Case Studies | FinTech

An AI prospecting agent for global payroll qualification, built on AWS

Agentic Productivity Programme

About

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

Challenge

The challenge had three focus areas.

A heavy manual research burden

The business development team spent significant time researching whether each target operated payroll across five or more countries and had the headcount to fit the client's profile. That effort scaled linearly with the size of the target list, capping how many accounts could realistically be assessed.

Inconsistent qualification

Manual research done account by account produces uneven results, different people weigh different signals differently. The client needed qualification decisions to be consistent, evidence-backed and repeatable so the team could trust and act on them.

Signals scattered across many sources

The evidence that matters, country presence, office locations, job postings, international headcount, legal entity indicators and payroll technology signals, is spread across company websites, careers pages, news and enrichment databases. Pulling that together by hand for every company was the bottleneck.

Solution

A business development representative gives the agent a company name and website, or uploads an exported list. Through Kiro, the agent orchestrates a research and qualification workflow: the client crawls the company website, extracts key pages and searches the wider web for external coverage, producing a cited research report, while a third-party provider.io enriches the company record with employee count, technology stack, funding stage, locations and industry.

5+

Countries with payroll operations the agent screens each account for

3

Qualification tiers: Qualified, Needs Review, Not Fit, each with confidence

100-300+

Per-country headcount signals the agent evaluates as evidence

By the numbers:

  • 5+ - Countries with payroll operations the agent screens each account for
  • 3 - Qualification tiers: Qualified, Needs Review, Not Fit, each with confidence
  • 100-300+ - Per-country headcount signals the agent evaluates as evidence
Changes

Cloud Combinator delivered a working MVP that meets its success criteria: automated research through the client, enrichment through a third-party provider.io, and a structured, consistent qualification output per company generated by Amazon Bedrock, tested against a sample set of real companies provided by the client. The result is a repeatable workflow that reduces manual research effort compared with the team's previous approach.

  • Automated research — The client crawls the company site, extracts key pages and searches external sources, returning a cited report rather than a manual browse.
  • Company enrichment — A third-party provider.io supplements the research with employee count, technology stack, funding stage, HQ and locations, and industry.
  • AI qualification — Amazon Bedrock with Claude scores each company against the client's ideal customer profile and returns a status, confidence level, evidence and a suggested outreach angle.
  • Batch processing — The agent runs an exported company list through the same workflow and returns a summary table, highlighting which accounts to prioritise and which need a manual review.
  • Handover — A live demonstration workshop and handover so the client's business development and technical stakeholders can run and refine the agent themselves.

With a working MVP proven against real accounts, the client has a foundation it can extend, tuning the qualification logic, widening the signals and, in future phases, connecting the agent more deeply into its outbound workflow.

AWS Stack

Amazon Bedrock

With Claude for reasoning over research findings and generating structured qualification outputs.

Kiro

For the business-development-facing workspace and workflow orchestration on Amazon-managed infrastructure.

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