Last updated: 6 May 2026 — Model capabilities and India DPDP guidance evolve; involve legal for customer-facing AI claims and data flows.

Key takeaways

• Ground assistants on approved corpora; never let public chat invent SKU terms or policy exceptions.
• Log with retention limits; transcripts can become PII lakes.
• Moosi Web offers AI integration services alongside WordPress and custom stacks from Hyderabad.

Use cases that deserve AI

Good fits: internal knowledge search for support reps, guided product finders with citations to spec PDFs, and draft helpers for marketers that never auto-publish. Poor fits: instant legal interpretations, medical dosing, or unbounded refund promises—route those to humans with ticket IDs.

If you sell regulated goods, your compliance officer signs off escalation text—not only marketing.

Grounding and retrieval

Retrieval-augmented generation reduces but does not eliminate hallucinations. Version your corpus, invalidate embeddings when prices change, and show citations so users can verify. For Woo-led stacks, avoid exposing database keys to the browser; call models from server routes with rate limits.

Coordinate with performance so chat widgets do not tank INP.

PII and logging

Strip or hash identifiers before sending text to third-party model APIs unless contracts explicitly allow processing. Separate marketing pixels from support chat analytics in your privacy policy. Offer clear deletion paths where regulation requires.

Moosi Web defaults to least data and documented flows—see privacy policy for how we treat site enquiries.

Human hand-off

When confidence drops or keywords indicate frustration, surface a human channel with context: SKU viewed, cart value, prior tickets. Never trap users in bot loops. Train support to read AI-suggested replies as drafts, not orders.

For B2B dealer flows, read B2B ecommerce trends—AI should reduce phone load, not hide unavailable stock.

FAQ

FAQ schema should match visible answers; update both when product behaviour changes.