AI PoCs
Showcasing real-world applications through AI proof of concepts
Description
A web application that generates flowcharts from natural language prompts, allowing users to create visually structured workflows, decision trees, or process maps effortlessly. The app leverages NLP to understand and convert user instructions into clear, interactive diagrams.
Tools/Technologies
- •Software Development: Map out algorithms, system architecture, or logic workflows.
- •Project Planning: Quickly outline project workflows or task dependencies.
- •Education: Teachers and students can visualize concepts or study processes.
- •Business Operations: Document standard operating procedures or decision-making processes.
- •Brainstorming: Capture and organize ideas visually during collaborative sessions."
Potential Use Cases
- •Software Development: Map out algorithms, system architecture, or logic workflows.
- •Project Planning: Quickly outline project workflows or task dependencies.
- •Education: Teachers and students can visualize concepts or study processes.
- •Business Operations: Document standard operating procedures or decision-making processes.
- •Brainstorming: Capture and organize ideas visually during collaborative sessions."
Technical Details
- •Backend: Uses Express.js with Llama 3.1 7B model for generating flowchart schemas from user prompts.
- •Frontend: Built with React and React Flow for rendering interactive flowcharts.
- •Workflow: User query → Schema generation via Llama → Flowchart rendered dynamically.
- •Features: Real-time schema-to-flowchart conversion with editable nodes and edges.
- •Scalability: Modular design allows integration of advanced models and tools."
Description
A tool that automates the detection and extraction of advertisements from The Times of India newspaper, converting unstructured PDF content into structured JSON response
Tools/Technologies
- •Automated Data Entry for Advertisement Archives: Generate structured data from newspaper ads, enabling faster and error-free archiving in databases for legal, marketing, or historical purposes.
- •Competitor Analysis in Advertising: Extract and compare ad details from newspapers to study competitor strategies, pricing, and frequency of advertisement placement.
- •Automated Invoice Generation for Advertisers: Use extracted ad details to generate invoices for advertisers, including ad size, content, and page placement, streamlining billing processes.
Potential Use Cases
- •Automated Data Entry for Advertisement Archives: Generate structured data from newspaper ads, enabling faster and error-free archiving in databases for legal, marketing, or historical purposes.
- •Competitor Analysis in Advertising: Extract and compare ad details from newspapers to study competitor strategies, pricing, and frequency of advertisement placement.
- •Automated Invoice Generation for Advertisers: Use extracted ad details to generate invoices for advertisers, including ad size, content, and page placement, streamlining billing processes.
Technical Details
- •Used the pdf2image library to convert The Times of India newspaper PDFs into high-resolution images for further processing.
- •Leveraged PaddleOCR to extract text from these images and identified potential ad pages.
- •Deployed a custom-trained YOLO model (via the Ultralytics library) to detect specific advertisement regions within the filtered pages.
- •Leveraged Groq, a Vision-Language Model (VLM) to receive structured JSON responses with extracted ad details, enabling integration into downstream analytics systems.
Description
Detects tables in an image and returns the precise coordinate points of the detected table, while accurately extracting and redrawing the entire table to preserve its structure. The model identifies the coordinates of each individual cell and performs Optical Character Recognition (OCR) on each cell separately to capture the data effectively.
Tools/Technologies
- •Financial Services
- •Extracting data from scanned financial statements
- •Processing bank statements and transaction records
- •Converting legacy paper documents to digital formats
- •Healthcare
- •Extracting clinical trial data tables
- •Processing medical billing statements
- •Insurance
- •Processing claim documents
- •Extracting policy details from legacy documents
- •Converting actuarial tables
- •Manufacturing & Supply Chain
- •Extracting data from inspection reports
- •Converting inventory records
- •Processing bill of materials
- •Legal Industry
- •Extracting data from legal contracts
- •Converting court documents and case files
- •Academic & Research
- •Processing survey results and statistical data
- •Extracting data from scientific publications
Potential Use Cases
- •Financial Services
- •Extracting data from scanned financial statements
- •Processing bank statements and transaction records
- •Converting legacy paper documents to digital formats
- •Healthcare
- •Extracting clinical trial data tables
- •Processing medical billing statements
- •Insurance
- •Processing claim documents
- •Extracting policy details from legacy documents
- •Converting actuarial tables
- •Manufacturing & Supply Chain
- •Extracting data from inspection reports
- •Converting inventory records
- •Processing bill of materials
- •Legal Industry
- •Extracting data from legal contracts
- •Converting court documents and case files
- •Academic & Research
- •Processing survey results and statistical data
- •Extracting data from scientific publications
Technical Details
- •Compares YOLO, Microsoft TATR, and Tablenet for detection.
