Case studies from engagements across industrial, financial services, and logistics environments. Each case is shared with permission and presented for illustrative purposes.
Heavy industrial
Where unplanned downtime translates directly into commercial loss, and AI investment needs a defensible business case.
By ranking failures by business cost rather than frequency, AI work focused on the 20% of assets producing 80% of downtime cost. Unplanned downtime on those assets dropped roughly 30% in the first year.
Read case studyCommercial finance
Where data quality, regulatory traceability, and analyst time directly shape commercial throughput.
A six-week readiness assessment showed roughly 40% of incoming documents needed pre-processing before any classifier could perform reliably. Building that data layer first turned a brittle proposal into a 4× throughput gain for the underwriting team.
Read case studyLogistics
Where ticket volume, response time, and consistency drive service quality — and where AI features only land if they live inside the agent's workflow.
Earlier chatbot attempts had failed because agents wouldn't switch tools. The new copilot was built directly into the existing ticketing UI — agents now handle around 60% more tickets per shift, with response quality holding through the rollout.
Read case studyMore on request
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