WATI vs Converiqo AI: Beyond WhatsApp Engagement
WATI is a WhatsApp-first customer engagement platform, and Astra is its separate AI-agent product for revenue teams. Converiqo AI (by Mobiloitte) is an enterprise AI workflow automation platform: one governed workspace where the assistant answers from reviewer-approved knowledge and conversations continue into tickets, approvals, field visits and purchase orders.
How do WATI + Astra and Converiqo AI compare at a glance?
| Capability | WATI + Astra | Converiqo AI (by Mobiloitte) |
|---|---|---|
| Product structure | WATI (inbox, campaigns, automation) plus Astra (AI agents), with a separate Astra sign-up | One unified platform and one governed workspace |
| Primary focus | "Conversation-led customer engagement" for marketing, sales and support | AI workflow automation across sales, service, HR, procurement and field teams |
| Where the AI agent runs | WhatsApp, web widget and voice; Astra on Instagram and Messenger not publicly documented | Web, WhatsApp, email, SMS, Instagram, Messenger, Telegram, LinkedIn and voice |
| How the AI learns | Website links, documents and Q&A pairs, retrained when content changes | Approved sources, FAQs and business data with vector grounding |
| Knowledge approval and citations | Not publicly documented | Second-person approval workflow; answers cite the verified source |
| Testing | Sandbox preview and automated evaluations scoring accuracy and latency | Scenario tests, RAG lab and automated QA verdicts |
| Versioned releases and rollback | Not publicly documented | Release candidates with a traceable release reference and rollback |
| Actions after the reply | Create or update CRM contacts, book meetings, post to Slack, call REST APIs | Tasks, approvals, tickets with service targets, field jobs, purchase orders, calendar bookings |
| Employee, procurement and field operations | Not publicly documented | Pre-built employee self-service, procurement and field force modules |
| Voice | Astra Voice 2.0 (30+ languages, voice cloning) and WhatsApp Business Calling | Pre-launch test console, launch readiness, live monitor and call dispositions |
| Roles and sign-in | 10 predefined roles and 2FA; custom roles and SSO not publicly documented | Custom least-privilege roles, OIDC single sign-on, MFA |
| Certifications (as worded) | Homepage: "GDPR and ISO 27001"; Wati AI page: "SOC 2 Type II certified" | Cloud infrastructure SOC 2 Type II & ISO 27001; platform controls aligned |
Choose Converiqo AI if…
Engineered for organizations where conversations must finish back-office work, uphold compliance, and reach beyond WhatsApp
- Conversations Must Turn Into Owned Work: Replies must turn into owned work: an SLA-bound support ticket, an approval hierarchy, a technician or phlebotomist visit, or a purchase order.
- Reviewer-Approved Knowledge & Citations: Compliance wants to sign off on what the AI says before customers see it, and inspect the exact source citation behind every answer.
- Channels Beyond WhatsApp: You serve customers on email, Telegram, LinkedIn, SMS, and web chat as well as WhatsApp, and require one unified assistant and one set of governance rules.
- Sovereign Privacy & Masked Records: Your privacy team requires routine access to customer records to be masked and logged, with an explicit written reason for each unmasking.
Is one platform better than a messaging tool plus an AI add-on?
The view: when the inbox, the AI agent and the back office live in different products, each boundary adds a login, a permission model and a place where context can drop.
The evidence: WATI and Astra are separate products. Astra needs its own account, created with the same email as the WATI account, and one Astra agent connects to one WhatsApp number at a time. In Converiqo AI (by Mobiloitte), the assistant, live-agent console, knowledge, tasks and reports sit in one workspace, governed by one set of roles.
Why it matters: one audit trail and one permission model make it simpler to prove who saw and changed what.
Who signs off what the AI tells your customers?
The view: in regulated or reputation-sensitive businesses, speed of answer matters less than whether the answer was approved.
The evidence: Astra learns from website links, documents and Q&A pairs, and is retrained when content changes. Knowledge connectors for Notion, Google Drive and Confluence are marked “Coming Soon”. We found no public documentation of a knowledge-approval step or of answer citations. In Converiqo AI, a Knowledge Editor submits content, and a different authorised reviewer approves it before the assistant can use it. Answers cite the source, and unanswered questions return as draft FAQs that need the same approval.
Why it matters: when a customer disputes an answer, you can show the approved policy it came from. See preventing hallucinations in enterprise AI.
How are changes tested and released?
The view: WATI has invested seriously in testing. Astra generates test cases from an agent's instructions, scores efficiency, accuracy and latency, and suggests fixes for review. That is useful and worth copying.
The evidence: what we could not find is agent versioning, staged release or rollback. Converiqo AI adds release control. A change is packaged as a release candidate containing the flow and its dependencies, then validated against knowledge and business data. It is scenario-tested for lead capture, booking and human handoff, and published with a release reference you can trace later.
