3. NeuroGen's 15-Platform Integration Architecture: Technical Validation

3.1 The Unified Service Interface

Every platform integration in NeuroGen implements the same core interface. Each service class exposes a send_message() method with a consistent signature:

# Every platform service implements this interface
def send_message(self, connection, recipient, text) -> bool:
    """Send a message to a user on this platform.
    Returns True on success, False on failure."""

This uniformity is not cosmetic. It means the NeuroGen routing layer treats all 15 platforms identically. When an agent generates a response, the dispatcher calls platform_service.send_message() without caring whether the destination is Telegram, Discord, or Nostr. The agent never knows which channel it is responding on. It only knows the conversation.

Code Evidence (from the production codebase — 15 confirmed implementations):

# telegram_bot_service.py
def send_message(self, bot_link, chat_id: int, text: str, ...) -> bool

# discord_service.py
def send_message(self, bot_token: str, channel_id: str, content: str, ...) -> bool

# slack_service.py
def send_message(self, bot_token: str, channel_id: str, text: str, ...) -> bool

# signal_bot_service.py
def send_message(self, connection, recipient: str, text: str) -> bool

# teams_bot_service.py
def send_message(self, connection, conversation_id: str, service_url: str, text: str) -> bool

# matrix_bot_service.py
def send_message(self, connection, room_id: str, text: str) -> bool

# nextcloud_bot_service.py
def send_message(self, connection, room_token: str, text: str) -> bool

# imessage_imsg_service.py
def send_message(self, connection, handle: str, text: str) -> bool

# imessage_bb_service.py
def send_message(self, connection, chat_guid: str, text: str) -> bool

# zalo_bot_service.py
def send_message(self, connection, user_id: str, text: str) -> bool

# zalo_personal_service.py
def send_message(self, connection, friend_id: str, text: str) -> bool

# nostr_bot_service.py
def send_message(self, connection, recipient_pubkey: str, text: str) -> bool

# tion_bot_service.py
def send_message(self, connection, peer_id: str, text: str) -> bool

# whatsapp_business_service.py
def send_message(self, phone_number_id: str, access_token: str, ...) -> bool

# webchat_service.py (self-hosted widget — NeuroGen IS the service)
# Routes via internal message queue to agent, responds via WebSocket

3.2 The Full Platform Registry

All 15 integrations are production-ready, with tier-gated access ensuring appropriate deployment economics across customer segments.

Platform	Tier	Auth Method	Service File	Notes
Telegram	Starter	BotFather webhook	telegram_bot_service.py	Per-user rate limiting, /start /help /reset commands
Discord	Starter	Developer Portal bot	discord_service.py	Discord API v10, guild-level bot permissions
Slack	Starter	Slack App, Events API	slack_service.py	OAuth2 workspace install, slash commands
WhatsApp Business	Starter	Meta Cloud API	whatsapp_business_service.py	WhatsApp Cloud API, template messages supported
WebChat Widget	Starter	Embeddable JS snippet	webchat_service.py	Self-hosted, CORS domain validation, SHA256 API keys
Signal	Professional	signal-cli bridge	signal_bot_service.py	signal-cli server-side bridge, end-to-end encrypted
Microsoft Teams	Professional	Azure Bot Framework	teams_bot_service.py	Azure Bot registration, Activity SDK
Matrix	Professional	Conduwuit homeserver	matrix_bot_service.py	Self-hostable, decentralized protocol
Nextcloud Talk	Professional	App password	nextcloud_bot_service.py	On-premise deployment friendly
iMessage (imsg)	Business	AppleScript bridge	imessage_imsg_service.py	Mac-based bridge via imsg
iMessage (BlueBubbles)	Business	BlueBubbles server	imessage_bb_service.py	BlueBubbles open-source server
Zalo OA	Business	Zalo Official Account API	zalo_bot_service.py	Southeast Asia's dominant messenger
Zalo Personal	Business	Personal account bridge	zalo_personal_service.py	Personal account messaging bridge
Nostr	Business	Decentralized keys	nostr_bot_service.py	Censorship-resistant, key-pair auth
Tion	Business	Internal integration	tion_bot_service.py	Internal team communication platform

VALIDATED: All 15 service files confirmed in production at /opt/neurogenserver/modules/services/.

