How a Trucking Company Built a $1M AI Dispatch Platform on No-Code (And What Ops Teams Can Reuse)

In 15 words or less

A trucking company built a $1M AI dispatch business on Glide. Here's the blueprint ops teams can steal.

In a few more words

Innovative Logistics Group (ILG) built iDispatchHub, an AI-powered dispatch platform, entirely on Glide, a no-code tool. No developers. No six-figure software contract. The results: $1M+ in sales, 20 hours saved per week, twice the carrier capacity without extra headcount, and a 12% average revenue bump for the trucking carriers using it.

But the real story isn't the tool. It's the pattern. ILG figured out how to layer AI document processing, automated workflows, role-based access, and a customer-facing portal on top of a spreadsheet database, and that exact five-layer stack is reusable in field services, facilities management, last-mile delivery, and pretty much any industry where someone dispatches someone else to do something.

Here's how they did it, and what you can lift wholesale.

What did ILG actually build?

Adam Wingfield runs Innovative Logistics Group, a Charlotte-based logistics company. Like most people in trucking, he saw the gap firsthand: 95% of US trucking carriers run fewer than 10 trucks, and none of the big transportation management systems (TMS) were built for them. "Larger transportation management systems simply left them out as users," Wingfield said. "Dispatchers really are not represented from a technology perspective just yet."

So he built his own.

iDispatchHub is a full dispatch operating system, built on Glide with two Glide Experts, Hassan Qureshi and Bob Petito, handling the technical build. Wingfield brought the domain knowledge. They shipped an MVP in about three months.

The platform now serves 1,000+ small trucking carriers. It handles the entire dispatch lifecycle: load sourcing, rate confirmation processing, driver assignment, real-time status tracking, proof of delivery, and billing. The AI reads PDFs, fills forms, and alerts everyone who needs to know. Pricing runs from $499.99/year (Basic) to $999.99/year (Standard), with a Professional tier on top, proper SaaS economics from a no-code foundation.

The Glide case study credits the platform with $1M+ in sales, 20 admin hours saved per week, and the ability to manage twice as many carriers without hiring.

Where does the AI actually do the work?

Two moments in the workflow make the AI indispensable.

First: rate confirmation ingestion. In trucking, every load starts with a rate confirmation PDF, a dense document with pickup/drop-off addresses, cargo details, rates, accessorial charges, and carrier instructions. Before iDispatchHub, a dispatcher would manually read that PDF and type everything into a dispatch form. Now the AI reads it, extracts every relevant field, auto-populates the dispatch record, and triggers notifications to the driver, the broker, and the customer. Wingfield described it as "almost error-free."

Second: proof of delivery. When a driver delivers, they take a photo on their phone. The AI processes the image, auto-completes the delivery record, and distributes the proof of delivery to all parties. No phone calls. No "did that load deliver yet?" emails.

Between those two moments, the platform runs profitability triggers, tracks load status in real time, and gives managers a dashboard to compare dispatcher performance.

The AI isn't doing anything exotic, it's doing the repetitive, error-prone stuff that burns 20 hours a week of dispatcher time. That's where the money sits.

Why did no-code beat a traditional TMS here?

Legacy TMS platforms, McLeod, TMW, MercuryGate, are built for fleets of 50+ trucks. They're expensive, they need implementation consultants, and they assume every carrier has an IT department. ILG's customers don't. They have a dispatcher, a handful of drivers, and a mobile phone.

Glide solved three things simultaneously that would've each been a separate headache with traditional software:

Cost. iDispatchHub started as an internal tool before becoming a commercial product. The build cost was essentially two Glide Experts for a few months, not a development team. At the SaaS pricing they charge now ($500–$1,000/year), their customers pay less in a year than one month of a legacy TMS licence.

Mobile-native from day one. Drivers don't have laptops. Glide apps run on phones with no extra work, the interface adapts automatically. A traditional TMS mobile app would've been a separate build.

Iteration speed. Glide ships new features constantly. "We keep up with Glide's new features when we implement our app release," Wingfield said. "As Glide gets better, we get better, too." Every time OpenAI improves or Glide adds a new AI integration, iDispatchHub absorbs it without a rebuild.

This last point is quietly the most important one. When you build on no-code, the platform shoulders the maintenance burden. ILG's competitive moat isn't the code, it's Wingfield's domain expertise encoded into workflows and AI prompts. The platform handles the plumbing.

What's the reusable pattern here?

Strip away the trucking specifics and iDispatchHub is a five-layer stack that maps onto any dispatch-heavy operation:

Layer 1: Structured data

Start with the thing everyone already has: a spreadsheet. ILG used Google Sheets. Loads, drivers, customers, rates, all living in structured tables that Glide reads as a database. If you can model your operation in rows and columns, you can build on it.

