The Good Tech Companies - How Gen AI and AWS Bedrock Supercharged Real Estate Data Extraction

Episode Date: June 25, 2024

This story was originally published on HackerNoon at: https://hackernoon.com/how-gen-ai-and-aws-bedrock-supercharged-real-estate-data-extraction. How Gen AI and AWS Bedrock Supercharged Real Estate Data Extraction Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #generative-ai, #data-extraction, #aws, #using-ai-for-real-estate, #gen-ai-for-real-estate, #gen-ai-client-case-study, #good-company, #indium, and more. This story was written by: @indium. Learn more about this writer by checking @indium's about page, and for more stories, please visit hackernoon.com. Real estate and private equity is a world where data is king, but extracting that data can be a royal pain. A client needed a data extraction knight in shining armor, a solution that could automate the process, improve efficiency, and free them from the shackles of manual data entry. Here's how they tackled the challenge.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. How General AI and AWS Bedrock Supercharged Real Estate Data Extraction, by Indium. Hey there, data aficionados. Today, we're diving into the world of real estate and private equity, where data is king, or maybe queen, depending on your market. But extracting that data? That can be a royal pain. Just ask our client, a digital middle office solutions provider who was drowning in a sea of paperwork. Let me tell you, manually extracting data from asset valuation reports, real estate assessments, and lease collection records is enough to make anyone cry. It's slow, tedious, and prone to

errors. Our client was facing a triple-threat manual data extraction. Extracting data by hand is like washing dishes. Never-ending and frankly, soul-crushing. Document complexity. These documents weren't your friendly neighborhood grocery list. They were unstructured, long-winded beasts with tables that would make a mathematician weep. Time consumption. Manually wrangling this data was a black hole, sucking away precious time and resources. There had to be a better way. General AI. The quest for efficiency. Why we needed automation. Our client needed a data extraction knight in shining armor. A solution that could automate the process, improve efficiency, and free them from the shackles

of manual data entry. Here's what they were looking for. One, efficiency, slashing the manual workload associated with data extraction. Two, accuracy, extracting data with laser-like precision to ensure reliable information. Three, document flexibility, handling diverse document types from unstructured chaos to neat and tidy tables. Building the dream team. General AI and AWS Bedrock we knew a one-size-fits-all approach wouldn't cut it. So, we assembled a dream team of technologies, with General AI and AWS Bedrock leading the charge. Here's how we tackled the challenge 1. Building a fortress. The power of AWS. We leveraged the mighty AWS cloud environment, utilizing AWS Bedrock services to construct a robust backend. This fortress provided a secure and scalable foundation for our custom-developed solutions. 2. Document parsing with general AI.

The brains of the operation, powered by AWS Bedrock, we built a custom-made document parser. This intelligent tool was like Sherlock Holmes for documents, analyzing their structure and content to identify and extract relevant data fields with pinpoint accuracy. 3. Specialized Pipelines. Tailored for every document, we didn't believe in a one-size-fits-all approach. Instead, we designed specialized data extraction pipelines for each document type, ensuring optimal performance and accuracy for every format, from asset valuation reports to real estate assessments. 4. Advanced AI Models and Tools The superpower squad, we assembled a league of extraordinary AI models and tools to achieve

the best possible results. OpenSearch provided a search platform with flexibility and scalability, while FAISS facilitated the efficient retrieval of similar documents. Additionally, we leveraged the power of foundation models like Titan and Cohere, along with Retrieval Augmented Generation, RAG, to take the extraction process to the next level. 5. Integration with Scanned Documents No level. 5. Integration with scanned documents. No doc left behind. We knew scanned documents were a reality, so we integrated a WS Textract. This powerful tool extracts data from scanned documents with impressive accuracy,

ensuring seamless processing of all document formats. 6. Data quality. Our top priority, maintaining data accuracy, was paramount. We implemented rigorous data quality, DQ, checks across the extracted data, employing filtering mechanisms to guarantee clean and reliable outputs. This ensured the client received trustworthy data, ready for further use. By combining these elements, we created a comprehensive solution that addressed the client's specific needs for efficient and accurate data extraction from complex and varied documents.

Quantifiable success. The numbers don't lie. The impact of our general AI-powered solution was clear as day and measurable. Here's how general AI helped our client achieve significant improvements soaring accuracy. 87% across the board, our client craved data accuracy, and we delivered. Our solution achieved an impressive 87% accuracy rate across all document types. This meant the extracted data was reliable and ready for further analysis and use without extensive manual verification. Dramatic reduction in manual effort. From days to hours, the time-consuming nature of manual data extraction was a major bottleneck for the client. Our solution streamlined

the process, resulting in a staggering 700x reduction in manual effort. Imagine tasks that previously took days to complete, now achievable in a matter of hours. Think of the reallocated resources and the potential for increased productivity. That's a 700x time multiplier, folks. That's like shaving weeks off a project, freeing up your team to focus on higher-level tasks, strategic analysis, or even chasing that elusive work-life balance. Significant cost savings. A 4x advantage, the efficiency gains brought about by automation also yielded substantial cost savings for the client. By eliminating the need for manual data extraction, the client achieved a 4x reduction in costs.

These savings can be reinvested in further growth initiatives, expanding their service offerings, or even delighting their clients with lower fees. Beyond the numbers, the ripple effect of efficiency. The benefits of our solution extended far beyond the numbers. Here's what our client experienced improved decision-making. With accurate and timely data at their fingertips, the client could confidently make data-driven decisions. Enhanced client service, faster turnaround times and improved data quality translated into better service for their clients. Increased scalability. The automation freed up resources, allowing the client to scale their operations and easily handle a larger volume of data. The takeaway. General AI and AWS Bedrock, your data extraction dream team. This project is a testament to General AI's and

AWS Bedrock's power. By combining these innovative technologies, we were able to transform a tedious and error-prone process into a streamlined and efficient operation. If you're drowning in a sea of documents and struggling with data extraction, don't despair. We can help you build your own dream team. Indium's expertise in GAN AI and our understanding of your industry challenges can help you unlock the potential of your data and achieve remarkable results. So, are you ready to say goodbye to manual data extraction and hello to a world of efficiency and productivity? Let's chat, we're here to help you turn your data extraction woes into a thing of the past. Thank you for listening to this Hackernoon story, read by Artificial Intelligence.

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