3.3 Security Architecture Across All Channels

Platform credentials are not stored in plaintext. Every bot token, OAuth token, and API key is encrypted with Fernet symmetric encryption via SecureAPIKeyService, with per-service salts. The Telegram service file's header documents the security posture explicitly:

"Bot tokens encrypted with Fernet (via SecureAPIKeyService). Webhook URLs include random secret for verification. Per-user rate limiting."

-- telegram_bot_service.py, line 8-12

This pattern — Fernet encryption, webhook signature verification, per-user rate limiting — applies identically across all 15 integrations. Agency owners deploying NeuroGen for clients inherit enterprise-grade credential security without additional configuration.

3.4 The WebChat Widget: Zero-Infrastructure Deployment

The WebChat integration is different from the other 15 platforms: all others route inbound messages to NeuroGen's servers, but WebChat makes NeuroGen's server the endpoint itself.

The service file's header is explicit about this design:

"Unlike other integrations, WebChat is self-hosted. There is no external service to connect to — WE are the service. Visitors on external websites send messages to our API, and we route them to the user's configured agent."

-- webchat_service.py, line 11-14

Deployment is a single JavaScript snippet embedded in any web page. The widget handles CORS validation (domain whitelist), SHA256 API key verification, and real-time message routing to the configured agent. For agencies building client solutions, this means the client's website can have a live AI assistant running within minutes of agent configuration — no platform accounts, no OAuth flows, no webhooks to configure.

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4. The Voice Agent: AI Engagement via Real-Time Phone Calls

4.1 Architecture Overview

Text-based messaging reaches customers who are willing to type. Phone calls reach everyone else. NeuroGen's voice agent uses a dual-WebSocket architecture that eliminates transcription latency:

Phone → Twilio PSTN → Twilio Media Stream (WS) → NeuroGen Server → OpenAI Realtime API (WS) → back
Audio codec: g711_ulaw (mu-law 8kHz) — no transcoding at any hop

The key engineering decision is codec uniformity. Both Twilio's media stream and OpenAI's Realtime API are configured to use g711_ulaw — the same mu-law audio format used in telephony for decades. This means audio packets flow from phone to AI and back without transcoding. The 20ms audio packets Twilio sends are forwarded directly to OpenAI, and OpenAI's response audio is forwarded directly to Twilio. Latency is bounded by network round-trip time, not processing time.

Code Evidence:

# voice_agent_service.py - Session configuration
session_config = {
    "type": "session.update",
    "session": {
        "modalities": ["text", "audio"],
        "voice": self.voice,
        "input_audio_format": "g711_ulaw",
        "output_audio_format": "g711_ulaw",
        "input_audio_transcription": {"model": "whisper-1"},
        "turn_detection": {
            "type": "server_vad",
            "threshold": self._vad_threshold,
            "silence_duration_ms": self._silence_duration_ms
        },
        "instructions": self.system_message
    }
}

4.2 Voice Selection and Persona Management

NeuroGen supports 8 OpenAI Realtime voices, with automatic mapping from legacy Polly and Google voice names to ensure backward compatibility when clients migrate from older TTS systems:

# voice_agent_service.py
OPENAI_VOICES = {'alloy', 'echo', 'shimmer', 'ash', 'ballad', 'coral', 'sage', 'verse'}

# Legacy voice mapping — transparent migration from Polly/Google
_VOICE_MAP = {
    'Polly.Joanna-Neural': 'alloy',
    'Polly.Salli-Neural': 'shimmer',
    'Polly.Kimberly-Neural': 'coral',
    'Polly.Kendra-Neural': 'sage',
    'Polly.Ruth-Neural': 'verse',
    'Polly.Matthew-Neural': 'echo',
    'Polly.Joey-Neural': 'ash',
    'Polly.Stephen-Neural': 'ballad',
    'Google.en-US-Neural2-C': 'shimmer',
    'Google.en-US-Neural2-D': 'echo',
}

Every voice agent automatically receives a conversation-optimized instruction suffix that adapts the same system prompt used for text interactions to the constraints of spoken dialogue:

# voice_agent_service.py
VOICE_PROMPT_SUFFIX = (
    "\n\nIMPORTANT VOICE GUIDELINES: Keep responses concise — 2-3 sentences per turn "
    "for natural voice conversation. Speak conversationally and naturally. "
    "If you don't know something, say so rather than guessing."
)

4.3 Inbound and Outbound Call Flows

The voice agent handles both call directions with direction-aware persona management:

Inbound calls (customer calls the business): The agent is configured to be reactive — it listens, asks clarifying questions, and routes or resolves based on the caller's stated need. The system prompt receives an INBOUND_PROMPT_PREFIX that instructs the agent to let the caller lead.

