AI Agent Strategy

Per-tenant AI assistants for TruxFlow & 10-4 Hire

Planning: Apr 20, 2026
2
Products
$0.27
Min GPU/hr
72B
Max Model Size
~500
Concurrent Users

The Vision

Every customer who signs up for TruxFlow or 10-4 Hire gets their own AI assistant that already knows:

🤖
Customer's AI Agent
Unique to each tenant
⬇️
📱
App Knowledge
How to use TruxFlow/10-4 Hire
🚚
Industry Knowledge
Trucking, FMCSA, DOT
📋
Job Workflows
Dispatch, recruit, invoice
➕
🏢
Company Context
Their settings & users
📊
Their Data
Drivers, loads, clients
💬
Conversation History
Learns preferences

Example Use Cases

TruxFlow Dispatch

"Schedule driver John for the Chicago load tomorrow"
Agent: Checks John's availability, assigns Load #4521 to John, updates dispatch board, sends notification

TruxFlow Compliance

"Which drivers have expiring medical cards this month?"
Agent: Queries driver records, lists 3 drivers with expirations, offers to send reminder emails

TruxFlow Billing

"Create invoice for ABC Logistics for last week"
Agent: Pulls completed loads, calculates totals with accessorials, generates invoice PDF

10-4 Hire Recruiting

"Find CDL-A drivers in Texas with hazmat"
Agent: Searches candidate database, returns 12 matches, offers to send outreach emails

10-4 Hire Onboarding

"What documents are missing for new hire Marcus?"
Agent: Checks onboarding checklist, identifies 3 missing docs, sends reminder to Marcus

Analytics

"What's our average detention time this quarter?"
Agent: Calculates from load data, shows 2.3 hours average, compares to last quarter

👥 One Agent, All Roles

A single AI agent handles all staff members simultaneously — no need for separate agents per department

🤖
Same AI Agent
Handles ALL concurrent requests • Role-aware responses
⬇️
🚚
Dispatcher
"Assign John to Chicago"
🛡️
Safety
"Expiring med cards?"
💰
Accountant
"Invoice for ABC"
⚙️
Admin
"Add new user"
⬇️
Dispatch Tools
Safety Tools
Accounting Tools
Admin Tools

How Role Separation Works

Each request includes user context — the agent automatically respects permissions:

{ "user": "sarah@acme.com", "role": "dispatcher", "permissions": ["dispatch.read", "dispatch.write", "safety.read"], "carrier_id": "acme_trucking" } // Agent automatically: // - Shows only tools they can use // - Queries only data they can see // - Refuses actions outside permissions

📊 Real-World Scaling Example

Small carrier with 10-20 trucks

Role Staff Count Est. Queries/Day
Dispatchers 3-4 50-100
Safety Officer 1 10-20
Accountant 1 10-20
Admin 2 5-10
Total ~8 users ~75-150/day

✅ One Agent Handles This Easily

  • LLM server handles 50-200 concurrent users
  • You have 8 total staff members
  • ~100 queries/day is trivial load
  • No need for multiple agents

Est. Cost: ~$60-90/month on Claude API

❌ Why NOT Sub-Agents Per Module

  • 4x complexity (4 separate prompts)
  • Need routing logic to pick agent
  • More context overhead
  • No cost savings — same tokens

Verdict: More work, same cost, no benefit

When You WOULD Need Multiple Agents

Separate Products

TruxFlow vs 10-4 Hire

Different knowledge bases, different tools — separate agents make sense

Massive Scale

1000+ concurrent users

Multiple LLM servers for load balancing (same agent, replicated)

Specialized Fine-Tuning

Custom models per department

Rare — only if one department needs highly specialized responses

System Architecture

How the AI agent integrates with your existing stack

💻
TruxFlow / 10-4 Hire Frontend
Chat widget embedded in app
⬇️
🔌
Your API Gateway
Auth • Tenant Context • Tool Routing
⬇️
🧠
LLM Server (vLLM)
Qwen 72B or Llama 70B • Batched Inference
⬇️
🗄️
TruxFlow DB
📧
Email/SMS
📄
Doc Gen

Key Components

🎯 Base Knowledge Layer

Same for all tenants

  • Complete app documentation
  • Industry regulations (FMCSA, DOT, HOS)
  • Workflow templates and best practices
  • Common troubleshooting guides

🏢 Tenant Context Layer

Unique per customer

  • Company profile and settings
  • User list and permissions
  • Custom fields and workflows
  • Conversation history

🔧 Tool Layer

Actions the agent can take

  • CRUD operations (drivers, loads, invoices)
  • Search and reporting
  • Send notifications
  • Generate documents