Layer 2: AI document processing

This is where the magic lives. Incoming documents, rate confirmations, work orders, invoices, get read by AI and auto-converted into structured data. The AI fills the forms instead of a human. For ILG this was ChatGPT via Glide's integrations. Today you could swap in Claude, a custom GPT, or whatever model handles your document type best.

Layer 3: Automated workflows and alerts

Every status change triggers something. Load dispatched → notify driver, broker, customer. Delivery confirmed → send proof of delivery to all parties. Exception flagged → alert the manager. These are simple "if this, then that" rules, the kind that take hours to build in custom code and minutes to configure on no-code.

Layer 4: Role-based access

Dispatchers see the full dispatch board. Drivers see only their assigned loads and a status-update button. Customers see their active shipments and delivery history. Managers see analytics. Getting this right is what separates an internal tool from a platform you can sell, and it's the layer where platforms like Stacker (with built-in user portals and granular permissions) have a natural advantage over spreadsheet-native tools.

Layer 5: Customer-facing portal

External stakeholders, brokers, shippers, end customers, get a login. They can track their loads, download proof-of-delivery documents, and view reports. Nobody calls dispatch for status updates. This is where the "20 hours saved per week" actually materialises.

Where else does this pattern work?

The stack isn't trucking-specific. Any industry where you dispatch people to locations and need documents to move alongside them fits the mould:

Field services (HVAC, plumbing, electrical). Work order comes in → AI reads it → dispatcher assigns technician → tech sees job details on phone → completes with photo → proof sent to customer automatically. Same five layers, different vocabulary.

Facilities management. Reactive and planned maintenance jobs, compliance documentation, client-visible dashboards. Every school, hospital, and office block needs this. Most run on spreadsheets and phone calls.

Last-mile delivery. Driver app + customer notifications + proof of delivery. ILG's exact pattern, just shorter delivery windows and more stops.

Home healthcare. Caregiver scheduling, patient-specific instructions, visit confirmation, family portal. Heavily regulated, which makes no-code's audit trail and permissions layer especially valuable.

Construction logistics. Equipment and materials dispatch to job sites. Multiple contractors, compliance certificates, delivery windows. Current state of the art is usually a WhatsApp group.

The common thread: these are all industries where the people doing the actual work are on their feet, not at desks. Mobile-native matters more than feature depth.

What do you actually need to pull this off?

Two things, and one won't surprise you.

First: domain expertise. Wingfield knew trucking cold. He knew what dispatchers actually do, where they waste time, and what the rate confirmation document looks like. You can't outsource this to an agency. If you don't have someone who lives and breathes the operation, the platform won't solve the right problems.

Second: a no-code builder who knows the platform. ILG hired Glide Experts. This is the bit that trips up a lot of ops teams, they assume "no-code" means "anyone can build it." Anyone can build *something*. Building something that handles edge cases, scales to 1,000+ users, and doesn't crumble when the AI misreads a PDF takes skill. You don't need a developer, but you do need someone who's built on the platform before.

The budget isn't zero, but it's not six figures either. ILG's investment was a few months of Expert time. Compare that to a traditional TMS implementation, typically $50K–$200K plus ongoing licensing, and it's not close.

Where does no-code hit its limits?

Let's be honest about what this pattern doesn't solve.

iDispatchHub works brilliantly for small carriers, the 95% of the market running fewer than 10 trucks. It doesn't replace McLeod for a 500-truck fleet. No-code platforms have performance ceilings: large data volumes slow them down, complex integrations (EDI with major brokers, for instance) require workarounds, and you're dependent on the platform's roadmap for new capabilities.

Security and compliance can also be friction points. If you're handling sensitive data, you need to verify where it lives. Glide stores data in Google Sheets by default, which isn't ideal for regulated environments. Tools with built-in data hosting and granular permissions, Stacker, for example, or a properly configured Bubble instance, become necessary at a certain scale.

And the "no developers" framing is slightly misleading. ILG hired two Glide Experts. That's still hiring technical people, they just work in a different medium. The savings come from speed and abstraction, not from zero technical cost.

Don't say / Do say

• Don't say "We need to build a custom TMS." Do say "We can model our dispatch workflow on no-code and launch in months."
• Don't say "No-code is too limited for real operations." Do say "No-code handles 90% of dispatch logic. The last 10% may need a workaround."
• Don't say "Our industry is too specialised." Do say "Our industry knowledge is the hard part. The platform handles the rest."
• Don't say "AI will replace dispatchers." Do say "AI replaces the admin. The dispatcher's judgment still matters."

Keep learning

• The full ILG case study, direct quotes, metrics, and a video walkthrough
• Building logistics software on no-code, Glide's guide to real-time visibility patterns beyond just ILG
• No-code supply chain management, more ops case studies, including ManBow (2 FTEs saved) and Champion Industries ($120K saved on QA)
• Dispatch software features and benefits, Knack's breakdown of the essential dispatch components