Secure, Fast, and Scalable: The Future of Tax Automation with Additive AI
Accounting firms have relied on outsourced labor to extract structured information from semi-structured tax documents for many years. It’s a sluggish process that’s prone to human error and fraught with data privacy concerns, as tax documents are often lengthy, filled with dense tables, annotations, semi-structured language, and varying formats depending on the institution and jurisdiction in question.
These documents are often sent overseas, where they are handled manually by business process outsourcing (BPO) teams, creating security risks, regulatory requirements, and inefficiencies in an already complex compliance landscape.
It’s exactly this bottleneck that applied machine learning and AI expert Manjunatha Jagalur is working to solve. As a senior engineer at Additive AI, he’s assisting design and implement systems that automate the entire data extraction workflow using large language models (LLMs), removing the need for human involvement and dramatically improving speed, security, and scalability.
Read on to learn how these systems are providing new possibilities for managing tax data and why they herald a more transparent and efficient future for the financial industry.
What Additive AI Does
“Currently, most accounting firms send the documents to BPO offices and use manual labor to extract information from tax documents. These systems are sluggish, sometimes taking hours per document, error-prone, and less secure since they need to be sent overseas and are reviewed by humans,” Manjunatha explains.
is working to resolve these issues by building enterprise-grade AI infrastructure that automates tax and accounting workflows. Their systems read, understand, and extract structured data from complex financial documents without manual input, integrating directly into existing tax and compliance platforms to reduce turnaround times, lower operational overheads, and improve data privacy.
Unlike traditional optical character recognition (OCR) or rules-based extraction tools, Additive AI’s foundation models adapt to inconsistencies in document format and language, enabling reliable automation even for highly unstructured documents.
The result is a system that is scalable, secure, and able to streamline data extraction tasks that previously required extensive, and expensive, human effort.
LLMs Offer a securer, quicker, and More Scalable Alternative
To achieve this, Manjunatha is designing scalable systems that replace manual labor with automation powered by large language models, and the results have been transformative: data extraction in minutes instead of hours, zero human involvement, and no need to transmit sensitive tax documents across borders.
By fully automating the process, the Additive AI system allows financial workflows to manage the growing volume and complexity of tax documents without adding staff or risking data exposure.
However, deploying LLMs to extract data from messy documents is far from straightforward. Tax documents come in diverse formats and require precise classification and intimate understanding of each data point. For LLMs to consistently deliver reliable results, they must be equipped to handle these nuances and variations in financial records.
To ensure these production standards were met, he developed multiple prompt engineering techniques to improve the accuracy so that the performance is at the required level. These techniques include detailed guidance, contextual clues, few-shot learning, a chain of thought prompting, post-prompt filtering, and layout hints.
These prompt engineering techniques ensure that the systems remain reliable and accurate in real-world environments where precision is crucial.
Long-Term Vision: A More Transparent and Accessible Tax System
Manjunatha’s work with Additive AI represents a significant shift in how sensitive tax data is handled in financial operations. By using secure AI-integrated systems instead of manual, overseas-based extraction workflows, he is directly tackling a persistent and long-standing industry challenge.
“In the long term, we can expect the tax industry to be more transparent and accessible by applying this technology,” he concludes, pointing to the broader impact of his work.
This approach is not just about improving processing speed, it’s about creating a financial process that is secure, disclosure-oriented, and better aligned with modern compliance and privacy demands.
Through innovation in prompt engineering and LLMs, is assisting set new technical and ethical standards for processing financial data, ensuring answers that scale with evolving needs.