Open-Source LLM Options

Models capable of running your AI agents

Model Parameters VRAM Required Quality Best For
Qwen 2.5 72B 72B ~80GB (2x A100) ⭐⭐⭐⭐⭐ Best overall quality, tool use
Llama 3.1 70B 70B ~80GB (2x A100) ⭐⭐⭐⭐⭐ Strong reasoning, large community
Qwen 2.5 32B 32B ~40GB (1x A100) ⭐⭐⭐⭐ SWEET SPOT Good balance
Llama 3.1 8B 8B ~10GB (1x RTX 4090) ⭐⭐⭐ Budget option, fast responses
Mixtral 8x22B 141B (MoE) ~90GB ⭐⭐⭐⭐ Efficient, good for variety

Serving Stack

vLLM

Best for production, high concurrency
Continuous batching ✓
PagedAttention ✓
OpenAI-compatible API ✓

Text Generation Inference

HuggingFace's solution
Easy model loading ✓
Tensor parallelism ✓
Docker-ready ✓

Ollama

Simplest setup
One-command install ✓
Model management ✓
Lower throughput ⚠

Cost Analysis

Self-hosted vs. API: when does each make sense?

☁️ Claude API

$0.01-0.05 per conversation
  • Zero infrastructure to manage
  • Pay only for what you use
  • Best quality responses
  • Instant setup, no GPU needed
  • Scales automatically

Best for: <1,000 conversations/day, starting out, validating product-market fit

🖥️ Self-Hosted (70B)

~$2,000 /month fixed
  • 2x A100 80GB GPUs
  • 50-200 concurrent users
  • No per-request costs
  • Full data control
  • Unlimited conversations

Best for: >1,000 conversations/day, data privacy requirements, predictable costs

Breakeven Analysis

At $2,000/month GPU cost vs. $0.03/conversation API cost:

~2,200/day Breakeven point
← API cheaper Self-hosted cheaper →

GPU Rental Cost Breakdown

Configuration Hourly Daily (24h) Monthly Can Run
1x RTX 4090 (24GB) $0.27 $6.50 $195 7B-14B models
1x A100 80GB $1.29 $31 $930 32B models
2x A100 80GB $2.58 $62 $1,858 70B models RECOMMENDED
8x A100 80GB $10.32 $248 $7,430 405B models

Cost Optimization Strategies

Spot Instances

~50% cheaper, can be interrupted

Good for dev/test, risky for production

Reserved Pricing

~30% cheaper with commitment

Best for predictable, long-term use

Scale Down Off-Hours

~40% savings if customers are 9-5

Auto-scale based on demand

Smaller Model

8B model = 1x RTX 4090 = $200/mo

Trade quality for cost

GPU Cloud Providers

Where to rent GPUs for your LLM server

🟢 Vast.ai

Cheapest - P2P Marketplace
RTX 4090 $0.27/hr
A100 80GB $1.89/hr
Reliability Variable ⚠️

🔵 RunPod

Best balance - Flexible
RTX 4090 $0.34/hr
A100 80GB $1.39/hr
H100 $1.99/hr

🟣 Lambda Labs

Premium - ML-focused
RTX 4090 $0.50/hr
A100 80GB $1.29/hr
Support Excellent ⭐

🟠 HOSTKEY

Dedicated servers
A100 80GB €1.53/hr
H100 80GB €2.00/hr
Dedicated HW ✓

Recommendation by Use Case

Use Case Provider Configuration Monthly Cost
Testing / Development Vast.ai RTX 4090 (on-demand) $0-50
Small Production (<50 users) RunPod 1x A100 80GB ~$1,000
Production (50-200 users) Lambda Labs 2x A100 80GB ~$1,900
Large Scale (>200 users) HOSTKEY / RunPod 4-8x A100 or H100 $4,000+

Implementation Roadmap

Recommended approach: start with API, migrate to self-hosted at scale

Phase 1: Prototype (2-4 weeks)

Build with Claude API to validate concept quickly

  • Create base knowledge system prompt
  • Define 5-10 core tools (search, create, update)
  • Build simple chat endpoint
  • Test with internal users

Phase 2: Beta (1-2 months)

Roll out to select customers, gather feedback

  • Embed chat widget in TruxFlow/10-4 Hire
  • Add tenant context loading
  • Expand tool coverage
  • Monitor usage and costs

Phase 3: Production (1 month)

Full rollout, optimize for scale

  • Enable for all customers
  • Add conversation history / memory
  • Build analytics dashboard
  • Track breakeven metrics

Phase 4: Self-Hosted (when ready)

