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Confidential (SEO platform) · Marketing technology

AI-powered SEO and content platform.

Client project, name changed on request

Category

SaaS

Year

2024-2025

Capabilities

AI, Cloud & Automation, Full-Stack

Industry

Marketing technology

Rankloom on a laptop
Rankloom on a phone

The problem

SEO teams switched between five different tools just to get from keyword discovery to tracking, wasting hours and risking inconsistent data. Most tools also lean on stale keyword and ranking data, so teams react slowly to search trends.

Our approach

One platform: an AI content engine on OpenRouter wired to live DataForSEO and Google Maps data, with Trigger.dev running bulk jobs and caching in the background.

Inside the product

More than one screen.

The product name and data have been changed at the client's request; these screens recreate Rankloom as it was built.

Rankloom: Keyword research on a laptop

Keyword research

Live volume, difficulty and intent, with local insights from Google Maps.

Rankloom: Content assistant on a laptop

Content assistant

Batch article generation as a resumable job, with an SEO score per draft.

Rankloom: Rank tracking on a laptop

Rank tracking

Daily positions, movers and the background jobs that refresh them.

On the phone

Rankloom: Home on a phone

Home

Rankloom: Keyword research on a phone

Keyword research

Rankloom: Content assistant on a phone

Content assistant

Rankloom: Rank tracking on a phone

Rank tracking

How it is built

Architecture

Next.js App Router with server components for data-heavy views, Supabase and PostgreSQL for storage, and Trigger.dev for durable background jobs. Generation is proxied through OpenRouter so models swap without touching product code.

Key features

AI content generation with multi-locale support
Real-time keyword and rank tracking
Location-based insights via Google Maps API
Bulk processing pipelines with caching
Stripe-backed subscription billing

Challenges & solutions

Where it got hard.

Challenge

Keyword API costs scaled linearly with users and quickly became the largest line item.

Solution

Introduced request batching and a caching layer keyed by keyword and locale, cutting tracking spend in half without reducing data freshness.

Challenge

Bulk content jobs exceeded serverless execution limits and failed midway.

Solution

Moved generation onto Trigger.dev as durable, resumable jobs that checkpoint progress and retry individual items rather than the whole batch.

Results

What shipping it changed.

3xlower cost per article through automated AI workflows.
30articles in 10 minutes, work that previously took hours.
50%lower keyword tracking cost, $0.09 down from $0.18.

Stack

  • Frontend

    • Next.js
    • TypeScript
  • Styling

    • TailwindCSS
    • ShadCN
  • Backend

    • Supabase
    • PostgreSQL
    • Trigger.dev
  • APIs & AI

    • OpenRouter
    • DataForSEO
    • Google Maps API
  • Payments

    • Stripe
  • Deployment

    • AWS EC2
    • AWS Lambda
  • Analytics

    • PostHog

Let's talk

We can usually tell within one call whether the approach above transfers to your problem, and what would need to change.