Outbound calls (the AI initiates contact): The agent is configured to be proactive — it has a defined agenda and leads with purpose. The OUTBOUND_PROMPT_PREFIX instructs the agent to introduce itself, state its purpose immediately, and close with a clear next step. The AI speaks first, triggered by _trigger_ai_greeting() at session initialization.

Six pre-built conversation flow templates are available for rapid deployment:

Flow	Direction	Stages
inbound_support	Inbound	Greet → Identify Issue → Search KB → Resolve or Escalate → Confirm
inbound_sales	Inbound	Greet → Qualify → Present Options → Handle Objections → Close
outbound_followup	Outbound	Introduce → Reference Context → Check In → Offer Help → Close
outbound_sales	Outbound	Open → Hook → Discover → Match → Handle Concerns → Next Step
appointment_reminder	Outbound	Introduce → Confirm Appointment → Handle Reschedule → Confirm → Close
outbound_demo_followup	Outbound	Reconnect → Check Experience → Address Questions → Suggest Plan → Close

4.4 Contact History Injection

The voice agent system integrates directly with the communications module's contact and call history. When a call arrives from a known phone number, the system automatically loads recent interaction history and injects it into the voice agent's context:

# voice_agent_service.py — load_contact_history()
# Loads last N calls from CallLog + recent SMS from SMSConversation
# Injects formatted history string into system_message before session starts
# "PREVIOUS INTERACTIONS WITH THIS CONTACT: [date]: [direction] — [summary]"

An outbound sales agent calling a prospect who inquired via SMS last week can open the conversation with specific context from that inquiry. This is the practical implementation of the cross-channel memory that 70 percent of customers expect but rarely receive.

4.5 Knowledge Base Integration for Voice

The voice agent can be connected to any NeuroGen knowledge base. The load_kb_context_for_voice() function retrieves indexed knowledge base content and injects it into the voice agent's system message before the call begins, with caching to prevent repeated database queries:

# voice_agent_service.py
def load_kb_context_for_voice(kb_id, max_tokens=12000, voice_optimized=False):
    """Load KB content for injection into voice agent system_message.
    Redis cache (5-min TTL) to avoid DB hits on repeated calls.
    voice_optimized=True caps at 6,000 tokens for faster initialization."""

A support agent that knows the company's product documentation in text also knows it on the phone. The knowledge base does not need to be reconfigured for voice deployment.

4.6 Tier-Gated Call Duration

Call duration limits enforce appropriate usage economics across tiers:

Tier	Max Duration	Credits/Minute
Demo	2 minutes	35
Starter	5 minutes	35
Professional	15 minutes	35
Business	30 minutes	35
Enterprise	60 minutes	35 (configurable)

All limits are environment-variable configurable. Agency owners deploying for enterprise clients can negotiate custom limits through the admin panel.

VALIDATED: VoiceAgentSession, build_voice_system_message(), load_kb_context_for_voice(), load_contact_history(), build_direction_prompt(), CONVERSATION_FLOWS, and all 6 flow templates confirmed in production at /opt/neurogenserver/modules/services/voice_agent_service.py.

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5. Cross-Channel Memory: One Conversation Spanning All Platforms

5.1 The Memory Architecture

Omnichannel deployment without shared memory is theater. The customer experience of 15 separate bots with the same name and appearance is functionally identical to 15 separate bots — the agent does not know what happened on other channels, so it cannot act on that knowledge.

NeuroGen's memory architecture ensures that an agent deployed across all 15 platforms maintains a single continuous memory for each user. Three systems work together:

Conversation Persistence: Every message on every platform is stored in agent_conversations with the full message history as a JSON array. The user_id foreign key ensures all conversations across all platforms are indexed against the same user identity. An agent can retrieve and resume any past conversation regardless of the platform it originated on.

AgentMemoryIndex: Beyond raw conversation storage, NeuroGen extracts structured memory from interactions. The memory index stores typed records — facts, entities, preferences, procedures — with confidence scores, source timestamps, access counts, and last-accessed timestamps for relevance scoring. This allows the agent to retrieve specific facts ("this user prefers email over phone") without loading or scanning full conversation histories.