Migrate to own infrastructure when usage justifies

  • Set up vLLM on GPU cloud
  • Deploy Qwen 72B or Llama 70B
  • Migrate API calls to self-hosted
  • Implement auto-scaling

Decision Criteria for Self-Hosting

✅ Move to Self-Hosted When:

  • >2,000 conversations/day sustained
  • API costs exceed $2,000/month
  • Data residency requirements
  • Need custom model fine-tuning
  • Want predictable fixed costs

❌ Stay on API When:

  • Usage is variable/unpredictable
  • Don't want to manage infrastructure
  • Need highest quality responses
  • Still validating product-market fit
  • Small team, limited DevOps

Pricing & Guardrails

Control costs and keep agents focused on work

🛡️ Agent Guardrails

Prevent users from using the agent for general questions outside your product scope

System Prompt Guardrails

You are a TruxFlow AI Assistant. You ONLY help with: • TruxFlow features, navigation, and workflows • Trucking and dispatch operations • Driver management, loads, invoicing, compliance • FMCSA regulations and DOT requirements You DO NOT answer questions about: • General knowledge, trivia, or facts • Coding, programming, or technical help outside TruxFlow • Personal advice, recipes, entertainment • Other software or competitors If asked off-topic, respond: "I'm here to help with TruxFlow and your trucking operations. Is there something specific about dispatching, drivers, or loads I can help you with?"

✅ Agent Will Answer

  • "How do I create a new load?"
  • "Which drivers are available tomorrow?"
  • "What's the HOS rule for team drivers?"
  • "Generate invoice for last week's deliveries"
  • "Show me loads going to Chicago"

❌ Agent Will Decline

  • "What's the capital of France?"
  • "Write me a Python script"
  • "What's the weather today?"
  • "Tell me a joke"
  • "How do I use [competitor software]?"

📊 Per-Customer Usage Tracking

Track and bill each tenant separately

💬
Customer Sends Message
tenant_id: "acme_trucking"
⬇️
📝
API Gateway Logs Request
tenant_id + timestamp + input_tokens
⬇️
🧠
LLM Processes
Generates response
⬇️
💾
Log Output Tokens
Store: tenant_id, total_tokens, cost
⬇️
📈
Usage Dashboard
Monthly report per tenant

Database Schema

CREATE TABLE ai_usage (
  id SERIAL PRIMARY KEY,
  tenant_id VARCHAR(50),
  user_id VARCHAR(50),
  conversation_id VARCHAR(100),
  input_tokens INTEGER,
  output_tokens INTEGER,
  cost_usd DECIMAL(10,6),
  created_at TIMESTAMP DEFAULT NOW()
);

💰 Pricing Model Options

Choose how to charge customers for AI usage

🎁 Included Free

AI as a feature differentiator
Monthly Cost $0
Limit 100-500 conv/mo

Best for: Competitive advantage, simple billing

📊 Tiered Plans

Upsell to higher tiers
Basic 100 conv/mo
Pro 500 conv/mo
Enterprise Unlimited

Best for: Predictable revenue, plan differentiation

📈 Usage-Based

Pay for what you use
Per Conversation $0.03-0.05
Your Margin 50-100%

Best for: Fair pricing, heavy users pay more

🔀 Hybrid

Base + overage
Included 200 conv/mo
Overage $0.02/conv

Best for: Balance of predictability + fairness

Example: Hybrid Pricing Calculator

Customer Plan Included Actual Usage Overage AI Charge
Acme Trucking Pro ($99/mo) 300 conv 250 conv 0 $0
FastFreight LLC Pro ($99/mo) 300 conv 450 conv 150 × $0.02 $3.00
MegaHaul Corp Enterprise ($299/mo) 1,000 conv 2,500 conv 1,500 × $0.02 $30.00

⚡ Rate Limiting & Abuse Prevention

Per-User Limits

  • Max 50 messages per hour
  • Max 500 messages per day
  • Cooldown after rapid-fire messages
  • Warning at 80% of limit

Per-Tenant Limits

  • Monthly conversation cap
  • Soft limit: warning email
  • Hard limit: block or charge overage
  • Admin dashboard visibility

✅ Implementation Checklist

1. System Prompt Guardrails

Add scope restrictions to base prompt - prevents off-topic usage

2. Usage Logging Middleware

Log every request with tenant_id, tokens, timestamp to database

3. Rate Limiting

Implement per-user and per-tenant rate limits with Redis

4. Usage Dashboard

Build admin view showing per-tenant usage, costs, trends

5. Billing Integration

Connect usage data to Stripe for overage charges (if applicable)

