Transforming Tax R&D with AI-Powered Automation
Langchain|Python
AI/ML
Dedicated team behind the project
The Client
Source Advisors, a leading U.S.-based tax consulting firm, specializes in innovative solutions to simplify and automate tax credit processes. With over four decades of expertise, the company sought to revolutionize its R&D study preparation system through advanced AI technologies. The project aimed to introduce an AI-driven solution that would streamline workflows, improve accuracy, and significantly reduce operational costs.
Client Achievements:
THE CHALLENGE
Source Advisors faced a significant challenge in modernizing their tax study preparation process, which relied on manual efforts to transform client interviews into comprehensive R&D tax studies. The existing method was time-consuming, required extensive human intervention, and was prone to inconsistencies. To address this, the client sought to develop an AI-driven system capable of:
Processing audio data with multiple speakers required precise transcription capabilities, including noise reduction and speaker differentiation, while recognizing tax-specific terminology.
Securely storing, retrieving, and versioning sensitive documents within a structured system was essential for compliance and operational efficiency.
The platform needed custom-trained large language models (LLMs) capable of understanding intricate tax regulations and terminologies to deliver accurate and context-aware analysis.
Ensuring that the platform could automatically generate compliant, professional-quality tax study reports while maintaining consistency and validation was critical.
The infrastructure needed to handle large volumes of data and provide seamless integration options for enterprise clients.
What Was Done
To address the challenges Source Advisors faced in automating R&D tax study preparation, we developed a robust AI-powered platform. The project focused on delivering end-to-end automation while ensuring compliance, accuracy, and scalability. Key actions included:
- Development of AI Models: Tailored large language models (LLMs) were trained to process tax-specific data, ensuring contextual accuracy and relevance in report generation.
- Speech-to-Text Pipeline: A high-accuracy transcription system was implemented, enabling automated conversion of client interview recordings into structured text inputs for further processing.
- System Architecture Design: A scalable, cloud-based infrastructure was established to support the system’s operational needs, ensuring performance consistency across varying workloads. Our system can scale both vertically and horizontally. It is also designed to work in different independent modules, which makes it easy to scale.
- Integration with Existing Systems: The solution was designed to work seamlessly with Source Advisors’ existing workflows, providing flexibility and ease of adoption. This system is to become a crucial part of their GOAT Platform.
Quality Assurance: Rigorous testing was conducted to ensure the accuracy, reliability, and compliance of the system before deployment.
Implemented Features:
We implemented a transcription pipeline using the Azure Speech-to-Text service. This solution provided high-accuracy speech-to-text conversion while leveraging Azure’s robust capabilities. Given the project requirements, speaker differentiation was not implemented to optimize costs, as it was not critical for generating accurate R&D tax study reports.
We implemented a cloud-based document management system with structured storage, version control, and audit trails. This system ensures compliance, secure handling of sensitive data, and seamless document retrieval for tax studies.
Custom-trained LLMs were integrated to analyze complex financial and tax-related content. These models enabled context-aware processing, pattern recognition, and accurate data extraction for R&D study generation.
The platform introduced dynamic templates for generating professional-grade reports. Currently utilized by internal Source Advisors users, the underlying technology is designed to enable customizable templating capabilities for future external users. These templates ensure consistent formatting, deep control over study parameters, and quality validation, supporting personalized and scalable financial studies.
Enhancements included automated pattern recognition, validation mechanisms in data entry forms, and real-time user feedback during report generation. These features improved consistency, quality control, and client satisfaction.
The system’s flexible architecture allowed for multiple revenue streams:
- Full Platform Solution: A white-label platform with customizable branding, reporting templates, and dashboards for enterprise clients.
- API Service Option: Seamless integration into existing systems with a pay-per-use pricing model for flexibility and scalability.
The system is based on Microsoft Azure, making it highly scalable, while using advanced tools and is designed to handle varying workloads with minimal latency. This infrastructure supported end-to-end workflow automation and integration with enterprise systems.
Results
The system achieved an impressive 20x reduction in operational costs compared to traditional methods, delivering unmatched efficiency for businesses of all sizes
Professionals can now complete study documentation and parallelize processing jobs in one-third of the time, thanks to automation of repetitive, time-consuming tasks
The dual-approach architecture offers:
- A Full Platform Solution: Ready-to-deploy with a polished UI and extensive features.
- API Service Option: Seamless integration with existing systems, ideal for scaling AI capabilities
Designed for reliability, the system handles growing workloads with real-time processing, robust security protocols, and minimal latency. The used tools are the most popular LLM libraries on Python, Postgres and Azure
Features tailored to industry-specific needs, with built-in analytics, monitoring tools, and regular updates to ensure ongoing performance optimization
This AI-powered system positions itself as a market leader, delivering cost-effectiveness and operational excellence for startups and enterprise-level organizations alike
Tech Stack
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