Upgalax
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Use cases

Real problems, shipped solutions.

Each build below opened with a specific problem and ended with a measurable outcome. No demos — production systems.

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D2C E-commerce

Checkout agent that recovered failed payments

Upgalax built a payment-recovery agent for an Indian e-commerce brand, reducing failed-payment cart abandonment by 32%.

The problem
A D2C brand was losing a third of ready-to-buy customers to failed payments and had no way to recover them in time.
What we built
An agentic checkout assistant that detects a failed payment, reaches out on WhatsApp with a retry link, offers an alternate method, and confirms the order — all without human involvement.

Outcome

Reduced failed-payment cart abandonment by 32% in the first 60 days.

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SaaS

Support agent that deflects 60% of tickets

Upgalax deployed a customer-support AI agent for a B2B SaaS product, deflecting 60% of inbound tickets while raising satisfaction.

The problem
A growing SaaS team was drowning in repetitive support tickets and could not scale headcount fast enough to keep response times acceptable.
What we built
A custom AI agent grounded in the product docs and help center, able to answer questions, walk users through fixes, and escalate to a human with full context when needed.

Outcome

Deflected 60% of inbound tickets and lifted CSAT by 11 points in one quarter.

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Fintech

MVP that reached paying users in eight weeks

Upgalax designed and shipped a fintech MVP that reached its first paying users eight weeks after the discovery call.

The problem
A founder had a validated idea and early interest but no product, and needed to reach real paying users before the funding window closed.
What we built
A production-grade MVP focused on one core value loop — onboarding, the primary transaction, and a dashboard — with auth, payments, and analytics built in from day one.

Outcome

First paying users in week 8; seed round closed three months later.

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Logistics

Agentic workflow that runs operations overnight

Upgalax built an agentic operations workflow for a logistics company, automating 40,000+ routine tasks per month.

The problem
An operations team spent hours every night reconciling shipments, updating systems, and flagging exceptions by hand.
What we built
An agentic system that pulls data from three systems, reconciles records, updates the source of truth, flags exceptions for a human, and posts a morning summary — running unsupervised overnight.

Outcome

Automates 40,000+ routine tasks per month with a 99.2% accuracy rate.

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