- •YOLO detects tables in images, returning coordinates and confidence scores for each detected table. TATR analyzes table structures and outputs the coordinates and confidence scores for table components.
- •Tablenet utilizes OpenCV independently for image preprocessing, enhancing table detection accuracy by adjusting image properties.
- •Returns cropped detected tables with confidence scores.
- •Table images are extracted into CSV format.
- •Cells in the table image are cropped using coordinates from the Tablenet method.
- •OCR is performed on each cell using Pytesseract, Paddle-OCR, Easy-OCR, and DocTR, returning the best OCR output based on the confidence score.
- •With the cell coordinates and their OCR outputs, a Pandas DataFrame is created and converted into CSV.
- •Gradio UI for table detection.
Description
Processes a PDF document as input and uses Retrieval-Augmented Generation (RAG) to answer queries related to the content of the uploaded PDF. It converts the PDF into embedding chunks using an embedding model. When a query is made, the model retrieves the relevant chunks from the embedded data and generates an accurate answer based on the retrieved information. This solution is tailored for healthcare-related PDFs, such as medical reports, clinical guidelines, or patient records.
- •Clinical Decision Support Systems that provide healthcare professionals with quick answers from medical reports and guidelines.
- •Patient Record Management for efficient retrieval of relevant information during consultations or treatment planning.
- •Medical Research tools enabling researchers to query large datasets of clinical guidelines and reports for insights.
- •Healthcare Compliance Monitoring to ensure that clinical practices align with the latest guidelines by retrieving relevant information from PDFs.
Potential Use Cases
- •Clinical Decision Support Systems that provide healthcare professionals with quick answers from medical reports and guidelines.
- •Patient Record Management for efficient retrieval of relevant information during consultations or treatment planning.
- •Medical Research tools enabling researchers to query large datasets of clinical guidelines and reports for insights.
- •Healthcare Compliance Monitoring to ensure that clinical practices align with the latest guidelines by retrieving relevant information from PDFs.
Technical Details
- •ChatGPT-4 pdf chat clone on our local server.
- •Upload any PDF and ask queries on that PDF
- •used LangChain for performing RAG (Retrieval-Augmented
- •Generation)
- •Used Embedding models for generating embeddings.
- •Used chroma for storing embeddings , also stored embedding on
- •local machine for avoiding dependency of vector store.
- •Streamlit for building UI.
Description
Processes a PDF payslip as input and utilizes Retrieval-Augmented Generation (RAG) to answer queries related to the content of the uploaded payslip. It converts the PDF into embedding chunks using an embedding model. When a query is made, the model retrieves relevant chunks from the embedded data and generates accurate answers based on the retrieved information. This solution is specifically tailored for payslip-related PDFs, enabling users to gain insights into their earnings, deductions, and other relevant details.
- •Personal Finance Management tools that allow users to analyze their payslips for budgeting and financial planning.
- •Payroll Inquiry Systems enabling employees to ask specific questions about their earnings and deductions directly from their payslips.
- •Tax Preparation Applications that help users understand tax deductions and withholdings detailed in their payslips.
- •HR Support Services for answering common payroll-related questions based on employees' uploaded payslips.
Potential Use Cases
- •Personal Finance Management tools that allow users to analyze their payslips for budgeting and financial planning.
- •Payroll Inquiry Systems enabling employees to ask specific questions about their earnings and deductions directly from their payslips.
