Omnisonant Design Guide

Product: Omnisonant — Every channel, one voice.

Tagline: AI voice agents that handle calls so humans don't have to.

Repo: git@github.com:Forth-AI/omnisonant.git

Trigger: When designing, reviewing, or improving Omnisonant features, voice agent behaviors, or architecture.

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1. The Paradigm Shift

Traditional Call Center Model

Customer calls → IVR maze → Hold music → Human agent → Manual CRM update
                    ↓           ↓            ↓               ↓
               Frustrating   Expensive    Inconsistent   Error-prone

Problems:

• Cost: $15-25/hour per agent
• Availability: Limited hours, timezone constraints
• Consistency: Agent quality varies
• Scale: Hiring/training bottleneck
• Data: Manual entry, lost context

Omnisonant Model

Customer calls → Voice agent → Instant resolution → Automatic CRM update
                     ↓               ↓                    ↓
                24/7 ready    Perfectly consistent    Zero manual work

Value:

• Cost: $0.10-0.20 per call (95%+ savings)
• Availability: 24/7/365, any timezone
• Consistency: Same quality every time
• Scale: Infinite parallel calls
• Data: Automatic logging, full transcripts

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2. Target Audiences

Primary: SMB Owners

Profile: Small business owners who hate phone work

Vertical	Pain	Voice Agent Use
Dental/Medical clinics	Staff on phones all day	Appointment confirmation, rescheduling
Real estate agencies	Leads go cold while agents are busy	Lead qualification, showing scheduling
E-commerce	Can't afford 24/7 support	Order status, returns, basic support
Professional services	Missed calls = missed revenue	Intake calls, appointment booking
Restaurants	Reservations interrupt service	Booking, waitlist management

Buying motivation: "I want to fire my phones"

Design implication: Must be self-service, no technical setup required.

Secondary: Voice AI Resellers/Agencies

Profile: Agencies building voice solutions for clients

Type	Need	Omnisonant Value
Marketing agencies	Add voice to service offering	White-label, easy deployment
IT consultants	Modernize client operations	Proven platform, fast implementation
BPO companies	Reduce headcount, increase margin	Hybrid human+AI workforce

Buying motivation: "I want to sell this to my clients"

Design implication: Multi-tenant, white-label capable, reseller dashboard.

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3. Three Core Use Cases

Use Case 1: Appointment Scheduling (Outbound)

Replaces: Staff calling to confirm/reschedule appointments

Example vertical: Dental clinic

Flow:

Agent calls patient → Confirms tomorrow's appointment →
  ✓ Confirmed: "Great, see you at 2pm"
  ↻ Reschedule: Opens calendar, finds slot, books
  ✕ Cancel: Marks cancelled, offers future booking
→ Updates calendar automatically

Voice Agent Script Pattern:

"Hi, this is Sarah from [Business Name].
I'm calling to confirm your appointment tomorrow at [time] with [provider].
Can you make it?"

[If yes] "Great! We'll see you then. Is there anything you need before your visit?"

[If reschedule] "No problem! Let me check what we have available.
How about [alternative 1] or [alternative 2]?"

[If cancel] "I understand. Would you like me to book a future appointment,
or shall I have someone call you later?"

Key metrics:

• Confirmation rate: Target 80%+
• Reschedule rate: Track, optimize
• No-show reduction: Target 50%+
• Call duration: Target <2 min

Design requirements:

• Calendar integration (Google, Outlook, practice management)
• Smart slot suggestion (based on availability + preferences)
• Reminder confirmation (SMS after call)
• Retry logic (voicemail, callback attempts)

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Use Case 2: Lead Qualification (Outbound)

Replaces: SDRs making initial qualification calls

Example vertical: Real estate

Flow:

Lead submits form → Agent calls within 5 minutes →
  Qualifies: Budget, timeline, preferences →
    ✓ Hot lead: Books showing with human agent
    ~ Warm lead: Adds to nurture sequence
    ✕ Cold lead: Marks as not ready
→ Updates CRM with full notes

BANT Qualification Script:

"Hi [Name], this is Alex from [Agency].
You inquired about homes in [area]. Do you have a few minutes to chat?"

[Budget] "What price range are you looking at?"

[Authority] "Will anyone else be involved in the decision?"

[Need] "What's prompting your move? More space, new job, investment?"

[Timeline] "When are you hoping to move by?"

[If qualified] "Based on what you've told me, I think [Agent Name]
would be perfect to show you some properties. They're available
[time slots]. Which works for you?"

