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
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
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
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.
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.
Description
It processes PDF invoices to efficiently respond to user queries about their content. By converting invoices into embedding chunks using an embedding model, it leverages Retrieval-Augmented Generation (RAG) to extract and provide precise answers. When a query is submitted, the model retrieves relevant information from the embedded chunks, allowing users to gain insights into billing details, payment statuses, itemized charges, and other crucial information found within the invoice. This tailored solution streamlines invoice management and enhances financial data accessibility.
Description
Finds QR and Bar codes in images and extracts them, then scans to give their URLs. Includes tabs for QR, Bar code, and OCR for extracting card details.
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Converts text queries to SQL, enabling users to interact with databases using natural language instead of writing SQL code. Fine-tuning defog/sqlcoder-7b-2 model for shopify store data.
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An AI agent chat interface that creates, deletes and lists events within a particular time limit. It checks if a person is free or busy, and lists available free schedules on Google Calendar using API calls.
Tools/Technologies
Description
This is an interactive voice-to-voice system that allows users to engage in natural conversations. The system captures spoken input through microphone, transcribes it into text using a speech-to-text model (OpenAI's Whisper). This text is then processed by an LLM(llama-3.1-70b-versatile) to generate a contextually appropriate response. Finally, the response is converted back into speech using a text-to-speech model(facebook/mms-tts-eng), enabling verbal communication with the system.
Tools/Technologies
Description
This is an interactive voice call conversational AI system designed to confirm basic candidate details and conduct pre-interview calls. The system utilizes Twilio to initiate calls and manages the conversation using the Groq 'llama-3.1-70b-versatile' model. Twilio converts user speech to text, which is then sent to the LLM to generate a response. The response is relayed back to Twilio, which plays it during the call, facilitating a natural conversation. The LLM is specifically prompted to emulate an HR representative and ask relevant questions.
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Real-time speech recognition converts spoken language into text instantaneously, enabling fast, accurate voice-to-text applications.
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Description
This is an easiest way to use Agentic RAG in any enterprise.. Flexible Dashboard to choose desired LLM from Respective providers such as openai, Groq and provide custom System Prompt with Websearch, Code and Image Generation.
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Late Chunking is a sophisticated chunking technique designed to tackle the issue of lost context in natural language processing. This method enhances the quality of text embeddings by ensuring that the contextual relationships between tokens are preserved, resulting in more meaningful representations.
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This diffusion model takes an image of a person and an outfit image to visualize how the person would look wearing that outfit. It accurately detects and analyzes the pose of the person, ensuring a realistic representation. The model then seamlessly fits the outfit to the individual, adjusting for body proportions and pose dynamics. This technology offers a novel way to experience fashion, allowing users to see themselves in various outfits without trying them on physically.
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Description
The AI Prescription Assistant is a innovative healthcare technology solution that combines a Chrome extension, web interface, and voice recognition capabilities to streamline the prescription documentation process. By leveraging Groq's AI capabilities, this tool transforms spoken medical information into accurately filled prescription forms, enhancing efficiency and reducing potential errors in medical documentation.
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A tool that seamlessly transforms files into interactive knowledge graphs and extracts insights through intuitive queries.
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A specialized computer vision system trained to automatically detect and extract corporate financial announcements from Kuwaiti newspapers. Built on Vision Transformer (ViT-base) architecture and fine-tuned through supervised learning, this model precisely identifies and isolates investor announcements and corporate disclosures from Arabic newspaper pages. The system effectively distinguishes financial notices from regular news content, advertisements, and other page elements, enabling automated monitoring of company announcements in the Kuwaiti financial market.
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A tool that reads images of GUIs and predicts the coordinates of clickable points based on user queries. It enables intuitive interaction with interfaces by combining visual understanding and natural language commands.
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An AI model that detect and localize bone fracture spots in X-ray images. It makes easier to focus on target fractured spot faster in X-ray scan report.
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An advanced video analytics solution that seamlessly transforms video content into actionable insights. The system processes video inputs through a multi-stage pipeline: first converting video to high-quality audio, then employing state-of-the-art speech recognition for accurate transcription. The platform leverages Retrieval-Augmented Generation (RAG) technology to create a knowledge base from the transcribed content, enabling contextual understanding and intelligent question-answering capabilities. Users can inquire about any aspect of the video content, receiving precise, context-aware responses enhanced by RAG's ability to reference specific segments of the video transcript. This creates a dynamic, interactive experience where users can explore and extract insights from video content through natural language queries.
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Description
A vision-language model that accepts an image as input and provides detailed answers to queries about the image. It supports multiple output formats, including JSON and markdown, and offers thorough image descriptions. It leverages the current best model for image-to-text use cases, ensuring accuracy and versatility in interpretation.
Tools/Technologies
Description
This Gradio application features a user-friendly tabbed interface for exploring four of the best open-source text-to-speech (TTS) models. Users can select from a variety of models, each showcasing unique voice qualities, languages, and capabilities. The interface allows users to input text, select their desired TTS model, and listen to the generated speech output in real time. This application aims to provide a seamless and interactive experience for those looking to experiment with different TTS technologies for various applications.
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Description
A Facebook model demo implementing the paper 'Better and Faster LLM via Multi-Token Prediction.' This model enables faster inference through self-speculative decoding.
