Case Studies

AI systems in the wild. What changed, what shipped, what stayed up.

Architectural breakdowns of recent Quellix Labs projects: what shipped, the security and governance limits, the measured outcomes, and how the systems are running now.

Quellix Intelligence management dashboard showing synthetic real estate investment metrics and property funding analytics
Real estate investment

Real estate analytics software with AI-generated dashboards.

See how Quellix built real estate analytics software with portfolio dashboards, a controlled AI analyst, and custom dashboards generated from natural language.

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Operations dashboard for a high-volume marketplace
E-commerce & marketplaces

AI agents that answer, sell, and support, for a two-sided marketplace.

A marketplace was scaling, but support was becoming the bottleneck. We added two practical AI agents: one for shoppers, one for sellers, both connected to the existing CRM without forcing a platform rewrite.

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Wheat field for Swastik Brand Aata production
FMCG & supply chain

A morning briefing run by AI agents for Madan Mohan Gradings.

Madan Mohan Gradings had sales data arriving every day, but decisions still depended on memory and instinct. Quellix delivered a 4am briefing system that turns overnight sales into forecasts, distributor priorities, and expansion signals before the morning meeting starts.

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Customer health and churn intelligence dashboard for accounting SaaS retention teams
B2B SaaS retention

Customer health and churn risk intelligence for a B2B accounting SaaS.

A B2B accounting SaaS platform needed earlier churn visibility than CRM notes and usage dashboards could provide. We designed an AI retention layer that scores account health, explains risk movement, and gives CSMs the next best action before renewal pressure arrives.

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TDS reconciliation workflow comparing compliance records and internal books
Tax compliance SaaS

AI-powered TDS reconciliation for a compliance SaaS workflow.

A tax compliance SaaS product could already show mismatches across Form 26AS, AIS, books, and return data. We designed the intelligence layer that explains why each mismatch exists, prioritizes review, recommends the next action, and drafts follow-up communication while keeping users in control.

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