Why it matters: testing tells you an agent answers well today; release control tells you exactly what changed when it stops.
What happens after the customer's question is answered?
The view: for service businesses, the reply is often step one of five.
The evidence: Astra's documented actions create or update contacts in HubSpot, Salesforce, Zoho or Pipedrive, book meetings, post messages to Slack and call external REST APIs. Internal approvals and multi-step back-office work are not publicly documented. Converiqo AI creates owned tasks and multi-step processes, opens support tickets with response and resolution targets, dispatches field jobs with check-in and proof of service, and runs procurement from purchase request to purchase order. See ticketing system automation and field force automation.
Why it matters: nothing falls between the chat and the job; each step has an owner and a status.
Which channels can the AI agent actually work on?
The view: a unified inbox is not the same as an AI agent that works on every channel in it.
The evidence: WATI's inbox covers WhatsApp, Instagram, Messenger, TikTok and website chat, with SMS through the customer's own Twilio account. Astra is documented on WhatsApp, a web widget and voice. Astra on Instagram and Messenger, and Telegram or email as conversation channels, are not publicly documented. Converiqo AI runs one assistant across the website, WhatsApp, email, SMS, Instagram, Messenger, Telegram, LinkedIn and voice. On email, AI-suggested replies show their approved-source evidence and are reviewed before sending. See WhatsApp Business, Email AI Agent and Telegram AI Agent.
Why it matters: customers who switch channels get the same approved answers and the same history.
How does one patient journey run on each platform?
| Step | WATI + Astra (as documented) | Converiqo AI (by Mobiloitte) |
|---|---|---|
| Report status | Astra can call the lab system through a REST API action | The assistant checks status through the lab system's API and replies, citing the approved report-turnaround policy |
| Home collection | Meeting booking through Calendly or Google Calendar | A home-collection slot is booked and a field job is created for a phlebotomist, with route and visit window |
| Fasting instructions | WhatsApp template message or campaign | An approved template goes out through a notification policy, within consent and quiet-hour rules |
| Collection visit | Not publicly documented | The phlebotomist checks in and out, and proof of collection attaches to the job |
| A complaint about delay | Human handoff to the WATI Team Inbox | A support ticket opens with an owner and a resolution target, and a follow-up survey closes the loop |
Every step in the Converiqo AI column lands in one record the lab manager can open. Bikaner Laboratory uses Converiqo AI for patient communication; see healthcare workflow automation.
How do WATI and Converiqo AI compare on security and privacy?
WATI documents several strong basics: a separate database for each customer, Docker isolation on Google Cloud, twice-daily backups, 2FA, API key rotation, read-only audit logs of sign-ins, and dedicated hosting with a choice of location. It lists 10 predefined roles. WhatsApp messages are deleted automatically after 30 days. Custom roles, single sign-on and masking of personal data are not publicly documented.
Converiqo AI (by Mobiloitte) adds controls a data protection officer can inspect:
- Custom roles that grant only the actions and records a job needs, OIDC single sign-on and MFA.
- Masked records by default. Opening a protected record needs a written reason, lasts a limited time, and appears in a client-visible operator-access log.
- Outreach checks: consent is recorded by channel and purpose, with do-not-contact, caller-ID and working-hour checks before any outbound call or campaign.
- Controlled deletion: retention settings, and erasure that previews its exact scope and needs a typed confirmation.
Converiqo AI is designed to support obligations under India's DPDP Act, the EU GDPR and the UAE PDPL. See security and data compliance.
How does Converiqo AI hold up as volumes grow?
As volumes grow, Converiqo AI gives administrators a system-health view with provider health and delivery events for every channel. An audit log can be filtered by who changed what. A lifecycle tool previews the impact of structural changes, snapshots configuration and keeps a rollback path. Queue monitoring and agent reporting show wait times and handoffs across live-agent teams.
Should you move from WATI or run both?
Run both: keep WATI for broadcast campaigns and ads on one WhatsApp number, and use Converiqo AI for service and operations on another. Converiqo AI exchanges data with your CRM and other systems through scoped API keys and verified webhooks.
Move over: Meta documents how a business phone number migrates from one WhatsApp solution partner to another. A typical sequence:
- Import contacts with their consent records and opt-outs intact.
- Load and approve knowledge through the review queue.
- Re-create message templates and routing logic.
- Connect channels and test inbound and outbound delivery.
- Run end-to-end journeys before switching live traffic.
Frequently asked questions
Bring one WhatsApp journey to a 30-minute AI Workflow Audit
We'll trace one of your busiest WhatsApp journeys, from the first message to finished work. You'll see where approved knowledge, tickets, field jobs or approvals would take the load off your team.