Knowledge Base RAG: Every agent's connected knowledge base is available across all channels. Product documentation, FAQs, company policies — anything in the knowledge base answers questions identically whether the question arrives on Telegram, Discord, a phone call, or the website widget.

5.2 TTS Across Channels

For channels that support audio, NeuroGen's Text-to-Speech service provides 300+ voices via Edge-TTS. The same voice characteristics used in a Telegram voice message can be matched to the voice used in a phone call, keeping audio identity consistent across modalities.

5.3 Standalone Chat: The Public Web Interface

Beyond the 15 platform integrations, every AI assistant and agent can be deployed as a standalone chat interface at a public URL (neurogen.cc/chat/<token>). The standalone chat uses a professional 2-panel design — sidebar with conversation history, main panel with full-width messages — matching the UI conventions established by Claude, ChatGPT, and Gemini.

White-label customization is fully supported: custom colors via CSS variables (--primary-color, --secondary-color), custom logo, custom domain via DNS verification and Let's Encrypt SSL, and custom CSS injection. For agency deployments, each client can have a standalone chat branded entirely to their own identity, running on their own domain, backed by the same NeuroGen agent infrastructure.

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6. The Communications Suite: SMS, Email, and Automated Outreach

6.1 Ten-Tab Communications Module

Beyond the 15 chat platform integrations, NeuroGen includes a full communications suite built into the platform. The Communications module provides 10 coordinated functions accessible from a single interface:

Tab	Function
Dialer	Browser-based PSTN calling via Twilio WebRTC
AI	Voice agent configuration and outbound AI call launcher
SMS Inbox	Two-way SMS/MMS with conversation threading
Email	SMTP + SendGrid outbound with templates
Contacts	CRM with CSV import, lead scoring, custom fields
Campaigns	Outbound dialer campaigns with disposition tracking
Call History	Recordings, AI-generated transcripts, call scoring
Phone Numbers	Marketplace provisioning for local presence
Calendar	Appointment scheduling with confirmation flows
Autoresponder	Multi-step drip sequences with conditional branching

The Autoresponder is worth noting for agency deployments. It supports multi-step sequences across email, SMS, and voice: a prospect can enter a workflow that sends an email on day 1, an SMS on day 3, and triggers an AI voice call on day 7, all within the same campaign configuration. Conditional sequence branching (v4.1) enables if/else routing based on email opens, link clicks, or non-response — the same logic used in enterprise marketing automation platforms, available at the Starter tier.

6.2 Social Media Unified Inbox

The Social Media module adds a 16th engagement surface: the unified social inbox that aggregates Facebook and Instagram comments and direct messages via Meta Graph API v21.0. AI reply suggestions (Business tier and above) route through the same agent system that powers all 15 chat integrations, ensuring that the agent's knowledge base and personality apply equally to social media responses.

Lead capture rules — keyword triggers, engagement thresholds, DM-received triggers — automatically route qualified social leads into the CRM contacts table, where they become available for voice and SMS follow-up campaigns. The same prospect can be engaged on Instagram, moved into the CRM, enrolled in an SMS sequence, and receive an AI voice call — all from a single NeuroGen configuration.

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7. Competitive Positioning

7.1 The Channel Count Advantage

The chatbot and customer engagement market is dominated by platforms that specialize in two to six channels. Intercom, the market leader in web chat, supports web chat, email, and mobile — three channels. Zendesk adds phone via a paid add-on and WhatsApp via a separate paid integration. Drift, positioned at the enterprise end, focuses primarily on web chat and email. None supports more than six channels natively.

Capability	Intercom ($79-299/mo)	Zendesk ($55-115/agent/mo)	Drift ($2,500+/mo)	NeuroGen ($47-997/mo)
Web chat	Yes	Yes	Yes	Yes
Telegram	No	No	No	Yes
Discord	No	No	No	Yes
WhatsApp	Add-on ($)	Add-on ($)	No	Included
SMS	Add-on ($)	Add-on ($)	Limited	Included
Voice calls	No	Zendesk Talk ($)	No	Included (AI voice)
Signal	No	No	No	Yes
Microsoft Teams	Integration only	Integration only	Integration only	Native
Matrix	No	No	No	Yes
iMessage	No	No	No	Yes
Total channels	3-5	4-6	2-3	15 + voice + SMS + email
Custom AI agents	Limited/scripted	Basic	Limited	Full AG2 multi-agent
Per-user pricing	Yes	Yes	Yes	No — per-org flat rate
Cross-channel memory	Limited	No	No	Full cross-session
Knowledge base RAG	Limited	No	No	Three-tier (keyword + full-text + semantic)
Outbound AI voice	No	No	No	Yes

7.2 The Pricing Architecture Advantage

Intercom, Zendesk, and Drift all charge per seat. As engagement volume grows, costs scale linearly. For agency deployments, every client's growing user base translates directly into growing per-seat costs that eat margin.