- •Tax Preparation Applications that help users understand tax deductions and withholdings detailed in their payslips.
- •HR Support Services for answering common payroll-related questions based on employees' uploaded payslips.
Technical Details
- •ChatGPT-4 pdf chat clone on our local server.
- •Upload any PDF and ask queries on that PDF
- •used LangChain for performing RAG (Retrieval-Augmented
- •Generation)
- •Used Embedding models for generating embeddings.
- •Used chroma for storing embeddings , also stored embedding on
- •local machine for avoiding dependency of vector store.
- •Streamlit for building UI.
Why Start with an AI PoC?
2025 has become increasingly complex, with businesses facing tough choices between numerous AI tools, frameworks, and approaches. Recent high-profile failures of well-known companies highlight a crucial lesson: developing an AI PoC is essential before jumping on the latest technology, as successful AI implementation isn't about using cutting-edge tools, but about validating your specific use case.
Business founders must begin their AI initiatives with a Proof of Concept due to the unique challenges and resource constraints they face. Building an AI POC helps validate both technical feasibility and market potential while minimizing initial investment risks. Through this approach, founders can quickly assess if their AI solution addresses real market needs and if it's achievable with their current data and resources.
This method builds Stakeholder confidence by demonstrating concrete results rather than theoretical possibilities. Early testing through an effective AI POC reveals potential technical challenges, accurate cost projections, and necessary team capabilities. Most importantly, a PoC prevents the significant time and financial investment that could be lost on an AI solution that doesn't align with business requirements or market demands.
What Are The Advantages of An AI PoC?
An AI PoC provides businesses with a practical way to test AI solutions in a controlled environment. This approach lets organizations validate their AI ideas with minimal risk while gathering concrete data about performance, requirements, and potential challenges.
Through implementing AI POCs, businesses can understand their true data readiness and infrastructure needs before making substantial investments. This early insight helps prevent costly mistakes and ensure resources are allocated effectively. The AI POC process also provides teams with hands-on experience, building internal capabilities and understanding of AI implementation requirements.
The evidence gathered during a PoC strengthens decision-making for larger AI initiatives. With clear metrics and real results, organizations can better evaluate potential returns and resource requirements, making it easier to secure stakeholder support and plan for successful scaling.
Key Benefits:
What are the Essential Components of an AI PoC
Implementation Framework of an AI PoC
Successful AI POC development follows a structured framework:
This framework ensures a structured approach while maintaining flexibility to adapt to your specific needs and challenges.
Key Deliverables at Each Stage:
How F22 Labs Can Help You Create The Best AI PoC
We follow a structured yet flexible approach to AI PoC development:
We specialize in turning complex AI concepts into practical business solutions. Our team brings extensive experience in machine learning, data science, and enterprise software development, ensuring your PoC is built on solid technical foundations.
Our Process of Building an AI PoC
Why Choose F22 Labs?
Throughout the PoC development process, you'll receive:
Our Support and Guidance
Ready to start your AI journey? Contact us to discuss how we can help bring your AI vision to life through a well-executed PoC.
Frequently Asked Questions (FAQs)
An AI PoC (Proof of Concept) is a small-scale test project that shows how artificial intelligence can solve specific business problems. It helps companies validate if an AI solution will work before making a larger investment, by demonstrating core features and measuring potential success.
Most AI PoCs can be completed within 1 week, as we focus on rapid assessment and feasibility analysis to provide quick insights into project viability.
From an AI PoC, you can expect deliverables such as a working prototype, performance metrics, feasibility reports, and recommendations for scaling or improvement.
Data requirements vary by project type. While some PoCs require sample data for validation, others can be assessed based on technical requirements and use case analysis.
An AI PoC helps validate the feasibility of AI solutions, minimize risks, and demonstrate potential ROI before committing to full-scale development, saving both time and resources.
AI PoC costs typically range from $15,000 to $50,000, varying based on features, complexity, and integration requirements. Each PoC is priced based on specific project needs and scope.