Voice Integration

Let customers talk to the AI agent instead of typing

💰 Voice vs Text Cost Comparison

Component Text Input Voice Input Notes
Input Processing Free ~$0.006/min (STT) Speech-to-Text conversion
LLM Processing ~$0.01-0.03 ~$0.01-0.03 Same cost either way
Text Output Free Free Display response as text
Voice Output (optional) N/A ~$0.005-0.02 Text-to-Speech conversion
Total per Interaction ~$0.02 ~$0.03-0.05 Voice adds ~50-100% cost

🎙️ Speech-to-Text (STT) Providers

Convert customer voice to text for the LLM

OpenAI Whisper

Best accuracy, easy API
Price $0.006/min
Accuracy ⭐⭐⭐⭐⭐
Languages 50+

Best for: Production use, multilingual support

Google Cloud Speech

Enterprise-grade, real-time
Price $0.006-0.024/min
Accuracy ⭐⭐⭐⭐⭐
Real-time ✓

Best for: Real-time streaming, enterprise

Azure Speech

Microsoft ecosystem
Price $0.006/min
Accuracy ⭐⭐⭐⭐
Custom Models ✓

Best for: Custom vocabulary (trucking terms)

Web Speech API

Browser built-in, FREE
Price FREE
Accuracy ⭐⭐⭐
Browser Support Chrome, Edge

Best for: MVP, testing, budget-conscious

🔊 Text-to-Speech (TTS) Providers

Convert AI responses to spoken audio (optional)

OpenAI TTS

Natural voices, simple API
Price $0.015/1K chars
Quality ⭐⭐⭐⭐
Voices 6 options

ElevenLabs

Most realistic, premium
Price $0.18-0.30/1K chars
Quality ⭐⭐⭐⭐⭐
Voice Cloning ✓

Google Cloud TTS

WaveNet voices
Price $0.004-0.016/1K chars
Quality ⭐⭐⭐⭐
Voices 200+

Browser SpeechSynthesis

Built-in, FREE
Price FREE
Quality ⭐⭐
Robotic Yes ⚠️

🏗️ Voice Integration Architecture

🎤
User Speaks
"Schedule driver John for Chicago tomorrow"
⬇️
🎙️
Speech-to-Text (STT)
Whisper API: $0.006/min
⬇️
🧠
LLM Processes Text
Same as text input
⬇️
📝
Text Response
Free - always show
🔊
Voice Response
TTS: ~$0.01 (optional)

📋 Recommendations by Phase

🧪 MVP / Testing

FREE

  • Web Speech API for input (browser)
  • SpeechSynthesis for output (browser)
  • No additional costs
  • Works in Chrome/Edge
  • Lower accuracy, robotic voice

Use to: Validate demand before investing

🚀 Production

~$0.01-0.02 /interaction

  • OpenAI Whisper for input
  • Text output (no TTS) or OpenAI TTS
  • High accuracy
  • Natural sounding
  • Easy to implement

Use for: Real customers, good UX

✨ Premium Experience

~$0.05+ /interaction

  • Whisper for input
  • ElevenLabs for output
  • Human-like voices
  • Custom voice cloning
  • Wow factor for demos

Use for: Enterprise clients, sales demos

🧮 Monthly Cost Example

Scenario Voice Interactions/mo Avg Duration STT Cost TTS Cost Total Voice Cost
Light usage 500 30 sec $1.50 $3.75 $5.25
Medium usage 2,000 30 sec $6.00 $15.00 $21.00
Heavy usage 10,000 30 sec $30.00 $75.00 $105.00

* Based on OpenAI Whisper ($0.006/min) + OpenAI TTS ($0.015/1K chars, ~500 chars/response)

✅ Implementation Checklist

1. Add Microphone Button

UI button in chat widget to start/stop recording

2. Capture Audio

Use MediaRecorder API to capture user's voice

3. Send to STT

POST audio to Whisper API, get text back

4. Process with LLM

Same flow as text input from here

5. Optional: TTS Response

Convert response to audio, play back to user

Quick Start: Browser Web Speech API (Free)

// Free browser-based speech recognition const recognition = new webkitSpeechRecognition(); recognition.continuous = false; recognition.interimResults = false; recognition.onresult = (event) => { const transcript = event.results[0][0].transcript; sendToAgent(transcript); // Send to your AI agent }; // Start listening when user clicks mic button micButton.onclick = () => recognition.start();

MCP Integration

Model Context Protocol — the open standard for AI-to-application integration

🔌 What is MCP?

Model Context Protocol

Open standard by Anthropic for connecting AI models to external tools and data sources.

  • AI auto-discovers available tools
  • Standardized protocol (not custom APIs)
  • Works with Claude, GPT, and other LLMs
  • Tools + Resources + Prompts
  • Built-in authentication handling

Why It Matters for TruxFlow

One integration enables multiple AI clients.