Key metrics:

• Contact rate: Target 60%+
• Qualification completion: Target 70%+
• Lead-to-meeting conversion: Target 30%+
• Speed to lead: Target <5 min

Design requirements:

• CRM integration (Salesforce, HubSpot, etc.)
• Lead scoring based on answers
• Intelligent routing to human agents
• A/B testing for scripts
• Time-of-day optimization

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Use Case 3: Customer Support (Inbound)

Replaces: Tier-1 support agents handling common queries

Example vertical: E-commerce

Flow:

Customer calls → Agent identifies (phone/order#) →
  📦 Order status: Pulls from system, provides update
  ↩️ Return request: Creates RMA, sends label
  ❓ General question: Answers from knowledge base
  ⚠️ Complex issue: Escalates to human
→ Logs interaction, updates ticket

Support Script Pattern:

"Thank you for calling [Company]. This is your AI assistant.
How can I help you today?"

[Order status] "I'd be happy to check on your order.
Can I get your order number or the email address on the account?"
→ "I found your order. It shipped on [date] and should arrive by [date].
Would you like me to text you the tracking number?"

[Return] "I can help you start a return. Which item would you like to return?"
→ "I've created a return label for you. It's being sent to [email].
Is there anything else I can help with?"

[Escalation] "I want to make sure you get the best help for this.
Let me connect you with a specialist. Please hold for just a moment."

Key metrics:

• Resolution rate (no human needed): Target 70%+
• Customer satisfaction: Target 4+/5
• Average handle time: Target <3 min
• Escalation rate: Target <30%

Design requirements:

• Order system integration
• Return/refund workflow automation
• Knowledge base for FAQs
• Seamless escalation to human
• Post-call survey option

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4. Voice Agent Design Principles

Principle 1: Sound Human, Be Honest

Right: "Hi, this is Sarah, an AI assistant calling from..."
Wrong: Pretending to be human without disclosure

Right: Natural speech patterns, appropriate pauses
Wrong: Robotic cadence, unnatural phrasing

Why: Trust requires transparency. Deception backfires.

Principle 2: Graceful Interruption Handling

Right: Stop talking when customer speaks, acknowledge, respond
Wrong: Keep talking over customer, ignore interruption

Right: "Oh, go ahead!" → listens → responds to what they said
Wrong: "Please wait for me to finish"

Why: Natural conversation requires turn-taking.

Principle 3: Fast and Focused

Right: Get to the point, respect their time
Wrong: Long introductions, excessive pleasantries

Right: "Hi, this is Sarah from Bright Smile confirming your appointment tomorrow at 2pm. Can you make it?"
Wrong: "Hello! How are you doing today? I hope you're having a wonderful day! I'm calling from..."

Why: People hate phone calls. Make them short.

Principle 4: Recover Gracefully

Right: "I didn't quite catch that. Could you repeat the date?"
Wrong: "Error. Invalid input. Please try again."

Right: "Hmm, I'm having trouble finding that order. Let me connect you with someone who can help."
Wrong: [Silence] or [Hang up]

Why: Errors happen. Recovery maintains trust.

Principle 5: Confirm Before Acting

Right: "So I'll book you for Thursday at 3pm with Dr. Chen. Does that sound right?"
Wrong: "Done. Goodbye." [Hangs up]

Right: Wait for confirmation before finalizing
Wrong: Assume and execute without verification

Why: Mistakes are costly. Confirmation is cheap.

Principle 6: End with Clear Next Steps

Right: "You'll get a text confirmation in a moment. Is there anything else?"
Wrong: "Okay, bye."

Right: Tell them what happens next
Wrong: Leave them wondering

Why: Closure creates confidence.

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5. Voice & Personality Guidelines

Voice Selection Criteria

Factor	Consideration
Gender	Match brand perception; test with audience
Accent	Match target market; consider regional preferences
Tone	Professional for B2B, friendly for B2C
Speed	Slightly slower than normal speech (clarity)
Energy	Match context (upbeat for sales, calm for support)

Personality Traits

For appointment scheduling:

• Friendly, efficient, respectful of time
• "I know you're busy, so I'll be quick"

For lead qualification:

• Curious, engaged, consultative
• "Tell me more about what you're looking for"

For customer support:

• Patient, helpful, solution-oriented
• "Let me take care of that for you"

Things Voice Agents Should NEVER Do

• Pretend to be human when directly asked
• Get frustrated or impatient
• Argue with the customer
• Share information about other customers
• Make promises outside their authority
• Continue calling after "stop calling me"

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6. Technical Architecture Principles

Dual Pipeline Support

Pipeline	Use Case	Tradeoffs
Vapi + Twilio	Production phone calls	Higher latency (~500ms), real phone numbers, proven scale
OpenAI Realtime	Web demo, premium UX	Lower latency (~200ms), browser-based, cutting-edge

Design implication: Abstract voice pipeline so agents work on either.