NeuroGen's credit model charges per interaction, not per seat. A 10,000-user deployment costs the same monthly base rate as a 100-user deployment; only credit consumption changes. For agencies billing clients at fixed retainer rates, this means costs stay predictable even as client usage grows.

7.3 The Agency Business Model

For agency owners, NeuroGen's architecture opens two revenue streams that alternatives do not support:

White-label resale: NeuroGen's white-label customization lets agencies brand the entire platform — standalone chat URLs, widget appearance, email communications, and the standalone chat domain — under the agency's or client's brand. Clients see a branded AI assistant, not a third-party tool. That separation supports premium pricing for what is operationally a NeuroGen deployment.

Configuration leverage: A single agent configuration deploys across all 15 channels. When an agency builds a customer support agent for a client, that agent is immediately available on every channel the client needs — today's Telegram and website widget, next quarter's WhatsApp and Teams. Adding channels requires no new development, only enabling the integration. The agency can sell channel expansion as a recurring upgrade without proportional implementation cost.

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8. Platform Integration Summary

8.1 Compliance Matrix

Integration	Production File	send_message() Signature	Tier	Security
Telegram	telegram_bot_service.py	Confirmed	Starter	Fernet-encrypted token, webhook secret
Discord	discord_service.py	Confirmed	Starter	Fernet-encrypted token, API v10
Slack	slack_service.py	Confirmed	Starter	OAuth2, workspace install
WhatsApp Business	whatsapp_business_service.py	Confirmed	Starter	Meta Cloud API, token encryption
WebChat Widget	webchat_service.py	Internal routing	Starter	SHA256 API key, domain whitelist
Signal	signal_bot_service.py	Confirmed	Professional	signal-cli bridge, E2E encrypted
Microsoft Teams	teams_bot_service.py	Confirmed	Professional	Azure Bot Framework auth
Matrix	matrix_bot_service.py	Confirmed	Professional	App service token, homeserver auth
Nextcloud Talk	nextcloud_bot_service.py	Confirmed	Professional	App password auth
iMessage (imsg)	imessage_imsg_service.py	Confirmed	Business	AppleScript bridge, local auth
iMessage (BlueBubbles)	imessage_bb_service.py	Confirmed	Business	BlueBubbles API key
Zalo OA	zalo_bot_service.py	Confirmed	Business	Official Account API token
Zalo Personal	zalo_personal_service.py	Confirmed	Business	Personal bridge auth
Nostr	nostr_bot_service.py	Confirmed	Business	Ed25519 keypair, decentralized
Tion	tion_bot_service.py	Confirmed	Business	Internal API token
Voice (Twilio + OpenAI)	voice_agent_service.py	WebSocket bridge	Starter+	TLS, HMAC webhook signatures

8.2 Voice Agent Validation Summary

Component	File	Status
WebSocket bridge (Twilio ↔ OpenAI)	voice_agent_service.py	VALIDATED
g711_ulaw codec passthrough	VoiceAgentSession.initialize_session()	VALIDATED
8 OpenAI voice options	OPENAI_VOICES constant	VALIDATED
Legacy voice name mapping	_VOICE_MAP dictionary	VALIDATED
Inbound/outbound direction handling	build_direction_prompt()	VALIDATED
6 conversation flow templates	CONVERSATION_FLOWS dictionary	VALIDATED
Contact history injection	load_contact_history()	VALIDATED
KB context injection with Redis cache	load_kb_context_for_voice()	VALIDATED
Tier-based duration limits	TIER_MAX_DURATION	VALIDATED
Token usage tracking	handle_openai_message() → response.done	VALIDATED
AI speaks first (outbound)	_trigger_ai_greeting()	VALIDATED
Voice prompt suffix	VOICE_PROMPT_SUFFIX	VALIDATED

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9. Implementation Guide for Agency Owners

9.1 The Deployment Sequence

For agencies onboarding a new client, this sequence gets the core system running fast and leaves room to expand channels without rework:

Week 1 — Core agent configuration Configure the agent's system prompt, personality, and knowledge base. Upload the client's product documentation, FAQs, and company policies. Test on the standalone chat URL before activating any external channels.