  • Your built-in AI agent uses it
  • Customers can connect their AI tools
  • Enterprise selling point: "AI-ready"
  • No custom API docs per AI provider
  • Future-proof for new AI models

📊 Custom API vs MCP Comparison

Aspect Custom REST API MCP Server
Your AI Agent Build custom API calls Tools auto-discovered
Customer AI Integration They build to your API docs Connect via MCP standard
Multiple AI Providers Build adapter per provider One MCP server, any AI
Tool Discovery Read docs, hardcode AI asks "what can you do?"
Maintenance API docs, SDKs, versioning Self-describing protocol
Auth/Permissions Custom implementation Built-in context passing

🏗️ TruxFlow Modules → MCP Tools

Our modular architecture maps directly to MCP

🤖
Your AI Agent
🏢
Customer's Claude
🌐
Any MCP Client
⬇️
🔌
TruxFlow MCP Server
Exposes modules as MCP tools • Handles auth/tenant context
⬇️
🛡️
Safety
getDriver, checkCompliance
🚚
Dispatch
createLoad, assignDriver
💰
Accounting
createInvoice, getPayments

💻 Code Mapping: Module → MCP Tool

Current: Module Exports

// modules/safety/index.js module.exports = { getDriver, getDriversByCarrier, checkDriverCompliance, getTruck, };

MCP: Same Functions as Tools

// mcp/safety-tools.js { "truxflow.safety.getDriver": { description: "Get driver by ID", parameters: { driverId: "string" }, handler: getDriver } }

🔐 Multi-Tenant MCP Context

MCP handles tenant isolation via context passing

// MCP request includes tenant context { "tool": "truxflow.dispatch.getLoads", "context": { "tenant_id": "acme_trucking", "user_id": "user_123", "permissions": ["dispatch.read", "dispatch.write"], "carrier_ids": ["carrier_456", "carrier_789"] }, "parameters": { "status": "active" } } // MCP server enforces permissions before calling module

🛤️ Two Integration Paths

Path A: REST API Only

Traditional approach

Your AI → REST API → TruxFlow
Customer AI → REST API → TruxFlow
(they build to your docs)
  • More work per integration
  • Maintain API docs
  • Build SDKs per language

Path B: MCP + REST API

Recommended approach

Your AI → MCP → TruxFlow
Customer AI → MCP → TruxFlow
Mobile/Web → REST API → TruxFlow
  • One MCP server for all AI
  • Self-describing tools
  • REST for non-AI clients

🧰 Proposed MCP Tools by Module

🛡️ Safety Module

Driver & Equipment Management
getDriver Get driver details
listDrivers Search/filter drivers
checkCompliance Verify driver status
getExpiringDocs Documents expiring soon

🚚 Dispatch Module

Load & Assignment Management
createLoad Create new load
assignDriver Assign driver to load
updateLoadStatus Update load progress
getAvailableDrivers Find drivers for load

💰 Accounting Module

Billing & Payments
createInvoice Generate invoice
getUnpaidInvoices Outstanding balances
recordPayment Log payment received
getDriverPay Calculate driver earnings

📊 Analytics Module

Reports & Insights
getRevenueSummary Revenue by period
getDriverPerformance Driver metrics
getDetentionReport Detention time analysis
getLaneAnalysis Profitable routes

📅 Implementation Timeline

Phase 1: MCP Server Setup

Create MCP server scaffold, authentication, tenant context handling

Phase 2: Safety Tools

Expose Safety module functions as MCP tools (read-only first)

Phase 3: Dispatch Tools

Add Dispatch module tools with write operations

Phase 4: Your AI Agent

Connect built-in AI agent to MCP server

Phase 5: Customer MCP Access

Allow customers to connect their AI tools via MCP

✨ Key Benefits

For TruxFlow

  • One integration for all AI clients
  • Self-documenting tools
  • Future-proof for new AI models
  • Reduced maintenance burden
  • Enterprise selling point

For Customers

  • Connect their own AI (Claude, GPT)
  • No custom development needed
  • Works with existing AI workflows
  • Secure, permissioned access
  • "AI-ready" platform

Chat & Messaging System

Real-time communication between dispatchers, staff, and drivers

💬 Chat Types for Trucking

1:1 Direct Messages

Private conversations
Dispatcher ↔ Driver ✓
Safety ↔ Driver ✓
Admin ↔ Anyone ✓

Group Chats

Team conversations
Carrier Team All staff + drivers
Dispatcher Team Dispatchers only
Load-Specific Driver + dispatcher

