Upcoming Event: The AI Agent Implementation Framework
Upcoming Event: The AI Agent Implementation Framework
3C Solutions is an IT services and software development firm based in Washington, D.C. that builds technology solutions for businesses across the real estate, healthcare, and professional services industries. The company faced a critical operational bottleneck: staff had to manually extract 22–24 data fields from each real estate transaction document, a process that was slow, error-prone, and unable to scale. Avahi, an AWS consulting partner, designed and delivered an AWS-native, event-driven GenAI-powered OCR pipeline that automated the entire document extraction workflow using Amazon Textract and Amazon Bedrock. The solution achieved 96% field extraction coverage with 90% accuracy on critical date fields, transforming a fully manual process into an automated pipeline capable of batch-processing 40–80 files per transaction.
3C Solutions is a Washington, D.C.-based IT services provider with over 20 years of experience delivering custom software development, managed IT services, network infrastructure, and cybersecurity solutions to businesses in the Northern Virginia, D.C., and Maryland region. The company serves clients across multiple industries, including real estate, healthcare, and professional services. Among its technology products, 3C operates Broker Central, a proprietary platform used by real estate brokerages to manage transaction data, document workflows, and compliance processes. With a team of approximately 15 employees, 3C combines deep technical expertise with a client-first approach, functioning as an extension of its customers’ IT departments.
3C Solutions’ real estate clients relied on a labor-intensive manual process to extract critical data from transaction documents. Each real estate transaction required staff to review and manually capture 22–24 specific data fields, including settlement dates, MLS listing information, agent details, commission structures, and property identifiers, from a mix of scanned forms, PDFs, and image files. This manual approach consumed significant staff time, introduced accuracy risks, and could not scale as transaction volumes grew.
The challenge was compounded by limited sample data availability. With only 22–23 documents available for establishing extraction patterns (well below the 40+ typically needed for reliable model development), building an automated solution required creative approaches to prompt engineering and classification design.
Without internal GenAI or machine learning expertise, 3C lacked the capability to build an AI-powered extraction system independently. The company’s existing Broker Central platform could manage transaction data once entered, but there was no automated bridge between raw documents and structured database records. Each jurisdiction (Maryland, Virginia, and D.C.) used different document formats and field conventions, adding further complexity. Continuing with manual workflows would leave 3C unable to offer the automated, scalable document processing services its real estate clients increasingly expected.
AWS offered a comprehensive suite of AI and machine learning services purpose-built for document processing workflows. Amazon Textract provided OCR capabilities for extracting text, key-value pairs, and form fields from PDFs and images, while Amazon Bedrock offered access to foundation models for GenAI-powered entity extraction, both critical components of the two-stage extraction pipeline the project required.
The broader AWS ecosystem, including Amazon S3 for document storage, AWS Lambda and Amazon EventBridge for event-driven orchestration, AWS Fargate for serverless compute, and Amazon RDS for structured data storage, enabled a fully managed, scalable architecture that minimized operational overhead. AWS’s pay-as-you-go pricing model and serverless components ensured cost efficiency for variable document processing volumes. The engagement was supported through AWS partner funding, reinforcing AWS’s commitment to enabling AI-driven business transformation for its customers.
3C Solutions needed a partner with deep expertise in both GenAI application development and AWS-native architecture design. Avahi, as an AWS consulting partner specializing in generative AI, cloud migrations, and application modernization, brought the specialized AI/ML engineering talent and AWS infrastructure knowledge that 3C lacked internally.
Avahi’s team included experienced data scientists, AI engineers, and cloud engineers capable of designing and delivering a production-ready extraction pipeline from the ground up, spanning OCR integration, GenAI prompt engineering, event-driven architecture, and front-end application development. This breadth of capability meant 3C could engage a single partner for the entire solution rather than coordinating across multiple vendors.
The engagement model, supported through AWS partner funding, allowed 3C to access enterprise-grade GenAI capabilities without upfront infrastructure investment, with Avahi managing the full delivery lifecycle from discovery through knowledge transfer and post-engagement email support.
Avahi designed and delivered an AWS-native, event-driven GenAI-powered OCR pipeline that automated the entire document extraction workflow for 3C Solutions. The solution combined Amazon Textract’s OCR capabilities with Amazon Bedrock’s GenAI foundation models to create a two-stage extraction pipeline capable of handling both structured form data and unstructured text found in real estate transaction documents.
The architecture follows an event-driven pattern orchestrated entirely through AWS services. Users upload documents through a Streamlit web interface deployed via Amazon CloudFront, which generates pre-signed Amazon S3 URLs through Amazon API Gateway and AWS Lambda for secure file transfer. Upon file arrival in Amazon S3, Amazon EventBridge triggers AWS Fargate tasks that orchestrate the processing pipeline. Documents first pass through a custom classification engine using confidence scoring to identify document types and filter irrelevant content. Amazon Textract then performs OCR to extract text, key-value pairs, and form fields from PDFs, JPGs, and PNGs. Amazon Bedrock applies GenAI-powered entity extraction using foundation models with document-type-specific engineered prompts tailored to real estate transaction formats across Maryland, Virginia, and D.C. jurisdictions.
Extracted data is transformed into structured JSON format and stored in an Amazon RDS database. The solution supports cross-account integration through an AWS Site-to-Site VPN connection, enabling seamless data flow into 3C’s existing Broker Central platform. The pipeline handles batch processing of 40–80 files per transaction using TransactionID-based tracking with queue-based asynchronous processing, ensuring scalability while maintaining cost efficiency through serverless compute.
Avahi managed the full delivery lifecycle across a five-month engagement from September 2025 through March 2026, with daily standups and weekly client touchpoints ensuring responsive collaboration. The engagement included comprehensive technical documentation, codebase handover with README files, and multiple knowledge transfer sessions to ensure 3C’s team could independently maintain and extend the solution. Two weeks of post-engagement email support were provided for any remaining clarifications.
The solution transformed 3C Solutions’ document processing from a fully manual operation into an automated AI-powered pipeline. The system achieved automated extraction of 23 out of 24 target fields, representing 96% field coverage, meaning nearly all required real estate transaction data points are now captured without manual data entry. On MLS dates, a critical time-sensitive field for transaction tracking, the system demonstrated 90% accuracy, confirming production-ready performance on high-priority data points.
The Fargate-based serverless architecture provides elastic scalability to handle variable document volumes, with event-driven processing initiated automatically upon file upload and queue-based asynchronous processing ensuring reliable throughput during peak loads. At project closure, the client rated the overall engagement 5 out of 5 across project delivery, technical expertise, project management, and overall experience with Avahi, noting the team’s responsiveness, support throughout the complex engagement, and ability to keep the project on schedule. The solution was delivered within the allocated budget framework at 95% budget utilization, demonstrating efficient resource use despite the technical complexity and limited sample data constraints.
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