The biggest misconception about voice AI deployment is that it takes months and requires a dedicated engineering team. For most Indian businesses — NBFCs, EdTechs, real estate developers, insurance distributors — it takes 5–7 working days with zero internal engineering.
Here's exactly what those 7 days look like.
Day 1: Define Your Use Case and Flow
Before touching any platform, spend Day 1 with your operations team defining exactly what the AI needs to do:
- What is the primary call objective? (Collect payment intent, book appointment, resolve issue)
- What data does the AI need to reference? (Account balance, policy number, lead details)
- What languages should the AI use? (Hindi, Hinglish, and which regional languages)
- What are the 5 most common customer responses? (Including objections)
- What happens when the AI can't resolve? (Escalation path)
Document these in a simple one-page call flow. This becomes the AI's "brain" for the deployment.
Day 2: Platform Setup and Knowledge Base
Sign up for Agni, select your plan, and upload your knowledge base — this is the information the AI uses to answer customer questions. For most deployments, this is:
- Product/service FAQs (PDF or text)
- Pricing and policy information
- Objection responses (optional but recommended)
- Escalation criteria
The knowledge base upload takes 1–2 hours. The platform processes and indexes it automatically.
Day 3: Voice and Persona Configuration
Select the voice profile (male/female, Hindi, Hinglish, or regional), set the call pace and tone, and configure the opening disclosure (DPDP compliance). If you have specific phrases or brand language you want the AI to use, add them here.
Test the configured agent by calling it yourself — listen for natural flow, correct information retrieval, and appropriate tone.
Common Day 3 issues: Voice pace too fast (reduce speed to 0.88×), disclosure too long (trim to 2 sentences), objection responses too formal (add more colloquial variants).
Day 4: Integration
Connect Agni to your contact list source:
- CSV upload: For one-off campaigns (simplest)
- CRM integration: Zoho, HubSpot, Leadsquared — native connectors available
- API integration: For real-time lead delivery (requires light developer work)
- GoHighLevel: Native workflow integration, no code required
Day 5–6: Testing
Run test calls against a sample of 50–100 real accounts (internal team members roleplay as customers). For each test, evaluate:
- Did the AI correctly identify the call purpose?
- Did it handle the most common objections?
- Did it correctly escalate when necessary?
- Did the compliance disclosure run correctly?
Make configuration adjustments based on test feedback. This is the most important step — don't skip it.
Day 7: Soft Launch
Launch with 10% of your intended call volume. Monitor in real-time via the Agni dashboard. Check:
- Connection rate (target: 35–50% for outbound campaigns)
- Completion rate (target: 60–70% of connected calls)
- Escalation rate (target: 20–30% for complex use cases)
- Outcome rate (varies by use case — compare to your baseline)
If metrics look good, scale to 100% of volume on Day 8. If not, spend another day on configuration before scaling.
Post-Launch: The First 30 Days
The AI improves with use. In the first 30 days, review call transcripts weekly to identify edge cases — questions or scenarios that the AI didn't handle well. Each one becomes a knowledge base update or flow adjustment. By Day 30, most deployments are running at 85–90% of their maximum effectiveness.