Smart Chats

Auto-created contexts
Load assigned Auto-create chat
Share location In-chat GPS
Send BOL/POD Direct photo share

✨ Modern Chat Features

Feature Description Priority
Real-time messaging WebSocket/SSE, instant delivery MUST
Push notifications Mobile + web alerts MUST
File/image sharing Photos, documents, BOLs, PODs MUST
Read receipts Seen/delivered indicators HIGH
Typing indicators "John is typing..." HIGH
Voice messages Record & send audio (great for drivers) HIGH
Offline support Queue messages when no signal HIGH
Message search Find past conversations MEDIUM
@mentions Tag specific users in groups MEDIUM
Reactions Quick emoji responses NICE
Link previews URL thumbnails NICE

🛠️ Build vs Buy Options

Option A: Chat SDK

Stream, SendBird, etc.

$99/mo + 2-3 weeks dev

  • 10,000 MAU included
  • Unlimited messages
  • All features built-in
  • SDKs for React Native, iOS, Android
  • Hosted infrastructure

Best for: Fast to market, proven reliability

Option B: Build Custom

WebSockets + PostgreSQL

$50-100/mo + 6-8 weeks dev

  • No per-user fees ever
  • Full control over features
  • Integrates with existing stack
  • Own your data completely
  • More maintenance burden

Best for: Long-term cost savings, full control

📊 Chat SDK Comparison

Provider Starter Price Includes Msg Limits Best For
Stream $99/mo 10,000 MAU Unlimited RECOMMENDED
SendBird $399/mo 5,000 MAU Unlimited Enterprise features
PubNub $98/mo 200 MAU Per transaction Small scale only
Twilio ~$0.001/msg Pay as you go Per message Low volume

MAU = Monthly Active Users (unique users who send at least 1 message). Each user can send unlimited messages.

💰 Cost Projection at Scale

TruxFlow Scale Total Users Stream SDK Custom Build
10 carriers × 20 users 200 MAU $99/mo ~$50/mo
50 carriers × 20 users 1,000 MAU $99/mo ~$75/mo
200 carriers × 25 users 5,000 MAU $99/mo ~$100/mo
500 carriers × 20 users 10,000 MAU $99/mo (at limit) ~$150/mo
1000+ carriers 20,000+ MAU Custom (~$300-500) ~$200/mo

🏗️ Chat Architecture

📱
Driver App
React Native
💻
Web App
TruxFlow Dashboard
⬇️
💬
Chat Service
Stream SDK or Custom WebSocket Server
⬇️
🔔
Push
FCM / APNs
📁
Files
S3 Storage
🗄️
History
PostgreSQL

🚚 Trucking-Specific Enhancements

Load-Linked Chats

Auto-created when load assigned

Chat thread attached to each load. All communication in one place. Archived when load delivered.

Location Sharing

Real-time GPS in chat

Driver taps to share location. Dispatcher sees on map. "ETA 2 hours" auto-calculated.

Document Sharing

BOL, POD, rate cons

Driver photos BOL → sent in chat → auto-attached to load record in TruxFlow.

AI Agent in Chat

Optional integration

@TruxBot in any chat. "What's John's next load?" Agent responds with info.

📅 Implementation Timeline

With Chat SDK (Stream)

Week 1: Setup

SDK integration, auth connection

Week 2: Core Features

1:1 chat, groups, file sharing

Week 3: Polish

Push notifications, offline, testing

Custom Build

Week 1-2: Backend

WebSocket server, DB schema, API

Week 3-4: Core Features

Messaging, groups, file uploads

Week 5-6: Advanced

Push, offline sync, read receipts

Week 7-8: Polish

Testing, optimization, mobile

✅ Recommendation

Start with Stream SDK

  • 2-3 weeks to launch vs 6-8 weeks custom
  • $99/mo covers up to 500 carriers
  • All modern features out of the box
  • Proven reliability and scale
  • Can migrate to custom later if needed

ROI: At $99/mo, you need just 1-2 customers paying for chat feature to break even. Everything after is profit.

