Discover how Black Dome cut work order costs by 80% using Lucidis AI—a powerful platform for transforming unstructured data into structured insights.

The experts: Binni Skariah, CEO of Lucidis, and Tobalo Torres, VP of AI Incubation Labs at WhitegloveAI.

The organization: WhitegloveAI, a small, innovation-focused tech firm, that helps businesses extract structured insights from unstructured data. Their client, Black Dome, a property preservation company managing repairs and maintenance for foreclosed properties.

The problem: Black Dome faced “data chaos” from receiving work orders in fragmented formats—PDFs, images, and emails. Manual data entry was inefficient, costly, and error-prone, creating workflow delays and reducing overall productivity.

The solution: Automated ingestion, cleaning, and structuring of unstructured data, integrating seamlessly into Black Dome’s existing systems, with a natural language chatbot interface.

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Key decisions

Replace outsourced data processing with an in-house AI-powered workflow
Reduce manual workflow steps from eight to two
Build a secure, cloud-agnostic architecture for scalable implementation

Key results

80% cost savings through internal automation
Processing times for work orders reduced from 30–40 minutes per order to a simple document upload
Improved data accuracy and reliability of work order instructions
Simplified employee experience, shifting focus to higher-value tasks

1. The Data Chaos Problem

An examination of the pervasive issue of data chaos, the high costs of manual data processing, and the need for scalable, AI-powered solutions.

2. The Black Dome Case Study

In-depth analysis of Black Dome's operational pain points before Lucidis integration: inconsistent data formats, labor-intensive data entry, and high outsourcing costs. Quantitative impact of AI automation, including the elimination of manual data entry, 100% accuracy, an 80% reduction in work order processing time, and regained control of outsourced tasks.

3. Data Processing Automation for Your Business

Framework for building scalable, AI-powered data pipelines with technologies like machine vision and NLP to manage unstructured data. Key steps for automating document ingestion, standardization, and structuring to support downstream applications and AI initiatives.

4. The Technology Stack

Detailed breakdown of Lucidis's core tech components and a comprehensive repository of popular tools for each step of the process.

5. Challenges and Solutions

Challenges Lucidis encountered when implementing their solution for Black Dome —such as downstream API integration and GPU-intensive data cleaning, and how they overcame them.

6. Future Innovations at Lucidis

Exploration of Lucidis's technical roadmap, including the integration of Graph RAG for knowledge-graph-driven insights and agentic tooling for autonomous data handling.

7. Cross-Industry Needs and Emerging Trends in Data Governance

Application examples from the legal, financial, healthcare, and government sectors, illustrating the use of machine vision and NLP for data compliance across industries.

8. DIY versus External Solutions

Key considerations for tech leaders: comparing the technical and financial demands of building an in-house solution versus adopting an external data cleaning platform.

9. Conclusions

A final reflection on key lessons from the Black Dome implementation and broader implications for industries facing unstructured data challenges.

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Meet the Experts

Binni Skariah

Strategic Advisor at WhitegloveAI

“One of the problems with introducing AI tools into a testing workflow is that you can show them to your colleagues, see them play with it, but they will never open it again. The initial novelty doesn’t transition into a sustained use. But if you implement it inside the interface, within their workflow, it will work.” -- Kirill Markin, Former Head of R&D at SOAX Kirill Markin brings over 12 years of experience in AI, data science, and business automation, with a proven ability to transform technical innovation into practical, impactful solutions. As a driving force behind SOAX's AI-powered customer support system, Kirill played a pivotal role in designing and implementing scalable architectures that balanced efficiency, adaptability, and cost-effectiveness. Kirill’s technical expertise includes prompt optimization, pipeline automation, and developing modular AI workflows to streamline operations. His innovative use of task-specific models and real-time feedback mechanisms ensured SOAX's AI systems remained secure, agile, and aligned with business goals. Describing himself as a “data monkey”, Kirill thrives on experimenting with emerging technologies and continually pushing the boundaries of AI-driven solutions. His hands-on leadership has solidified SOAX’s position as a leader in intelligent customer support automation.

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Tobalo Torres

AI Incubation Labs at WhitegloveAI

With a background in enterprise architecture and cloud engineering, Tobalo leads the engineering initiatives for Lucidis. His experience with large language models, edge computing, and data pipelines is instrumental in developing Lucidis's ability to clean, structure, and process unstructured data. Tobalo is passionate about using technology to improve efficiency and solve real-world problems for businesses. His leadership and technical expertise are crucial in driving Lucidis's product development and ensuring its scalability and security for enterprise-level deployments

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Generative AI for 80% Reduction in Work Order Processing Time (test)