Case Studies | SaaS B2B

Turning market intelligence into an AI-native product line

Joint AI Solution Scoping

About

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

Challenge

The scoping work centred on four focus areas that any AI motion on the client's data had to satisfy before a build could be justified.

Protecting the data asset

The first focus was ensuring that the underlying intelligence is never absorbed into a model. Fine-tuning bakes data into model weights that cannot be perfectly un-trained, so an architecture was needed where the data is used but never retained inside the AI itself.

Preserving the customer channel

The second focus was channel conflict. Any joint go-to-market had to avoid Cloud Combinator approaching the client's existing customers as a competing channel, which meant a disclosure and vetting process had to be designed in from the start.

Controlling data on exit

The third focus was retention risk: what persists if a customer cancels. The model had to guarantee that access can be revoked and derived data destroyed cleanly, with no residual copies embedded anywhere.

Finding the right first use cases

The final focus was commercial fit. The engagement had to identify concrete, buyer-ready use cases that play to the client's existing data strengths rather than requiring new data acquisition, so a first pilot could land quickly.

Solution

The recommended design is a retrieval-augmented generation (RAG) solution built on AWS. Rather than training the client's content into a model, the AI retrieves the relevant slice of data from the client's own storage at query time, uses a large language model on Amazon Bedrock to generate a grounded answer, and then discards the retrieved content. Nothing is baked in, so the data asset is used without ever being absorbed.

55

AWS-funded sales-qualified leads targeted per cycle (projected)

3

Tiered AI use cases mapped to the client's data

£200k–

Net-new ARR per cycle (projected, at 10–15% conversion)

By the numbers:

  • 55 - AWS-funded sales-qualified leads targeted per cycle (projected)
  • 3 - Tiered AI use cases mapped to the client's data
  • £200k– - Net-new ARR per cycle (projected, at 10–15% conversion)
Changes

The scoping engagement delivered exactly what it set out to: a defensible recommended architecture, a risk position the client's legal team can stand behind, and a costed commercial motion ready to take into a decision. The figures below are indicative projections from the scoping model, not delivered results.

  • Recommended architecture — The RAG joint solution was identified as the lowest-risk path, using the client's data at query time only and keeping the data asset out of any model.
  • Risk mitigation matrix — Each of the three core risks, model training, channel conflict and data retention, was matched to a concrete mitigation, including a pre-check process that vets every prospect against the client's customer base.
  • Tiered use cases — Three buyer-ready use cases were scoped: retail brand protection and fraud detection, a retail analytics and model layer, and a bench of future angles such as demand forecasting and ESG reporting.
  • Commercial model — A joint go-to-market was structured around AWS-funded lead generation, revenue share, and new procurement via AWS Data Exchange and AWS Marketplace, with incentives aligned so Cloud Combinator only wins when the client's AI line wins.
  • Handover — The work was packaged for a 60-minute working session, with three use cases prepared to map against the client's data live and stress-test fit before committing to a build.

With the architecture, risk position and commercial model now on the table, the path forward is a joint working session and a BOX application timeline, with the potential to turn the client's trusted content into an AWS-native, AI-driven product line that grows cycle on cycle.

AWS Stack

Amazon Bedrock

For the large language model that generates grounded answers from retrieved data at query time.

Amazon S3

For secure storage of the client's data slices retrieved by the solution.

AWS Data Exchange

For listing the client's data products so customers can procure using existing AWS commitment spend.

AWS Marketplace

For one-click deployment and AWS-billed procurement of the joint solution.

AWS partner

Funding programmes for underwriting lead generation and end-customer proof-of-concept deployments.

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