About UppuTech

UppuTech AB is run by me, Arun Uppugunduri. I'm a data engineer and AI architect based in Sundbyberg, Stockholm.

In recent years I have built AI agents, data pipelines and integrations that run in production in regulated environments. I have worked closely with leadership teams, taken systems through security review and handed them over so others can run them.

Now I bring the same way of working to more companies: find what costs the most, remove it, and show the result in hours and money. Small, clear projects first. Bigger things once the first ones have paid for themselves.

Experience from

Klarna, Blocket, Netlight, GoClimate

Education

MSc in Industrial Engineering and Management with a specialisation in machine learning and AI, and a BSc in Computer Science. Linköping University.

Technology

Python, SQL, React, Node.js, AWS, Snowflake, Kafka, PostgreSQL, LLMs and AI agents, RAG, MCP

Selected past work

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Tenders and RFPs

Before: Every tender was written by hand by specialists in product, legal and sales.
After: An AI assistant drafts answers with sources from past tenders and documents.
Result: 60 to 70 percent of standard answers written automatically.

Fintech company, in production since 2024

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Lead qualification

Before: New leads were assessed by hand using an expensive external analytics tool.
After: An AI agent scores every lead the moment the form is sent and posts it to the sales channel.
Result: The tool was cancelled. A six-figure saving per year.

Fintech company, passed security review with no findings

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Sign-up flow

Before: Applications dropped out of sign-up and nobody could see where or why.
After: A new flow, more payment providers and pricing rules per market.
Result: More than a third higher conversion.

Payments company, several markets

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Data quality

Before: Messages from partners silently disappeared inside an internal system.
After: Four data sources joined into one view, errors found and fixed.
Result: Verified partner channels more than doubled.

Tens of thousands of records, several teams

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Cloud costs

Before: A regulated data pipeline with high running costs and audit risk.
After: Redesigned to meet banking data requirements.
Result: Sharply lower AWS costs and a pipeline that passes audit.

Bank-regulated environment

What takes the most time in your business today?

Book 30 minutes. You talk directly to Arun Uppugunduri, who also does the work, and get an honest answer on what can be automated, what it would cost and what it would save.

Book a free 30-minute call