Latency Budget

Ideal conversation turn:
  Customer speaks     → 500ms → Agent responds

Acceptable:
  Customer speaks     → 800ms → Agent responds

Frustrating:
  Customer speaks     → 1500ms+ → Agent responds

Design implication: Every millisecond matters. Optimize ruthlessly.

Tool Execution Model

Customer: "What's the status of my order?"
    ↓
Agent: [Thinking] "Let me check that for you"
    ↓
Tool call: lookupOrder({ phone: "+1..." })
    ↓
Agent: "Your order shipped yesterday and should arrive Friday."

Design implication: Tools must be fast (<1s) and reliable.

Fallback Strategy

Level 1: Agent handles completely
Level 2: Agent + tool call
Level 3: Agent transfers to human
Level 4: Agent takes message for callback

Design implication: Never dead-end. Always a path forward.

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7. Value Proposition Checklist

Every feature must deliver on at least one:

✅ Cost Reduction

• Does this reduce cost per call?
• Does this reduce need for human agents?
• Is ROI measurable and significant?

✅ Availability Improvement

• Does this extend service hours?
• Does this handle more concurrent calls?
• Does this reduce wait times?

✅ Consistency Improvement

• Does this ensure same quality every call?
• Does this reduce human error?
• Does this improve compliance?

✅ Scale Enablement

• Does this remove hiring bottleneck?
• Does this handle demand spikes?
• Does this expand geographic reach?

Red flags (features that don't fit):

• "Requires human review for every call" ❌
• "Only works during business hours" ❌
• "Needs custom development per client" ❌
• "Improves metrics but costs more" ❌

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8. Interface Patterns

Admin Dashboard

Primary actions:

1. View active calls (live monitoring)
2. Review call history + transcripts
3. Configure voice agents
4. Manage campaigns (outbound)
5. View analytics

Key UX requirements:

• Real-time call status visibility
• One-click access to any call transcript
• Easy agent script editing
• Clear performance metrics

Agent Builder

Primary actions:

1. Define agent persona (name, voice, personality)
2. Set greeting and conversation flow
3. Configure available tools
4. Test with sample calls
5. Deploy to phone number

Key UX requirements:

• Natural language prompt editing
• Voice preview (hear before deploy)
• Sandbox testing environment
• A/B testing support

Campaign Manager (Outbound)

Primary actions:

1. Upload/select contact list
2. Choose agent and script
3. Set calling schedule and rules
4. Monitor progress
5. Review results

Key UX requirements:

• Bulk contact management
• Scheduling controls (time windows, timezone handling)
• Real-time progress dashboard
• Export results to CRM

Web Demo Interface

Primary actions:

1. Click to start call
2. Speak with agent
3. See live transcript
4. Experience the product

Key UX requirements:

• One-click to start (no signup for demo)
• Visual audio feedback
• Live transcript display
• Mobile-friendly

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9. Anti-Patterns (Omnisonant-Specific)

"Sounds Like a Robot"

Symptom: Unnatural speech, no personality, mechanical responses. Fix: Better prompts, voice selection, natural language patterns.

"IVR in Disguise"

Symptom: "Press 1 for...", rigid menu trees, no natural conversation. Fix: Open-ended listening, intent detection, flexible responses.

"Infinite Hold"

Symptom: Can't reach human when needed, escalation fails. Fix: Clear escalation paths, graceful handoffs, callback option.

"Amnesia Agent"

Symptom: Doesn't remember what was said earlier in call. Fix: Proper context management, conversation memory.

"Over-Promising Agent"

Symptom: Agent commits to things it can't deliver. Fix: Constrain agent authority, confirm before committing.

"The Interrogator"

Symptom: Feels like a survey, too many questions, no empathy. Fix: Conversational flow, acknowledge answers, show understanding.

"Uncanny Valley"

Symptom: Too human-like in a way that's creepy. Fix: Honest about being AI, consistent persona, appropriate boundaries.