Email System

Unified email hub inside TruxFlow — manage load emails, rate cons, broker communication with AI assistance

📧 The Vision

📧
TruxFlow Email Hub
Unified inbox inside the app • MCP-ready for AI
⬇️
🏢
TruxFlow Email
@dispatch.truxflow.app
📨
Gmail
Link existing account
📬
Outlook
Link existing account
🌐
Any IMAP
Yahoo, custom domain

🔄 Two Operating Modes

Mode A: TruxFlow Email

We provide the email address

dispatch@acmetrucking.truxflow.app
— or —
dispatch@acmetrucking.com (bring your domain)
  • Clean, dedicated dispatch inbox
  • Full control and deliverability
  • No external account needed
  • Simple setup

Mode B: Link Existing Email

Connect Gmail, Outlook, etc.

dispatch@acmetrucking.com
(their existing Gmail/Outlook)
  • Keep existing email address
  • Sync inbox into TruxFlow
  • Send/receive from TruxFlow
  • OAuth secure connection

✨ Key Features

Feature Description MCP Tool
Unified Inbox All emails in TruxFlow UI getEmails
Send/Reply Compose and respond to emails sendEmail, replyToEmail
AI Extraction Parse rate cons → Create Load automatically extractLoadFromEmail
Link to Records Attach emails to Loads, Brokers linkEmailToLoad
Templates Quick responses, rate negotiations sendFromTemplate
Full-Text Search Find any email instantly searchEmails
Bulk Actions Archive, label, forward multiple bulkEmailAction
Threading Conversation view like Gmail getThread

🤖 AI Agent Email Use Cases

Find Rate Cons

"Check my inbox for new rate cons from ABC Logistics"
Agent: searchEmails(from: "abclogistics", subject: "rate con") → Returns 3 emails with load details extracted

Create Load from Email

"Create a load from that Chicago email"
Agent: extractLoadFromEmail → createLoad(Chicago→Miami, $2,800) → linkEmailToLoad

Confirm Load

"Reply and confirm we'll take it"
Agent: sendEmail(to: broker, template: "load_confirmation", loadDetails)

Check Broker History

"Show me all emails from XYZ Freight this month"
Agent: searchEmails(from: "xyzfreight", dateRange: thisMonth) → Lists 15 conversations

🛠️ Build Options

Option A: Nylas API

Unified API for all providers

$10 /account/month

  • Handles Gmail, Outlook, Yahoo, IMAP
  • OAuth and sync handled for you
  • 3-4 weeks development
  • Proven reliability

Best for: Fast launch, multiple providers

Option B: Custom Build

Direct Gmail API + IMAP

~$100 /month hosting

  • No per-account fees
  • Full control
  • 8-12 weeks development
  • Handle OAuth refresh, rate limits

Best for: Scale, cost savings long-term

💰 Cost Projection (Nylas)

Carriers Email Accounts Nylas Cost Custom Build
10 carriers ~20 accounts $200/mo ~$100/mo
50 carriers ~100 accounts $1,000/mo ~$150/mo
200 carriers ~400 accounts $4,000/mo ~$200/mo
500 carriers ~1,000 accounts $10,000/mo ~$300/mo

Note: At scale, custom build saves significant cost. Start with Nylas, migrate later.

🏗️ Technical Architecture

💻
TruxFlow Email UI
Inbox, compose, threads, search
⬇️
⚙️
TruxFlow Email Service
Sync • Store • Search • MCP Tools
⬇️
🔌
Nylas/API
Gmail, Outlook
📤
SendGrid
TruxFlow emails
🗄️
PostgreSQL
Email storage
📁
S3
Attachments

🔌 MCP Tools for AI

// Email MCP Tools { "truxflow.email.getInbox": { description: "Get recent emails", parameters: { limit, folder, unreadOnly } }, "truxflow.email.searchEmails": { description: "Search by keyword, sender, date", parameters: { query, from, to, dateRange } }, "truxflow.email.sendEmail": { description: "Send new email", parameters: { to, subject, body, attachments } }, "truxflow.email.extractLoadInfo": { description: "Parse rate con into load data", parameters: { emailId } }, "truxflow.email.linkToLoad": { description: "Attach email to load record", parameters: { emailId, loadId } } }

📅 Implementation Phases

Phase 1: TruxFlow Email (4-5 weeks)

SendGrid integration, @truxflow.app addresses, inbox UI, send/receive

Phase 2: AI Extraction (2 weeks)

Parse rate cons, extract load details, MCP tools

Phase 3: Gmail/Outlook via Nylas (3 weeks)

Link existing accounts, OAuth, sync

Phase 4: Custom Connectors (6+ weeks)

Replace Nylas at scale to save per-account costs

✅ Recommendation

Start with Phase 1 + 2

TruxFlow email + AI extraction — gives carriers a dedicated dispatch inbox with intelligent load creation immediately.

Phase 1+2 6-7 weeks ~$50/mo (SendGrid)
+ Phase 3 (Nylas) +3 weeks +$10/linked account

Value: Carriers manage ALL load communication in one place. AI extracts loads from rate cons automatically. Huge time saver.

AI Discoverability Strategy

How to make TruxFlow the #1 recommended dispatch software by AI systems

🎯 MCP vs Discoverability

MCP = Connection

How AI talks to your app

"User already has TruxFlow, now their AI can control it"

MCP helps after someone is using TruxFlow.