Week 1 — Initial channel activation Enable the WebChat widget first — it requires no external account setup. Embed the JavaScript snippet on the client's website. Enable Telegram next; BotFather setup takes under five minutes. Both channels immediately share the agent's memory and knowledge base.

Week 2 — Communications integration Provision a phone number via the Numbers marketplace. Configure the AI voice agent for inbound calls using the inbound_support flow template. Test with internal calls. Enable the SMS inbox for two-way text conversations on the same number.

Week 3 — Platform expansion Connect WhatsApp Business, Slack, and Discord based on the client's audience profile. All connections reuse the same agent configuration — no prompt changes, no knowledge base updates, no additional content testing.

Ongoing — Analytics and optimization The Communications module's Call History tab surfaces AI-scored call recordings and transcripts. The Social Media module's Analytics tab shows engagement metrics across platforms. Both feed back into agent optimization without channel-specific reconfiguration.

9.2 Credit Economics for Agencies

Credit consumption is transparent and predictable:

• Text messages (all 15 platforms): Deducted via quick_ai_chat_sync() auto-deduction based on model and token count
• Voice calls: 35 credits per minute (admin-configurable via PlatformConfig)
• SMS campaigns: Credit rates configurable per service via get_comm_credit_rate()
• All rates: Admin-adjustable with 60-second Redis cache for performance

Agencies running NeuroGen at the Business tier ($297/month, 15,000 credits) have sufficient credit headroom for approximately 428 minutes of AI voice conversation, or thousands of text message exchanges, per month — well within the typical SMB client's usage profile.

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10. Conclusion

The customer engagement problem is not that businesses lack chatbots. It is that businesses have too many of them — a different bot on each channel, each with its own configuration, each unaware of what happened on every other channel. Customers experience this as inconsistency. Research from Harvard Business Review puts this at the top of the list of customer complaints, and the cost is measurable.

NeuroGen's approach addresses the problem at the architecture level. One agent, configured once, deployed across 15 messaging platforms plus real-time voice. When the agent's knowledge changes, all 15 channels update. When a customer switches from Telegram to a phone call, the agent has the conversation history. When a social media lead enters the CRM, the voice agent can call with that context already loaded.

The 15 platform service files in production — each implementing the same send_message() interface, each encrypting credentials with the same Fernet pattern, each routing through the same agent layer — are not the product of 15 separate engineering projects. They are the result of one architectural decision made at the platform's foundation: the channel is a transport layer, not the product. The product is the agent.

For agency owners building customer-facing AI, this architecture means clients who start with a website widget and Telegram can expand to voice, WhatsApp, and Teams without rebuilding. Every channel added increases the value of the existing agent configuration rather than creating a new maintenance burden.

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References

1. Salesforce (2024). "State of the Connected Customer, 6th Edition." Salesforce Research. Survey of 14,300 consumers across 25 countries. Key metrics: 9.4 average channels per customer; 88% rate experience equal to product quality.

2. Harvard Business Review (2024). "The Value of Keeping the Right Customers." Channel switching costs businesses 15-25% in customer satisfaction. Inconsistent cross-channel experiences identified as #1 customer complaint category.

3. Zendesk (2025). "CX Trends Report." 70% of customers expect full context continuity across interactions. 64% spend more when issues are resolved on their channel of origin. Methodology: survey of 8,000+ consumers and 4,800 business respondents.

4. Juniper Research (2024). "Chatbots: Trends, Market Analysis & Forecasts 2024-2028." AI chatbot market projected at $72B by 2028. Messaging platforms account for 65% of customer service interactions in key markets.

5. NeuroGen AI Engineering Division (2026). Production codebase audit of modules/services/ — 15 integration service files confirmed with send_message() interface at /opt/neurogenserver/modules/services/. Voice agent architecture confirmed in voice_agent_service.py (1,003 lines, March 2026).

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NeuroGen Omnichannel AI — 15 platforms, 1 brain, 0 context loss. NeuroGen Intelligence Report NIR-005 — March 11, 2026.