Turning scattered partner and FI knowledge into an AI intelligence layer

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

Discovery surfaced three distinct focus areas, each a candidate for the funded build. They share a common theme, applying AI internally to relieve a specific operational pressure, but they address it from different angles.

Rather than attempt all three use cases at once, the engagement is deliberately shaped to commit to one substantive, working MVP and treat the other two as roadmap items. It runs in three phases.

The scoping engagement gives the client a clear, defensible path from ambition to build. Three genuine use cases are defined with objectives and success criteria, an AWS-native architecture is set for each, and a recommendation to focus the funded engagement on a single MVP is agreed, with the remaining two use cases kept on the roadmap.

• A shared decision framework — Leadership and delivery teams have one view of the options, the trade-offs, and the natural overlaps between them, so the use-case choice can be made quickly and with conviction.
• An AWS-native architecture direction — Each use case has a concrete pattern anchored on Amazon Bedrock Agents and Knowledge Bases, so the build starts from a validated design rather than a blank page.
• A route to evidence-based decisions — The lead use case is designed to let leadership answer questions such as whether to invest further in a given partner with evidence drawn from the tool rather than instinct (projected outcome, to be proven in the build phase).
• A roadmap beyond the MVP — The two use cases not selected remain scoped and ready to pick up as natural next steps once the first capability is proven.
• Handover — The engagement closes with a written recap, the open questions to resolve in the deep-dive, and the material the client needs to gather, so momentum carries straight into the Statement of Work.

With the direction set, the client moves into use-case selection and a Statement of Work, ready to build a first internal AI capability that is no longer a broad ambition but a scoped, AWS-native tool designed to grow into production.