premium B2B express network, 49 cities, 3,500+ pincodes | 85%+ autonomous CX resolution (from 50% baseline); 90%+ FADR from ~75% |
| India’s largest pharmacy chain, 3,000+ riders | 17 incident types auto-detected; 30-minute early warning before SLA breaches; 3,000+ selfies auto-processed per shift |
| Global alco-bev leader operating across 70+ countries | 50% reduction in failed deliveries due to “customer not present”; enthusiastic secondary-customer feedback on comms |
| National postal operator powering 200+ countries | 90% first-attempt delivery rate; 25% reduction in manual CX workload |
| Leading Western European parcel operator, 50%+ market share | “WISMO” queries down 30–40%; 15–20% increase in delivery success |
Integrations
- Customer channels: WhatsApp Business API · voice (Twilio, Exotel, regional providers) · SMS · email · in-app notifications
- CRM & ticketing: Salesforce Service Cloud · Zendesk · Freshdesk · HubSpot
- Driver & depot stack: Shipsy driver app · ePOD · hub ops app · scan-sort integrations
- ERP & order systems: SAP · Oracle ERP · Magento · Shopify · marketplace APIs
- Tracking telemetry: Wialon · Wheelseye · native GPS · 240+ carrier APIs
- Analytics: BI no-code dashboards · event export to Snowflake, BigQuery, Redshift
Deployment
Most enterprises go live with Clara in 8–12 weeks, with a first region or vertical live in weeks 4–6.
Phase 1 — Discovery (weeks 1–2). Mapping CX incident types specific to your operation (we start from our 17-type library and add vertical-specific ones — cold-chain for pharma, appointment slots for B2B, COD-heavy flows for quick-commerce). Stakeholder workshop with CX, ops, and IT.
Phase 2 — Configuration (weeks 2–5). Policy engine setup: per-incident rules, confidence thresholds, escalation routing. Channel provisioning (WhatsApp BSP, voice provider). Language and tone training on your content. CRM integration.
Phase 3 — Pilot (weeks 4–8). Shadow mode for 1–2 weeks (Clara recommends, human executes) → assisted mode (Clara acts on high-confidence incidents only) → autonomous mode. Pilot in a single depot, region, or product line. Success criteria locked before go-live: autonomous resolution %, CSAT delta, NDR reduction, associate handling time.
Phase 4 — Scale (weeks 8–12+). Rollout across regions with a governance model — weekly tuning review of Clara’s decisions, monthly capability reviews, quarterly policy audits. The governance cadence is what keeps resolution rates climbing after go-live.
Success criteria we set before go-live
- Autonomous resolution rate (baseline vs. target)
- NDR reduction percentage, measured per region
- Average handle time reduction in the associate queue
- CSAT / NPS delta on Clara-handled vs. human-handled interactions
- Cost-per-contact reduction
- SLA-credit leakage recovered
These numbers are written into the deployment statement of work. If we miss them at 90 days, we jointly diagnose the cause — usually a data-coverage or policy-threshold issue — and remediate before the next review.
Security & compliance
- SOC 2 Type II · ISO 27001 · GDPR
- 21 CFR Part 11, GDP, GMP for pharma-relevant deployments
- PII tokenisation for customer phone numbers and addresses; transcript storage encrypted at rest
- Three-tier confidence scoring on every Clara decision (high auto-act, medium suggest, low escalate)
- Human-in-the-loop enforced for any action above configurable thresholds (refunds, credits, escalations)
- Full audit trail — every interaction tied to shipment, customer, agent version, and policy rule fired
- Regional data residency options — EU, India, ME, SEA, LATAM
Case study callouts
Premium Indian B2B express network · 49 cities, 3,500+ pincodes
Moved from 50% to 85%+ autonomous CX resolution by handing appointment confirmation, EDD-miss communication, and NDR rescue flows to Clara — freeing CX associates to handle the genuinely complex escalations. FADR went from ~75% to 90%+ in parallel.
India’s largest pharmacy chain · 3,000+ delivery riders, 2 shifts/day
17 automated incident types replaced 150+ field executives manually scanning for delays. Clara delivers a 30-minute early warning before SLA breach, contacting riders and customers ahead of the risk window. Rider attendance prediction hit 70% accuracy in pilot.
Global alco-bev leader · 70+ countries, 2M+ annual distribution trips
Failed deliveries due to “customer not present” cut by 50% after Clara took over pre-arrival confirmation for secondary customers. The team noted “enthusiastic feedback from secondary customers” on the new comms cadence.