An AI-native Transportation Management System for small and medium US trucking carriers. Seven cooperating agents automate dispatching, load sourcing, rate negotiation, driver communication, tracking, and back-office operations — replacing $240,000/year in dispatch costs for a 10-truck carrier.
The US trucking industry generates approximately $900 billion in annual revenue. The operational backbone — dispatching — has not meaningfully changed in 30 years. Carriers are not just open to automation; with -2.3% average operating margins in 2024, they need it to survive.
"The value pool isn't the software price. It's the $240K/year a 10-truck carrier is spending on dispatch services that can be reduced by 85%."
The TMS market is crowded — DAT, McLeod, Tailwind, Samsara, Motive, AscendTMS, Rose Rocket, and dozens more. So why is the dispatch workflow still manual for 99% of small carriers? Every incumbent is structurally disincentivised to solve it.
| Incumbent | Why they haven't solved it |
|---|---|
| McLeod / Tailwind (legacy TMS) | Per-seat pricing model. Automating dispatcher tasks destroys their revenue. Structurally disincentivised to build this. |
| Samsara / Motive (telematics) | Monetise hardware and safety compliance, not back-office labour. Different cost centre entirely. |
| DAT / Truckstop (load boards) | Monetise load discovery, not dispatch workflow. No incentive to replace the dispatcher. |
| Uber Freight / Convoy model | Digital brokerage — takes the margin, doesn't reduce the carrier's cost. Convoy shut in 2023. |
| New AI entrants (HappyRobot, FleetWorks) | Broker-side or dual-sided focus. Carrier workflow is under-served by all current entrants. |
The structural gap: no product owned the carrier's financial system-of-record while also automating their daily dispatch workflow. That's the wedge.
A human dispatcher makes five sequential decisions every time a load comes in. We mapped every branch, every data input, and every possible outcome — then designed a specialist agent for each decision phase.
Each agent has a single, narrow responsibility. No agent calls another directly — they emit events and subscribe to events. This means any agent can be replaced, upgraded, or disabled without touching the others.
Three principles govern every agent in the system — chosen to eliminate hallucination risk and make outputs auditable in a regulated industry.
"The goal is not to replace the dispatcher. The goal is to make one dispatcher as effective as three."
Each technical choice was made to eliminate unnecessary cost and complexity at MVP scale while keeping the path clear to 1,000+ customers. Zero-overhead infrastructure. No Redis dependency. No separate auth provider.
| Layer | Technology | Why this choice |
|---|---|---|
| Frontend | Next.js 14 | Server Components for fast initial load; Supabase realtime for instant board updates without polling |
| Backend | NestJS 10 | Module-per-agent architecture; class-validator for DTO safety; scales to microservices if needed |
| Database | Supabase (PostgreSQL) | Multi-tenant RLS, realtime subscriptions, and Auth in one service — no separate auth provider needed |
| Jobs | Inngest | 50K runs/month free; step-level retry; dev server for local debugging; no Redis dependency at MVP |
| Inbound email | Cloudflare Email Routing | Free, unlimited, unblockable; Worker forwards to NestJS webhook with shared secret |
| Outbound email | Resend | 3K/month free; developer-first API; clean TypeScript SDK |
| AI agents | Claude API (Anthropic) | Tool-calling for structured outputs; claude-haiku-4-5 for high-volume tasks (scoring, ranking); Sonnet for complex parsing |
| Messaging | Twilio (WhatsApp + SMS) | WhatsApp Business API with SMS fallback; TCPA-compliant consent and disclosure built in from day one |
| Caching | Supabase cache table | TTL caching via expires_at field; nightly Inngest cron cleans stale rows; zero extra infrastructure |
The underlying models commoditize any thin wrapper. The durable competitive advantage is built at two layers — both of which compound over time and are impossible to replicate without operating history.
We believe clients deserve an honest view of the challenges in any AI build. Here is what we monitor closely in this project — and in any similar engagement.
| Risk | How we manage it |
|---|---|
| AI gross margin compression | Voice and inference costs are metered usage, not bundled into flat pricing. Monitored per-customer monthly with alerting thresholds. |
| Regulatory exposure (FMCSA, TCPA) | Legal review of architecture before launch. Consent capture and AI disclosure built into the comms layer from day one — not retrofitted. |
| Thin-wrapper commoditisation | Proprietary data capture and system-of-record lock-in are first-class product goals, not afterthoughts. Architecture enforces this from the first line of code. |
| Trust and adoption | Human-in-the-loop by default. Autonomy is earned with outcome data over 6–12 months, not assumed. Dispatcher remains in control throughout v1.0. |
The AI Dispatch Platform is not just a product story. It shows how Devsphinx approaches a build — from market analysis and competitive mapping through architectural decisions to production engineering. The same thinking applies to your engagement.