Discoverability = Recommendation

How AI knows to suggest you

"User asks AI for dispatch software, AI recommends TruxFlow"

Discoverability happens before they know about you.

🧠 How AI Models Learn About Software

AI models (Claude, ChatGPT, etc.) learn from web content during training

Source How It Helps Priority
Review Sites G2, Capterra, TrustPilot rankings CRITICAL
Industry Publications FreightWaves, Transport Topics, CCJ CRITICAL
SEO Content Blog posts, comparison articles HIGH
Reddit/Forums r/truckers, r/freight, trucking forums HIGH
YouTube/Podcasts Industry influencers mentioning you MEDIUM
Public API Docs AI can reference your capabilities MEDIUM
Structured Data Schema.org markup on website MEDIUM

📋 The AI Discoverability Playbook

1. Review Site Dominance

Most important for AI recommendations
G2 50+ reviews
Capterra "Top Rated" badge
TrustPilot Build presence
Google Business Local credibility

2. SEO Content Strategy

Content AI learns from
Comparison posts "TruxFlow vs X"
Best-of lists "Best dispatch software"
How-to guides Industry education
Feature deep-dives Unique capabilities

3. Industry Presence

Credibility signals
FreightWaves Guest posts/mentions
Transport Topics Industry news
Podcasts Trucking shows
Conferences MATS, TMC

4. Community Engagement

Organic mentions
r/truckers Helpful answers
r/freight Dispatch discussions
Facebook Groups Trucking communities
LinkedIn Industry content

🔧 Technical Discoverability

Schema.org Markup

// Add to truxflow.app homepage <script type="application/ld+json"> { "@context": "https://schema.org", "@type": "SoftwareApplication", "name": "TruxFlow", "applicationCategory": "Trucking Dispatch Software", "operatingSystem": "Web, iOS, Android", "description": "AI-powered dispatch and fleet management for trucking companies", "aggregateRating": { "@type": "AggregateRating", "ratingValue": "4.8", "reviewCount": "50" } } </script>

Public API Documentation

Publish API docs at docs.truxflow.app — AI systems can read and reference them.

🤖 AI-Specific Channels

Custom GPT (OpenAI)

Create "TruxFlow Dispatch Assistant"

  • Free to create
  • Shows in GPT Store
  • People searching "dispatch" find it
  • Direct lead generation

Claude Integration

When marketplace launches

  • MCP integration listed
  • Claude users can connect
  • Official Anthropic channel
  • Enterprise visibility

AI Tool Directories

Emerging discovery channels

  • Product Hunt AI
  • There's an AI for That
  • FuturePedia
  • AI tool aggregators

📝 Content That Gets AI Attention

Write articles that answer questions people ask AI:

Comparison Content

"TruxFlow vs Samsara for small carriers"
Detailed feature comparison with honest pros/cons

Best-Of Lists

"Best dispatch software for owner operators 2026"
Include TruxFlow with specific differentiators

Problem-Solution

"How to automate trucking dispatch"
Educational content positioning TruxFlow as solution

Industry Trends

"AI in trucking dispatch: what's possible in 2026"
Thought leadership mentioning TruxFlow capabilities

📅 Implementation Timeline

Phase Actions Timeline Impact
Phase 1 G2/Capterra reviews campaign Now HIGH
Phase 2 Schema.org + public API docs 2 weeks MEDIUM
Phase 3 SEO content (10-20 articles) 1-2 months HIGH
Phase 4 Custom GPT + AI directories 1 month MEDIUM
Phase 5 Industry PR (FreightWaves, etc.) Ongoing HIGH
Phase 6 MCP registry when available Future HIGH

⚡ Quick Wins (Do This Week)

1. Create Custom GPT

Free, takes 1 hour. "TruxFlow Dispatch Expert" shows in GPT Store immediately.

2. Claim Review Profiles

Set up G2, Capterra, TrustPilot if not done. Start asking customers for reviews.

3. Add Schema.org Markup

15 minutes of code. Helps search engines and AI understand TruxFlow.

4. Publish API Docs

Even basic docs help AI reference your capabilities.

💡 Key Insight

AI Recommendations = Digital Word of Mouth

When someone asks Claude or ChatGPT "What's the best dispatch software for a small trucking company?", the AI recommends based on:

  • Review ratings — G2/Capterra scores
  • Content mentions — How often TruxFlow appears in articles
  • Industry authority — FreightWaves, trade publications
  • Community sentiment — Reddit, forums

Bottom line: MCP is table stakes for AI integration. Discoverability is marketing + content + presence. Do both.