Case Studies | SaaS B2B

Putting generative AI to work in media

AI Webchat and Analytics on Bedrock

About

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

Challenge

The challenge had three focus areas, each about turning an AI ambition into working capability inside a busy media business.

Making AI useful against real data

Generic language models are not enough on their own. Amazon Bedrock had to be set up to work with the client's historical data, with prompts engineered to cover the communication and reporting needs of the business.

Delivering insight in real time

Clients wanted relevant, current insight on ROI and campaign performance, not a monthly retrospective. The solution needed to autonomously surface insights on key performance indicators to both the internal team and clients.

Freeing up staff time

Much of the reporting and workflow effort was manual. The goal was intelligent automation that could take on that repetitive work, targeting a meaningful annual saving in staff time.

Solution

Cloud Combinator ran the engagement through its Cloud Accelerator Program, a structured path that takes a client from idea to a working solution in their own AWS account while upskilling their team along the way.

10-15%

Targeted annual saving in staff time (projected)

Real-time

Client insight on ROI and campaign performance

5-phase

Cloud Accelerator delivery, from discovery to build

By the numbers:

  • 10-15% - Targeted annual saving in staff time (projected)
  • Real-time - Client insight on ROI and campaign performance
  • 5-phase - Cloud Accelerator delivery, from discovery to build
Changes

The engagement delivered a working Amazon Bedrock pipeline in the client's own AWS account and validated Bedrock as a suitable platform to continue developing the client model, with the internal tech lead signed off as competent to carry the work forward.

  • An AI back end on Bedrock — A working pipeline that sets up Amazon Bedrock against historical data and provides the back end for AI webchat.
  • Autonomous KPI insight — The system is designed to deliver insights on key performance indicators to both the team and clients, supporting real-time reporting on ROI and campaign performance.
  • Platform validated — The project confirmed Amazon Bedrock as a suitable environment to continue developing the client model.
  • Team upskilled — A targeted immersion day and hands-on labs left the client's internal tech lead ready to continue model development.
  • Handover — Delivery took place in the client's AWS account with acceptance signed off against the original scope and the internal tech lead's competence confirmed.

With Amazon Bedrock proven against their own data and their team upskilled, the client is positioned to extend the pipeline across more of its reporting and client-service workflows, deepening the real-time insight it can offer clients.

AWS Stack

Amazon Bedrock

For generative AI inference over historical data, powering webchat and insight generation.

AWS Lambda

For the serverless functions that invoke the Bedrock models and orchestrate the pipeline.

Amazon API Gateway

For secure data transfer in and out of the solution.

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