Deflect the queue without deflecting the customer.

• 01

  Order status and delivery exceptions

  Reads the live carrier status, explains what actually happened, and offers the right remedy — reship, refund, or wait — according to your policy rather than the model's judgement.

• 02

  Returns and exchanges

  Checks eligibility against your rules, issues the label, and books the exchange against real stock, so the customer isn't promised a size that sold out yesterday.

• 03

  Pre-purchase product questions

  Answers fit, compatibility and materials from the catalogue and your reviews, with the source shown — the questions that decide whether a cart converts.

• 01

  The catalogue is the ground truth

  Answers about price, stock and fit come from your product data at query time, never from what the model remembers about a product from training. Out-of-stock claims are a refund conversation later.

• 02

  Peak traffic is a design input

  Sale-day load is where naive AI support falls over and where costs spike hardest. We put rate limits, caching and per-conversation cost ceilings in before launch, not after the first invoice.

• 03

  Returns policy is policy, not vibes

  Eligibility is evaluated by your rules engine, with the agent explaining the outcome. A model improvising goodwill on returns is an unbudgeted liability.

1. 1

   Use-case scoping

   Pick the workflow with the strongest ROI. Define success metrics, guardrails, and integration points.

2. 2

   Tools & memory

   Connect the agent to your real systems — CRM, helpdesk, data warehouse, internal APIs — with auth and audit trails.

3. 3

   Eval suite

   Build a versioned eval set from real conversations so we can prove the agent improves before each release.

4. 4

   Pilot with humans

   Shadow mode → suggested replies → autonomous on low-risk tasks. Humans stay in the loop where it matters.

5. 5

   Production scale

   Monitoring, fallbacks, cost guards, and observability. Weekly improvement cycles based on production data.

• Shopify & WooCommerce
• Gorgias, Zendesk & Intercom
• Klaviyo
• Stripe & Razorpay
• Shiprocket, Delhivery & carrier APIs
• Algolia / Typesense
• GA4 & server-side tagging
• Meta & Google Ads APIs

FAQ

AI customer service agents for e-commerce: your questions.

• How does it handle sale-day spikes?

  Queueing and graceful degradation are part of the build: under load it prioritises order-status intents, which is the bulk of spike traffic, and defers anything expensive. Capacity is tested against your last peak, not a synthetic benchmark.

• Can it handle WhatsApp and Instagram DMs?

  Yes — same agent, same tools, same escalation rules across channels. Keeping one behaviour set is the point; per-channel bots drift apart within a quarter.

• How is this different from a chatbot?

  Chatbots reply. Agents act. Our agents call APIs, update records, send emails, schedule meetings — with audit logs and rollback. They take actions in your systems, not just respond in a chat window.

• Which LLMs do you use?

  We pick the right model for the task — Claude Sonnet/Opus for reasoning, GPT-4o for tool use, Llama / open-weights when data residency demands it. We're not locked into any vendor and we'll tell you what's cheapest at your volume.

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