MindCraft

Project Overview

The AI Policy Compliance Checker is an enterprise-grade solution designed to automate the rigorous process of verifying document adherence to internal and external regulations. By replacing manual audits with an intelligent analysis engine, this agent provides instant, objective insights into policy alignment, helping organizations maintain the highest standards of governance and risk management.

The Challenge

In complex corporate environments, ensuring that every contract, report, or internal memo aligns with constantly evolving policies is a massive task. Manual reviews are slow, expensive, and prone to human oversight—especially when identifying subtle inconsistencies or "hidden" risks across hundreds of pages. The goal was to create a "digital auditor" that could provide real-time validation without disrupting existing workflows.

Our Solution: The Intelligent Audit Pipeline

We developed a sophisticated text-analysis framework that acts as a 24/7 compliance officer:

• Deep Semantic Policy Analysis: Users can upload core policies or input regulatory standards. The AI engine analyzes them for clarity, identifies potential ambiguities, and builds a "logic map" of requirements.

• Automated Cross-Verification: When a new document is uploaded, the system performs a multi-layered check against defined policies, identifying specific clauses that are non-compliant or require revision.

• Risk-Based Highlighting: Instead of generic alerts, the agent categorizes findings by risk level, allowing legal and compliance teams to focus on the most critical violations first.

• Actionable Reporting Engine: The tool generates concise, data-driven reports with specific recommendations for improvement, transforming "rejections" into clear action items.

Key Benefits & Innovation

• Subtle Risk Detection: Leverages advanced NLP to identify nuanced language or "grey areas" that manual reviews often miss.

• Seamless Workflow Integration: Designed as a plug-and-play solution that can be integrated into existing document management or legal tech stacks.

• Proactive Governance: Shifts compliance from a "reactive" check to a "proactive" strategic advantage.

• High Efficiency: Processes thousands of words in seconds, allowing for 100% document coverage instead of periodic sampling.

Key Results & Impact

• Drastic Risk Mitigation: Minimized the risk of regulatory fines and internal policy breaches through automated, high-precision oversight.

• 80% Reduction in Review Time: Freed up valuable time for legal and compliance departments, allowing them to focus on high-level strategy rather than routine vetting.

• Consistency Across Departments: Ensured that policy interpretation remains uniform across different branches and international offices.

• Data-Driven Oversight: Provided management with clear trends and analytics on common compliance pitfalls.

Project Overview

The AI Travel Agent is a sophisticated trip-planning ecosystem designed to transform how travelers discover and organize their journeys. Moving beyond static search engines, this solution utilizes a "Multi-Agent" architecture where specialized AI agents work together to handle destination research, real-time budgeting, and activity curation. A standout feature is the Live Intelligence Stream, which allows users to observe the internal reasoning and collaboration of the agents as they build the itinerary in real-time.

The Challenge

Travel planning is traditionally fragmented, requiring users to juggle multiple tabs for flights, hotels, and local attractions, often leading to "decision fatigue." Existing automated planners often produce generic results that ignore the traveler's specific lifestyle or real-time budget constraints. The goal was to create a unified, transparent, and hyper-personalized platform that mimics a high-end human travel consultant.

Our Solution: The Collaborative Multi-Agent System

We developed an intelligent framework where individual AI entities take on specialized roles:

• The Researcher Agent: Scours vast datasets and travel APIs to find destinations that match specific regional preferences (Europe, Asia, etc.) and interests (beaches, historical sites, etc.).

• The Financial Strategist Agent: Generates comprehensive budget breakdowns, including accommodation, transit, and daily dining, ensuring the plan stays within the user's financial parameters.

• The Experience Curator Agent: Tailors activity recommendations based on the number of travelers and duration, focusing on unique cultural experiences.

• Live Dialog Interface: We implemented a unique transparent UI where users can see the "conversation" between agents, providing insight into why certain choices were made.

Key Features & Innovation

• Holistic Destination Overviews: Provides deep-dive insights into local culture, weather patterns, and must-see attractions.

• Dynamic Budget Allocation: Real-time estimation of costs that helps travelers maximize their experiences without overspending.

• Interconnected Planning: Unlike standard tools, the agents check for logical consistency (e.g., ensuring travel times between activities are realistic).

• High Degree of Personalization: The system learns from user inputs to suggest unique hidden gems rather than just popular tourist traps.

Key Results & Impact

• Reduced Planning Time: Users can generate a complete, 14-day itinerary with a full budget in under 3 minutes, compared to hours of manual research.

• Increased User Trust: The "Live Dialog" feature significantly boosted user engagement and trust by demystifying the AI’s decision-making process.

• Optimized Travel Spend: Our budgeting engine identified cost-saving opportunities for users, improving their "experience-per-dollar" ratio.