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Speeds up the inference of LLMs when processing inputs with more tokens. Supports specific models: LLaMA-3-1M, GLM4-1M, Yi-200K, Phi-3-128K, and Qwen2-128K.
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Demo with different tasks using speech to text, including audio file to transcription, microphone to transcription, live stream transcription, YouTube link to transcription, translation, and transcription with time-stamping.
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Description
Creates a VLLM server for the Codestral model, establishing an endpoint for using the model similar to OpenAI API calls. Codestral model is best for coding-related tasks, particularly text to code.
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Utilizes WhisperX diarization to identify the number of speakers in an audio recording and capture their dialogues. This system allows for the naming of speakers and generates a transcription that includes speaker names along with their respective dialogues.
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Description
Employs LlamaIndex's built-in agentic RAG (Retrieval-Augmented Generation) methods, including L1—a query engine mechanism for selecting tools, L2—directly passing tools to the LLM, L3—a ReAct agent (Reason and Action agent), and L4—support for processing multiple PDFs on ReAct agent.
Description
Allows designing and executing advanced diffusion pipelines using a graph/nodes/flowchart-based interface. Provides full control over the pipeline of diffuser models like CLIP, UNets, ControlNets, VAE.
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A Gradio UI for training diffuser adaptors like LoRA, DreamBooth, and textual inversion. It includes tabs for specifying training parameters, captioning, and testing trained models.
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Develops a user interface (UI) for uploaded forms in JPG or PDF format. This system replicates the form structure in JSON and generates UI based on that structure.
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It is an user interface (UI) for image annotation, which is essential for creating datasets for image detection models. This tool effectively manages annotation tasks for large datasets and serves as an alternative to Roboflow.
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A GUI for using diffuser models, providing full control over the pipeline of diffuser models like CLIP, UNets, ControlNets, VAE, and adapters like LoRA and textual inversion.
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A framework for building voice conversational agents, such as personal coaches, meeting assistants, customer support bots, intake flows, and social companions.
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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 ensures 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:
Risk reduction through controlled testing
Early identification of technical challenges
A clear understanding of data requirements
Accurate resource planning
Team capability development
Evidence-based decision making
Stronger stakeholder support
Better scaling preparation
What are Essential Components of An AI Poc?
Problem Statement
Clear definition of the business challenge, desired outcomes, and scope. Must be specific enough to measure success but narrow enough to test quickly and effectively.
Data Strategy
Plan for data collection, processing, and management. Your AI POC implementation requires quality data assessment, preparation methods, storage solutions, and handling of both training and testing dataset.
Model Selection
Choosing the right AI approach based on your problem, data, and requirements. Consider factors like accuracy needs, processing speed, and resource constraints.
Success Metrics
Quantifiable measures to evaluate PoC performance. Include both technical metrics (model accuracy, speed) and business metrics (cost savings, efficiency improvements).
Timeline Planning
Clear definition of the business challenge, desired outcomes, and scope. Must be specific enough to measure success but narrow enough to test quickly and effectively.
Resource Allocation
Identification of necessary technical, human, and financial resources. Includes computing infrastructure, team expertise, and budget requirements.
Testing Framework
Structured approach for validating model performance. Includes test cases, validation methods, and procedures for handling edge cases.
Documentation Plan
System for recording technical specifications, decisions, results, and learnings. Essential for knowledge transfer and scaling decisions.
Evaluation Criteria
Framework for assessing PoC success. Combines technical performance, business impact, and feasibility for full-scale implementation.
Stakeholder Input
Process for gathering and incorporating feedback from key stakeholders throughout the PoC development and testing phases.
Implementation Framework of an AI PoC
Successful AI PoC development follows a structured framework:
Discovery Phase
Understanding your business needs and defining success. This includes gathering requirements, identifying stakeholders, and setting clear objectives for your AI PoC. We assess your current data landscape and determine technical feasibility.
Planning Phase
Creating the roadmap for your PoC development. We establish timelines, allocate resources, and define specific milestones. This phase includes selecting appropriate AI models and setting up the development environment.
Development Phase
Building your AI solution through iterative development. Starting with data preparation and model training, we focus on creating a working prototype that addresses your core requirements. Regular checkpoints ensure we stay aligned with objectives.
Testing & Validation
Rigorous testing of your AI solution against defined success metrics. We validate both technical performance and business value, ensuring the solution meets quality standards and delivers expected results.
Review & Analysis
Comprehensive evaluation of PoC results. We analyze performance data, gather stakeholder feedback, and document key findings. This phase helps determine the viability of scaling to a full implementation.
Implementation Framework of an AI PoC
This framework ensures a structured approach while maintaining flexibility to adapt to your specific needs and challenges.
How F22 Labs Can Help You Create The Best AI Poc
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
We follow a structured yet flexible approach to AI PoC development:
Initial consultation and problem definition
Data assessment and preparation strategy
AI model finetuning
Rapid prototyping and testing
Clear communication and progress tracking
Why Choose F22 Labs?
Deep Technical Knowledge : Our team stays current with the latest AI technologies and best practices, ensuring your PoC leverages the most appropriate solutions for your needs.
Result-Driven Approach : We focus on delivering measurable business value. Every PoC we develop includes clear success metrics and performance indicators aligned with your business goals.
Proven Track Record : Our portfolio includes successful AI PoCs across various industries, demonstrating our ability to handle diverse business challenges effectively.
Our Support and Guidance
We follow a structured yet flexible approach to AI PoC development: