Case Studies | SaaS B2B

A generative AI foundation for the creative process

Generative AI Creative Workflow Foundation

About

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The project focused on three areas.

The right platform

The client needed to validate whether Amazon Bedrock or Amazon SageMaker was the better fit for deploying and running their generative models, rather than committing blind.

Faster scaling

Startup time for new model instances was a bottleneck, taking around 25 minutes, so the platform needed to scale up more quickly to keep the creative workflow responsive.

An automated pipeline

Beyond an one-off deployment, the client needed a backend pipeline that could run the models and handle infrequent retraining as their needs evolved.

Solution

Generative models are deployed on AWS using Amazon SageMaker, with Amazon Bedrock assessed as an alternative platform. The models take individual parameters and produce creative outputs, synthesising briefs, generating ideas and producing high-resolution artwork.

25 min

Instance startup time identified as the bottleneck to reduce

Bedrock +

Platforms evaluated to find the best fit

~$1,126/mo

Estimated running cost for the workload (projected)

By the numbers:

  • 25 min - Instance startup time identified as the bottleneck to reduce
  • Bedrock + - Platforms evaluated to find the best fit
  • ~$1,126/mo - Estimated running cost for the workload (projected)
Changes

The engagement delivered a working generative AI foundation in the client's AWS environment and validated the platform choice against the agreed success criteria, demonstrating that AWS is a suitable base for continued model development. The backend pipeline gives them a path to scale and retrain as demand grows.

  • Validated platform — Amazon SageMaker deployment with Amazon Bedrock assessed as an alternative, confirming AWS as a suitable base for the client's models.
  • Scalable foundation — A backend pipeline built to support faster scaling, addressing the previous startup-time bottleneck.
  • Automated pipeline — Lambda, SQS and CloudWatch orchestration with an infrequent retraining process to keep models current.
  • Creative acceleration — Models that synthesise briefs, generate ideas and produce high-resolution artwork from individual parameters.
  • Handover — Knowledge transfer and an immersion session so the client's team could continue model development.

With a validated platform and a scalable pipeline in place, the client is positioned to expand their generative AI capabilities and refine the models further, with a Well-Architected review recommended to harden the foundation as usage grows. Running costs are an estimate that will depend on actual usage.

AWS Stack

Amazon SageMaker

For deploying and running the generative models, with Amazon Bedrock evaluated as an alternative.

AWS Lambda

For pipeline orchestration and processing.

Amazon SQS

For decoupling and queuing workload tasks.

Amazon CloudWatch

For monitoring and observability of the pipeline.

Amazon S3

For storage of inputs and generated outputs.

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