Project Overview

The Smart Trash Bin Agent is a next-generation AI-powered assistant designed to automate and optimize urban waste management. By integrating advanced computer vision with Large Language Models (LLMs), this innovative system identifies, classifies, and guides the sorting of recyclables in real-time. This project represents a significant leap toward sustainable "Smart City" infrastructure, bridging the gap between consumer behavior and effective recycling.

The Challenge

Despite global recycling efforts, cross-contamination remains a major hurdle in waste management. Consumers often find recycling categories confusing, leading to misplaced items that compromise entire batches of recyclable materials. The challenge was to create an intuitive, high-accuracy interface that removes the guesswork from waste disposal at the point of contact.

Our Solution: The Vision-AI Integrated Bin

We engineered a seamless interaction between hardware and cutting-edge AI to ensure flawless sorting:

• Real-time Visual Recognition: Equipped with an integrated high-definition webcam, the bin captures the item's visual data the moment it is presented

• OpenAI-Powered Analysis: Leveraging the semantic and visual capabilities of OpenAI's models, the system instantly identifies the material (e.g., PET plastic, aluminum, corrugated cardboard) and its specific recycling requirements.

• Interactive Guidance System: A sleek, user-friendly interface provides immediate feedback, confirming the item type and directing the user to the correct compartment.

• Human-in-the-Loop Confirmation: To ensure 100% sorting integrity, the system prompts a quick user confirmation before the automated disposal mechanism activates.

Key Features & Innovation

• Unmatched Identification Accuracy: The AI engine is trained to recognize a vast array of consumer packaging, even when deformed or partially obscured.

• Instant Feedback Loop: Processing happens in milliseconds, ensuring that the smart sorting process does not slow down the user experience.

• Sustainability Analytics: The system can track and report recycling metrics, providing valuable data for corporate sustainability goals or municipal waste monitoring.

• Scalable Architecture: The software-defined nature of the agent allows for remote updates to include new waste categories as recycling regulations evolve.

Key Results & Impact

• Reduced Contamination: Significantly improved the purity of recyclable streams by providing real-time education and sorting at the source.

• Promoted Environmental Responsibility: Increased user engagement with recycling programs through a gamified and high-tech interface.

• Future-Ready Infrastructure: Provided a scalable blueprint for smart waste management in malls, airports, and corporate campuses.

Project Overview

We developed a disruptive AI solution for the digital publishing industry that automates metadata extraction and content analysis from massive PDF datasets. By utilizing Large Multimodal Models (LMMs), we eliminated the traditional requirement for manual data labeling, allowing companies to process thousands of complex documents with unprecedented speed and surgical precision.

The Challenge

Digital publishers often deal with vast archives where manual data entry is the primary bottleneck. Industry standards for manual processing average 10 minutes per page at a cost of $0.10. The challenge was to create a system that could not only "read" these documents but also objectively analyze user feedback and extract features without the high costs and subjective biases associated with human review.

Our Solution: LMM-Driven Automation

Our approach leverages the latest advancements in Large Multimodal Models to handle complex document structures:

• Zero-Shot Metadata Extraction: The system identifies and extracts key features and metadata from PDFs without needing prior dataset labeling.

• Objective Sentiment Analysis: We trained the model to predict star ratings and analyze user feedback, providing a consistent, data-driven assessment that removes human subjectivity.

• High-Speed Processing Pipeline: The architecture was optimized to handle bulk uploads, transforming the workflow from minutes per page to hundreds of pages per minute.

• Adaptive Deployment: The solution features a flexible framework, allowing it to be tuned for different document complexities based on specific cost or timing constraints.

Key Results & Impact

• Massive Cost Reduction: Achieved a processing cost of $0.02 per page, which is 5x cheaper than the industry standard ($0.10) and significantly below our initial target of $0.05.

• Extreme Acceleration: While human processing takes 10 minutes per page, our AI processes 100 pages in just 2-3 minutes — a 500x increase in throughput.

• Superior Accuracy: The LMM consistently outperformed human analysts in predicting star ratings and extracting nuanced features from customer reviews.

• Predictable Quality: The technology ensures a stable and controlled level of data processing, eliminating the fluctuations in quality typical of manual workforces.

if you are looking to build an AI & ML solution like this, contact us [email protected]

Project Overview

This project introduces a revolutionary advancement in warehouse logistics: an AI-driven autonomous drone system designed for complex indoor environments. By replacing manual inventory checks with high-speed drone navigation, we’ve created a "store autopilot" that significantly reduces operational downtime and human error in large-scale retail and storage facilities.

The Challenge

Modern warehouses often face "dark spots" where traditional GPS cannot reach, making autonomous navigation difficult. Manual retrieval and inventory tracking are slow, prone to inaccuracies, and costly. The client required a solution that could navigate dense, multi-level shelving units without relying on external sensors or pre-installed infrastructure.

Our Solution: AI & 3D Mapping

We developed a sophisticated navigation stack that allows drones to "see" and "understand" their surroundings in real-time. The core of the system is based on:

• Direct Sparse Odometry (DSO): A cutting-edge visual SLAM (Simultaneous Localization and Mapping) algorithm that allows drones to map environments with high precision.

• Autonomous Flight Logic:

  • Intelligent Video Capture: High-resolution onboard cameras record the environment from multiple angles.

  • Real-time DSO Processing: The AI agent extracts visual cues to calculate movement and distance without GPS.

  • Dynamic 3D Environment Mapping: The system builds a live 3D digital twin of the warehouse, identifying landmarks, aisles, and obstacles.

  • Path Optimization: Drones use the generated maps to calculate the most efficient route for goods retrieval.

Key Results & Impact

• Zero-Infrastructure Setup: The DSO-powered system requires no beacons or external markers, reducing deployment costs by 60%.

• Enhanced Retrieval Speed: Drone-based retrieval is up to 3x faster than manual forklift operations in high-density areas.

• 24/7 Inventory Accuracy: Real-time 3D mapping ensures a 99.9% accuracy rate in locating stock within large facilities.

• Operational Safety: Advanced obstacle detection minimizes the risk of collisions, ensuring a safe environment for warehouse staff.

if you are looking to build an AI & ML solution like this, contact us [email protected]

Project Overview

This project involved the development of a high-precision AI system designed to track minute objects within dynamic thermal video streams. Unlike standard visual tracking, this solution focuses on identifying and following targets as small as a few pixels, providing mission-critical data in environments where traditional optical sensors fail.

The Challenge

Tracking tiny objects in thermal footage presents significant technical hurdles:

• Low Resolution: Targets often consist of only a few pixels, making traditional feature extraction impossible.

• Environmental Noise: Atmospheric turbulence and thermal "clutter" can easily obscure small heat signatures.

• Camera Instability: Even minor vibrations or movements can cause a loss of tracking for micro-targets.

Our Technical Approach

To solve these challenges, we implemented a multi-layered computer vision architecture:

• Thermal Signature Isolation: We leveraged thermal imaging to isolate objects based on heat signatures, effectively eliminating visual distractions caused by ambient light.

• Sophisticated Blob Detection: Our custom algorithm scans each frame to identify potential targets, filtering out irrelevant thermal noise to focus strictly on high-interest areas.

• Robust Object Tracking: Using advanced motion-prediction models, the system maintains track continuity even when objects are partially obscured or momentarily lost.

• Dynamic Image Stabilization: We integrated a robust stabilization algorithm that compensates for camera movement and atmospheric distortions, ensuring sub-pixel tracking accuracy.

Key Results & Impact

• Extreme Precision: Successfully tracks objects occupying less than 0.1% of the total frame area.

• Operational Reliability: Achieves consistent tracking performance in low-visibility and high-turbulence conditions.

• Versatile Application: The technology is scalable for use in defense, search and rescue, and industrial monitoring.

• Reduced False Positives: The dual-stage filtering process (Blob Detection + Noise Reduction) significantly minimizes false alarms in complex thermal environments.

if you are looking to build an AI & ML solution like this, contact us [email protected]

Project Overview

In the modern corporate landscape, companies handle massive volumes of unstructured data stored in Document Management Systems (DMS) or cloud repositories. We developed a sophisticated AI pipeline that transforms these static documents into organized, searchable, and actionable databases. By leveraging Large Language Models (LLMs) and advanced extraction techniques, we automate the classification and tagging of articles and corporate documents at scale.

The Challenge

The primary difficulty lies in the "unstructured" nature of human-readable documents. Manually sorting thousands of files by type or topic is labor-intensive, error-prone, and prevents companies from fully utilizing their data. The technical challenge involves accurately extracting text from diverse formats (PDFs, images, scanned reports) and maintaining high semantic accuracy during classification.

Our Solution: The Intelligent Extraction Pipeline

We implemented a multi-stage workflow to bridge the gap between raw files and structured information:

• Multimodal Content Extraction: We utilize a robust toolkit including Textract, NLTK, and LangChain to extract text from various file formats.

• Hybrid OCR Engine: For scanned images and embedded graphics, we employ a high-performance combination of Google Tesseract and cloud-based OCR services (AWS/Azure) to ensure near-perfect character recognition.

• LLM-Driven Classification: Extracted text is processed by Large Language Models to automatically identify document types (e.g., invoices, contracts, technical reports) and assign relevant thematic tags.

• Seamless DMS Integration: The system is designed to plug directly into existing Document Management Systems, automatically organizing files into the correct directories based on AI-generated metadata.

Key Results & Impact

• Eliminated Manual Sorting: Automated 100% of the initial document classification process, saving hundreds of administrative hours per month.

• Enhanced Searchability: By applying precise semantic tags, employees can now locate specific information across thousands of documents in seconds.

• High Extraction Accuracy: The hybrid OCR approach ensures data integrity even for low-quality scans or complex layouts.

• Scalable Knowledge Management: The system easily handles surges in document volume, allowing the company’s digital archive to grow without increasing overhead.

Project Overview

In an era of information overload, maintaining user engagement requires more than simple filtering. We developed a pioneering recommender system based on Knowledge Graph Attention Networks (KGAT). By transforming a traditional item-centric model into a high-performance hybrid architecture, we’ve enabled businesses to provide hyper-personalized experiences that adapt in real-time to both changing product catalogs and evolving user behaviors.

The Challenge

Standard recommender systems often struggle with "cold starts" or frequently changing inventories, failing to capture the complex relationships between users and items. Traditional KGAT models primarily focus on item-side information, often overlooking the rich, multifaceted characteristics of the users themselves. Our goal was to bridge this gap, creating a system that understands the "why" behind user interactions in highly dynamic environments.

Our Solution: The Hybrid KGAT Framework

We engineered a sophisticated graph-based model that treats data as a deeply interconnected web of relationships:

• Knowledge Graph Integration: The system handles complex data structures, mapping relationships between products, categories, and attributes within a unified network.

• Pioneering Hybrid Architecture: We evolved the standard KGAT by integrating user-side features directly into the framework. This allows the model to analyze user demographics and historical context alongside item attributes.

• Attention-Based Learning: The "Attention" mechanism assigns different weights to different neighbors in the graph, ensuring the system focuses on the most relevant connections for each specific recommendation.

• Dynamic Adaptation: Built specifically for businesses with rapid product turnover, the model updates its understanding as new items and user data enter the system, ensuring recommendations never go stale.

Key Results & Impact

• Enhanced User Retention: By providing more relevant, relationship-aware suggestions, the system drives deeper user engagement and long-term loyalty.

• Superior Cold-Start Performance: The knowledge graph allows the system to recommend new products effectively by linking them to known attributes and similar user profiles.

• Deep Semantic Understanding: Unlike "black-box" models, our hybrid KGAT provides a transparent understanding of how multifaceted user traits interact with complex item ecosystems.

• Scalable Real-Time Updates: Optimized for performance, the system maintains high accuracy even as the product database and user base scale.

Model is ready to be put into production and used for document classification, we received 99% of accuracy on the training data and around 90% on the test datasets

Benefits you'll get with this Engine:

• Classifying dozens of different document categories.
• Classification and recognition of handwritten documents and mixed-type document
• A system could automate various document processing

tasks which are now performed manually

If you are looking to build an AI & ML solution like this, contact us [email protected]

Technologies used:

Subject: Automated Document Classification with Machine Learning, Data extraction
Data Science areas: Natural Language Processing, Computer vision, Optical Character Recognition
Architectures: Logistic Regression, Random Forests
Tools: Python, Tensorflow, Sklearn, Tesseract

A Machine Learning system we developed for the customer can help achieve a significant Inventory Turnover Ratio and thus increase revenue per dollar invested in stock items

Benefits you'll get with this Engine:

• This approach applies to any retail business.
• Predict sales of any product in stock and boost the general Business Intelligence.
• The Inventory Turnover Ratio of 80% in two

weeks. 

If you are looking to build an AI & ML solution like this, contact us [email protected]

We managed to create a business intelligence system that not only predicts the prices of products but also measures its own accuracy

Benefits you'll get with this Algorithm: 

• Almost 95% of average relative accuracy for product pricing.
• Keep the error rate to the minimum
• Opportunity use engine in other domains, Retail, E-commerce, hospitality
• Individual engine development under

the business client.

If you are looking to build an AI & ML solution like this, contact us [email protected]

Technologies used:

Subject: Price Prediction Software, Prediction Accuracy Evaluation, Deep Learning
Data Science areas: Feature Analysis, Estimators, Machine Learning
Architectures: Linear Regression, Boosting Regressors, Deep Neural Networks
Tools: Python, Tensorflow, Sklearn

Subject: Automated Document Classification, Document Capturing, Data Extraction, AI Document Recognition
Business Areas: Banking, FinTech, Retail
Data Science Areas: Computer Vision, Machine Learning
Tools: Python, Tensorflow, Sklearn, Tesseract

MindCraft helped to automate the document capture and recognition for a client in the Banking industry using AI Document Recognition Software. The system can process documents for any domain and containing any kind of content, from handwritten text to fields and tables.