Real outcomes from real engagements. Client names are anonymised at their request — the numbers are not.
A Fortune 500 manufacturer needed a single intelligent view across 11 million customers and 1.6 million SKUs — spanning 150 countries, multiple CRM instances, and thousands of sales leaders with no unified analytics.
We designed and deployed a global sales intelligence platform on Azure ML and Snowflake — combining propensity-to-buy models, opportunity lifecycle analysis, and personalised coaching recommendations delivered through Power BI and Microsoft Teams.
A global B2B services company was managing 250,000 renewable service contracts reactively — teams only became aware of at-risk accounts when customers were already disengaging. By then, the retention window had often already closed.
We built predictive churn and renewal models, an early-warning scoring system, and an Azure OpenAI-powered retention assistant — shifting the entire organisation to proactive, evidence-based retention management.
A global industrial services company was carrying high days sales outstanding driven by disputed invoices, payment delays, and an AR team working from manual spreadsheets with no systematic prioritisation.
We deployed payment-risk models, automated pre-dispatch invoice conformity checks, and Power Platform early-warning dashboards — delivering $1.8M in impact in the first quarter alone.
Following a major merger, a Fortune 500 enterprise inherited a fragmented customer data estate of 1.7 billion address entries across multiple legacy systems — with no reliable way to identify real customer relationships for cross-sell, account management, or global analytics.
We built a multi-agent AI pipeline combining deep learning deduplication, NLP translation, geolocation enrichment, and external validation — establishing 11 million clean golden customer records.
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