India’s new labour codes, which began taking effect in late 2025, have noticeably changed how employers handle payroll. Media reports highlight higher wage-related costs, stricter provident-fund rules, and more detailed documentation requirements. For many small and mid-sized companies, this has made payroll slower, more error-prone, and significantly harder to manage without automation.
India is not alone in this transition. Across major economies, governments are tightening payroll, tax, and reporting requirements as they adapt to new labour conditions and digital oversight tools. For companies that operate across borders, this creates a shared challenge: staying compliant in a world where rules change faster than traditional systems can handle. As a result, firms are actively looking for tools that update tax logic automatically, prevent mistakes, and provide a clear audit trail when regulations shift.
This gap between rising compliance demands and limited in-house technical capacity is exactly what Unityware AI was created to address. As the core data-engineering lead for the platform, Shanmuka Siva Varma Chekuri designed the data architecture that allows Unityware AI to run payroll across the world. His work includes building real-time data pipelines, region-specific tax models, and other components that help the system operate consistently across different jurisdictions. This architecture helped the tool achieve faster payroll cycles and avoid compliance errors. For Indian businesses adjusting to the new labour codes, this type of system shows what compliant payroll can look like when the underlying data is structured correctly and the rules update automatically. Instead of recalculating contributions by hand or interpreting regulatory changes on their own, companies can rely on a platform designed for fast updates, clear traceability, and region-specific accuracy.
Across many countries, payroll rules are becoming more complicated each year. This creates a real challenge for companies (especially smaller ones) because they must keep up with changing tax laws, reporting formats, and regional practices. Unityware AI was created to make this work easier. It is an AI-powered payroll and compliance platform designed to help small and mid-sized businesses run payroll correctly in multiple countries, calculate taxes based on local rules, check for possible errors before payments are made, and keep a full record of how each figure was produced. In other words, it serves as both a payroll engine and a smart assistant that helps employers stay compliant without constant manual recalculations.
As Senior Data Engineer, Shanmuka Siva Varma Chekuri helped shape the system so it could operate reliably in the U.S., Canada, India, Dubai, and Australia. To do this, he first documented how payroll is handled in each of these places, including older practices that are not written down anywhere. Based on this research, he built models that reflect the tax rules of each region, set up fast data flows so the system could update information in real time, and added tools that detect unusual entries before they turn into errors. He also ensured that every step in the process can be easily traced, which is important for audits and for companies that need to show how calculations were made.
These decisions had a direct business impact. Testing showed that payroll cycles ran about 60 percent faster and that the system avoided compliance mistakes entirely. The reliability of the platform played a significant role in securing investment for future global expansion.
“We had to understand how payroll actually works in each place before we could build something universal,” Chekuri explains. “A lot of the older workflows aren’t documented anywhere. Mapping them out was the only way to make the system stable for people who depend on it every month.”
For businesses in India adapting to new payroll requirements, this example shows how well-designed technology reduces manual checks and routine rework. Instead of reviewing every number by hand, companies can rely on a system that keeps track of local rules, updates when policies change, and alerts teams to possible issues before they cause trouble. This makes compliance less stressful and gives small and mid-sized companies the kind of support that usually exists only in bigger organizations.
Much of his approach comes from his earlier work in financial data engineering. Through American Software Group, he has contributed to automation projects for Everest Reinsurance, a company that has operated for more than 50 years across North America, Europe, Asia-Pacific, and Latin America. It handles large volumes of financial and policy data, which makes accuracy, consistency, and auditability central to the systems Chekuri has worked on.
In those projects, he created a system that checks whether the financial data coming in is complete and correct. For example, it checks whether any records are missing, duplicated, or in the wrong format before the information is used in reporting or payments. The framework was designed to prevent issues such as missing records or mismatched formats and helped reduce reconciliation time by about 40%. Chekuri also created a metadata-based audit-lineage method that improves traceability. That solution shortened audit preparation time by more than half.
“We moved the older workflows onto tools like Azure Data Factory and Databricks,” Chekuri says. “Azure Data Factory helps automate how data moves, while Databricks lets us organise and prepare it. Some of the original steps weren’t documented, so I had to trace how everything worked and rebuild it in a clearer, more reliable way.”
While these systems were originally developed for insurance and financial workflows, the same ideas apply to payroll. Just as a building needs a solid foundation before anything else can function safely, payroll systems depend on accurate and well-organised data before calculations begin. This becomes especially important in payroll, where even small inconsistencies can affect salaries, deductions, or statutory contributions. When the underlying data is stable, the entire process becomes easier to manage. Meanwhile, tools like Unityware Al illustrate how technology can help reduce manual work by organising data more consistently and applying rules in a structured way. This does not remove the need for oversight, but it can make day-to-day processes more predictable. As companies adapt to new payroll guidelines introduced under the labour codes, technology that reduces manual steps and flags irregularities early can help prevent errors that might otherwise go unnoticed.
I believe technology should make everyday work easier, not more complicated, Chekuri says. “Many small teams don’t have dedicated technical staff, so the systems we build should help them.’’
This outlook also shapes the way he works with people. As part of his human-focused approach, Chekuri mentors junior engineers and international students and supports collaboration across teams in different regions. He aims to create environments where individuals with varying levels of experience can contribute effectively. This approach supports the idea that reliable systems depend not only on technical design but also on how teams build, maintain, and improve them over time.
His work has also drawn recognition from the professional community. As his architectural approaches and governance frameworks gained wider adoption, the Association of Information Technology Experts (AlTEX) invited Chekuri to become a Council Member of the organization. The appointment followed a peer review of his professional contributions, technical leadership, and impact on enterprise data-engineering practices, reflecting recognition of his role in advancing reliable, audit-ready data systems in regulated environments.
Chekuri plans to expand Unityware Al further into international markets, with India positioned as one of the most important regions due to the scale of its small and mid-sized business sector. For businesses navigating an increasingly complex regulatory environment, the impact of this work may be felt sooner than they realize.
