A fully custom enterprise CRM with AI lead scoring, real-time pipeline management, and multi-region deployment — built to scale a distributed sales force of 200+ agents.
The client — an enterprise services company with operations in India, UAE, and Southeast Asia — had 200+ sales agents using a patchwork of tools: Salesforce for some teams, spreadsheets for others, WhatsApp for follow-ups, and a legacy CRM for reporting. Data was siloed, lead attribution was broken, and management had no real-time visibility into pipeline health.
They needed one unified platform — custom to their sales process, accessible to all regions, with AI that could score and prioritise leads automatically, and reporting their CFO could trust.
We built a multi-tenant CRM from the ground up — designed around their exact sales workflow, not a generic template. The AI lead scoring engine processes 40+ behavioural signals to rank leads automatically. A real-time collaboration layer lets regional managers and global leadership see the same live pipeline data simultaneously.
A Next.js frontend with server-side rendering for fast initial loads. Python microservices handle the AI scoring pipeline. Real-time updates run over WebSockets with Redis pub/sub. The entire system is deployed on AWS with multi-region replication ensuring each regional team gets sub-50ms response times from local infrastructure.
Six months post-launch, lead conversion tripled. The AI scoring meant agents focused on the right leads instead of the noisiest ones. The average deal cycle shortened by 45% — from lead to close — because automated follow-ups eliminated the gaps in manual outreach. Regional managers reported saving 10+ hours per week previously spent on pipeline reporting.
Before XtrazCon, our managers spent their Mondays pulling pipeline numbers from five different systems. Now they open one dashboard and the entire picture is there — live. The AI scoring alone paid for the project in the first quarter.