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10. Competitive Positioning

vs. Traditional Call Centers

Dimension	Call Center	Omnisonant
Cost per call	$5-15	$0.10-0.20
Availability	8-12 hours	24/7
Consistency	Variable	Perfect
Scale time	Weeks (hiring)	Minutes
Data capture	Manual	Automatic

Omnisonant advantage: 95%+ cost reduction with better consistency.

vs. IVR Systems

Dimension	IVR	Omnisonant
User experience	"Press 1 for..."	Natural conversation
Resolution rate	Low (frustration)	High (actual help)
Flexibility	Rigid menus	Open-ended
Updates	IT project	Prompt change

Omnisonant advantage: People hate IVR. They tolerate or even enjoy good AI.

vs. Vapi (Direct)

Dimension	Vapi DIY	Omnisonant
Target	Developers	Business owners
Setup	Build it yourself	Ready-to-use
Templates	Generic	Industry-specific
Integrations	You build	Pre-built

Omnisonant advantage: Vapi is infrastructure. Omnisonant is solution.

vs. Other Voice AI Platforms

Dimension	Others	Omnisonant
Multi-channel	Often single	Phone + web + more
White-label	Limited	Built for resellers
Pricing	Complex	Simple per-minute

Omnisonant advantage: "Omni" in the name is the promise.

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11. Review Checklist

When reviewing Omnisonant designs:

Voice Quality

• Does it sound natural?
• Is there appropriate personality?
• Are interruptions handled well?
• Is latency acceptable (<800ms)?

Conversation Quality

• Does it get to the point quickly?
• Does it confirm before acting?
• Does it recover from errors gracefully?
• Does it end with clear next steps?

Business Value

• Does this reduce cost per call?
• Does this extend availability?
• Does this improve consistency?
• Does this enable scale?

Integration Quality

• Does data flow to CRM automatically?
• Are actions executed in real systems?
• Is escalation to humans seamless?
• Are transcripts accessible?

User Experience (Admin)

• Can they set up without technical help?
• Can they monitor calls in real-time?
• Can they make changes without coding?
• Can they measure ROI?

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12. Key Metrics

Call Quality

• Resolution rate: Calls resolved without human (Target: 70%+)
• Customer satisfaction: Post-call rating (Target: 4+/5)
• Average handle time: Call duration (Target: <3 min)
• Error rate: Calls with issues (Target: <5%)

Business Impact

• Cost per call: All-in cost (Target: <$0.25)
• Conversion rate: Leads converted, appointments booked (varies)
• ROI: Savings vs. human agents (Target: 10x+)

Technical Performance

• Latency: Time to first response (Target: <500ms)
• Uptime: System availability (Target: 99.9%+)
• Accuracy: Speech recognition accuracy (Target: 95%+)

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13. Feature Prioritization Framework

Must Have (P0)

• Core voice conversation capability
• At least one use case working end-to-end
• Basic analytics (calls, duration, outcomes)
• Phone number provisioning

Should Have (P1)

• All three use cases polished
• CRM integrations (top 3)
• Campaign management
• Transcript search

Nice to Have (P2)

• White-label support
• Custom voice training
• Advanced analytics
• Multi-language

Won't Build (v1)

• Video calling
• Chat/SMS (v2)
• Custom voice cloning
• On-premise deployment

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14. The Omnisonant Promise

To SMB owners:

"Your phone rings, AI answers. Appointments get confirmed. Leads get qualified. Customers get helped. You get your time back. All for less than the cost of a part-time receptionist."

To resellers:

"Add voice AI to your service offering. White-label our platform. Your clients get cutting-edge technology. You get recurring revenue."

Every design decision should reinforce this promise.

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15. Voice Agent Prompt Template

Use this structure for creating voice agents:

## Agent Identity
- Name: [Agent name, e.g., "Sarah"]
- Company: [Business name]
- Role: [What they do, e.g., "appointment coordinator"]

## Personality
[2-3 sentences describing tone, style, approach]

## Goal
[Primary objective of this call]

## Key Information to Gather/Share
1. [Item 1]
2. [Item 2]
3. [Item 3]

## Available Actions
- [Action 1: e.g., "Book appointment"]
- [Action 2: e.g., "Check availability"]
- [Action 3: e.g., "Transfer to human"]

## Constraints
- Never [constraint 1]
- Always [constraint 2]
- If [condition], then [action]

## Escalation Triggers
- [When to transfer to human]
- [When to offer callback]

## Closing
[How to end the call professionally]