Apex Lab

Arlos

Scaling Lesson Content with AI

Arlos is an AI-powered education platform built for schools, giving every student a personal AI tutor and teachers a dashboard that automates the admin work. Our challenge was helping Arlos generate a library of custom, on-brand lesson visuals fast enough to hit a hard launch deadline, without adding headcount to a small founding team.

Skills

  • Cloud Solutions
  • Amazon Bedrock
  • Python
  • AWS Lambda
  • DynamoDB
  • CloudWatch
  • Amazon S3
  • Amazon Step Functions
  • Flux2

Industry

  • EdTech
online school

The Mission

Arlos needed 100 to 140 interactive lesson intros per subject per year, each with custom artwork in the platform's own visual style. Producing that volume by hand wasn't realistic for a small team, and it stood between Arlos and its launch.

At the same time, Arlos wanted broader, more flexible access to model infrastructure for the AI tutor at the heart of the platform, one that needed to sound encouraging and age-appropriate for students aged 10 to 14, not just technically correct.

The Solution

Migrating the tutor to AWS Bedrock

We evaluated Claude models on Bedrock against Arlos's own tutor and classifier prompts, testing for reasoning quality rather than just wording match. Claude Sonnet came out ahead, demonstrating correct intent-matching on real tutoring scenarios, and was selected to power the live tutor.

Teaching a model Arlos's visual style

We fine-tuned a Flux2 image model with LoRA on Fal.ai, training it on real lesson artwork supplied by Arlos so generated visuals match the platform's established look instead of generic AI art.

Building a quality gate, not a bottleneck

Manual art review doesn't scale to hundreds of lessons. We built an automated quality gate checking style similarity, color palette, transparency, and embedded text on every generated image, so only on-brand visuals move forward without a human reviewing each one.

The Outcome

The quality gate hit near-perfect accuracy on style-matching, with a 0% false-accept rate, screening generated lesson art automatically instead of relying on manual review. Arlos's tutor now runs on Claude Sonnet via Bedrock, and the LoRA model was trained and validated against real brand assets, giving the team a repeatable pipeline for lesson visuals as they head toward their September launch.

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