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The team aggressively narrowed 16 potential use cases down to a single, high-value problem : the expensive, manual process of comparing tax computations line by line.
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Tax Systems is a leading corporate tax compliance and automation provider, serving major enterprises and accountancy firms across 20 countries, including numerous FTSE 100 organisations. Despite their market position, they operate in an industry they describe as backwards, where manual reviews and PDF-based workflows are still heavily prevalent.

The business had already invested in Generative AI research and launched AI-powered capabilities. However, like many AI-forward organisations, they faced a strategic inflection point. A structural gap existed between experimentation and execution, meaning the pathway to scale had not been fully de-risked for board approval.
They needed to move from technical validation to commercial confidence. Compounding this strategic challenge was a severe operational bottleneck. Operating in an industry they described as 'super backwards', manual reviews and PDF-based workflows were still highly common.
Senior professionals were spending expensive, repetitive hours comparing tax computations against financial accounts line by line, a process that was notoriously difficult to scale. Furthermore, with sixteen potential AI use cases identified across their tax workflows, the leadership team lacked a clear prioritisation logic to determine which initiative would actually drive measurable value and secure investment.
To move from ambiguity to evidence, Colibri Digital deployed a disciplined, three-week Agentic AI discovery model supported by the AWS Rapid GenAI programme.
The process began by aggressively narrowing down 16 potential AI use cases to just three, and ultimately focusing on one core proof-of-concept targeting the manual corporate tax review process. Colibri built an agentic prototype using AWS services, leveraging enterprise-grade foundation models such as Nova. These agent workflows were then integrated directly into Tax Systems' cloud platform via APIs.
Rather than deploying an autonomous system, the application was designed with strict human-in-the-loop guardrails to augment professional judgement rather than replace it. The AI processes both structured and unstructured data to reconcile tax computations, instantly surfacing anomalies for a human professional to review. Think of it as a highly diligent digital auditor highlighting discrepancies, allowing the senior tax professional to focus their expertise solely on the anomalies rather than the tedious line-by-line comparison.
Within just three weeks, Tax Systems shifted from broad AI exploration to possessing a validated, production-minded prototype and a credible board-ready narrative.
Reduced to within 6-second lead time
Faster predictions on customers' needs and capturing new opportunities
+ 20 headcount in the data team


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