Digital OOH Inventory Platform for Mobile Advertising in Mexico

What did Rodada need from this AI project?

Rodada needed to convert Mexico's mobile Out-of-Home (OOH) advertising market from a WhatsApp-and-spreadsheet operation into a structured B2B platform where advertisers could plan campaigns and fleet owners could sell inventory without a phone call. The brief was less about model sophistication and more about coordination: turn moving trucks into bookable, trackable advertising assets, and replace dozens of manual handoffs with a single workflow.

The market context made the problem unusual. Truck fleets in Mexico move past millions of impressions a day, but the entire industry priced and sold inventory through informal threads — no standardized rate cards, no shared availability calendars, no campaign reporting beyond photos texted back to the advertiser. Advertisers had no way to plan a campaign at scale; fleet owners had no structured way to monetize a moving asset. Off-the-shelf ad-tech platforms assumed digital screens with programmatic feeds, not physical trucks with route patterns and human operators. The bottleneck was coordination, not algorithms — and the right intervention was a purpose-built operational platform, with AI used selectively where it actually moved the needle.

How did Clearframe Labs approach the build?

Phase 1: Discovery and operational mapping

We started by mapping every step in the existing market — how a brand briefed a campaign, how a fleet owner quoted, how routes got assigned, how proof-of-delivery flowed back. The output was a written operations spec that defined every role (advertiser, operator, manager, fleet owner, admin), every state transition in a campaign's lifecycle, and the points where coordination broke down. That spec, not a feature list, drove the architecture.

Phase 2: Inventory management as the platform foundation

We built the platform around treating each truck as a digital asset with its own profile: availability calendar, route classification, pricing band, campaign history, and revenue tracking. Fleet owners onboard through a structured flow that captures vehicle data, defines availability windows, and generates the digital inventory record advertisers actually book against. This single change — turning a phone-quoted truck into a typed, queryable asset — is what made everything downstream possible.

Phase 3: Campaign workflow engine

We developed a full lifecycle workflow covering request, review, approval, assignment, execution, and close-out. The engine handles automated notifications, internal state machines, role-based permissions across operators, managers, fleet owners, and Rodada admins, and per-truck revenue tracking on every campaign. Each role sees a tailored view: advertisers see campaign status and proof of delivery; fleet owners see their assignments and earnings; Rodada operators see the orchestration layer.

Phase 4: Revenue tracking and intentional scope discipline

We built financial visibility into the platform from day one — per truck, per campaign, per fleet — while deliberately deferring payment automation, Stripe integration, and other features that would have widened the build without solving the actual bottleneck. The architecture cleanly separates advertiser billing from fleet compensation, which is what gives Rodada margin control as the network scales. The team picked operational excellence first, financial automation second — a scoping decision that kept the system lean and shipped on the timeline the business needed.

What were the results?

Rodada moved from a fully manual operation to a digitized platform managing the complete OOH campaign lifecycle across Mexico, with the coordination overhead that defined the old market replaced by a single operational system.

• Campaign coordination: fully digitized — every campaign runs through one workflow from brief to close-out
• Pricing standardization: achieved across the platform, replacing per-thread negotiation
• Fleet onboarding: structured into a repeatable flow with typed inventory data
• Manual processes eliminated: end-to-end, from quoting to revenue reconciliation

For advertisers, this means structured pricing, simplified planning, and predictable budgeting. For fleet owners, a new revenue stream with professional contracts and clear assignment flow. For Rodada, the platform is now the infrastructure layer of a market that never had one — and the network gets more valuable with every truck and brand added.

What technical decisions made this work?

• AI used selectively, not by default: in an under-digitized market, the highest-leverage move was structuring coordination — calendars, role-based workflows, typed inventory — not training a model. We deliberately scoped AI to where it added measurable value (pricing intelligence, route classification) and built classical software for everything else.
• Each truck modeled as a digital asset: availability, routes, pricing band, and history live as structured data rather than free text. This is the single decision that unlocked standardized pricing and bookable inventory; everything else followed.
• Role-based workflows over a generic CRUD app: the platform serves advertisers, operators, managers, fleet owners, and admins with role-specific views and permissions on top of a shared workflow engine. This eliminated the role confusion that had fragmented the old market.
• Financial visibility before payment automation: we built per-truck, per-campaign revenue tracking from day one, but deferred Stripe/payment integration. That sequencing gave Rodada margin control and reporting without overengineering for a feature the early market did not yet need.
• State machines as the workflow primitive: every campaign moves through explicit, named states with role-gated transitions. This made debugging, audit, and customer support tractable in a way ad-hoc workflow code never would have been.

Lessons for teams considering similar projects

• In under-digitized industries, the biggest value usually comes from structuring coordination, not adding AI. Operational clarity alone can transform a market — pick the AI scope after the workflows are clean.
• Treating physical assets as digital inventory unlocks pricing standardization, marketplace dynamics, and scalable economics that manual coordination never could. The data model is the product.
• Scope early-stage platforms for operational excellence before financial automation. Payment integration and billing automation are easy to add once the workflow is right and impossible to remove cleanly when the workflow is not.
• Role-based workflows that serve every stakeholder from one system are how you eliminate the fragmentation that holds traditional industries back. One platform, many views.
• Build financial visibility from day one even if you defer payment automation. Margin control and reporting are foundational; payment rails are not.

What's next

With the operational layer in place, Rodada is positioned to layer in selective AI — route-based pricing intelligence, campaign performance prediction, and fleet network optimization — on top of a structured data foundation that did not exist in the market before.