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Demonstrations on mcube.ai
  • Hybrid Gen-AI – combining RAG & Knowledge Graphs
  • Multi-modal AI
  • LLM’s to capture fraud transactions
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Auto EDA to MLOPs
  • Extended Auto EDA with customization capabilities to fine-tune analysis
  • Auto-ML for accelerating ML model development
  • MLOps to move AI models from PoC to large-scale production, ensuring fast value realization 

Multi-modal AI

Our AI workbench supports multi-modal workflows using Traditional AI, Deep Learning, Computer Vision, Gen-AI, all accessible from a low-code front-end.
Generative AI in Action

Hybrid Gen-AI Search – Combining RAG & Knowledge Graph

By combining RAG with Knowledge Graphs, this hybrid search solution enables more contextual and accurate information retrieval...

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Test Case Creation using GenAI – LIMS Test Method Validation

The solution automates LIMS test method validation, leveraging a multimodal Large Language Model (LLM) to enhance accuracy and compliance...

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RAG – Enterprise
Data Search

Our Retrieval-Augmented Generation (RAG) product revolutionizes enterprise search by transforming unstructured data into a searchable knowledge base...

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GenAI - Powered
Fabric Design

The Gen-AI-powered fabric design system merges traditional saree aesthetics with advanced AI, using GANs and Neural Style Transfer to create unique patterns...

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Accelerating Catalyst Formulation with Generative AI

This solution accelerates hydrocracker catalyst formulation using Generative AI, Design of Experiments (DOE), and optimization models...

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Agentic Architecture – AI Agents and Text-to-Query Agents

In the context of generative AI, agents serve as autonomous intermediaries, handling tasks like data extraction, interpretation, decision-making, and ...

Business Intelligence
Turn complex data into clear, visually intuitive insights, to make informed, strategic decisions with confidence
  • Data insights by employing statistical methods to uncover patterns, trends, and exceptions within datasets.
  • Data visualization done by means of an extensive library of charts, graphs, and self-service dashboards.
  • Reporting done by delivering regularly scheduled or on-demand summaries of KPI’s and findings.
  • Operational reporting for compliance and regulatory requirements.

Hybrid GenAI Search – Combining RAG & Knowledge Graph

By combining RAG with Knowledge Graphs, this hybrid search solution enables more contextual and accurate information retrieval. This approach enhances search relevance and provides a deeper understanding of data relationships, perfect for advanced analytics needs in enterprises. In a recent case study involving biomedical data, we applied both RAG and knowledge-based information retrieval approaches to achieve more accurate and complete answers. This combined method is especially beneficial in use cases where the accuracy and completeness of answers are critical to the business process—much like how ensemble models improve prediction accuracy in machine learning.

Test Case Creation using GenAI – LIMS Test Method Validation

Leverage our GenAI-powered test case generation tool to automate the creation and validation of test methods in Laboratory Information Management Systems. By integrating AI models and a private multimodal Large Language Model LLM, this solution significantly reduces manual effort while enhancing accuracy, consistency, and compliance across various validation steps.

The LLM plays a crucial role by interpreting diverse data sources—such as text documents, method protocols, technical standards, and structured data within LIMS—allowing it to cross-validate test methods, parameter lists, and configurations. This intelligent automation ensures that the entire process adheres to industry standards, improving operational efficiency and accuracy.

Test Method and Naming Verification:
The solution begins by validating naming conventions and parameter lists for test methods by analyzing the Naming Guide and comparing it with LIMS metadata. The LLM flags inconsistencies, ensures all required fields (e.g., Parameter List ID, Repeat Count, Equipment links) are completed correctly, and confirms compliance with naming standards, reducing human errors and maintaining procedural accuracy.

Parameter and Configuration Validation:
The solution extracts relevant parameters (e.g., Data Type, Units, Replicates) from the test method documentation and compares them to LIMS configurations. This guarantees that the parameter structures align with the test method flow, ensuring data consistency. The LLM also automates sample creation in LIMS to validate data integrity and alignment.

Consumables and Instrument Integration:
The multimodal LLM interprets the test method documents to identify the required consumables and instruments, verifying their availability and status (active or expired) within LIMS. This ensures that only available, compliant items are used in test setups, further enhancing accuracy and efficiency.

Validation of Conditional Statements and Calculations:
One of the most complex tasks involves verifying conditional logic and calculated components within test methods. The LLM extracts parameters, rules, and conditions from LIMS master data and generates test data to validate various pathways. Through simulations (using Design of Experiment or LLM methods), it checks for boundary conditions and exception cases, ensuring that calculations and workflows operate as expected.

Unit Consistency and Standards Compliance:
The LLM ensures that the units in Lab Vantage result parameters match the technical standards, preventing errors caused by incorrect units. In cases requiring manual calculations, such as using the Master Verification template, the LLM automates complex calculations, generates reports, and uploads them as evidence for documentation.

In summary, this end-to-end automated solution provides seamless test method validation in LIMS by integrating AI to handle both structured and unstructured data. Its ability to validate workflows, interpret complex calculations, and ensure regulatory compliance significantly reduces human error, boosting efficiency and ensuring accurate test method verification.

RAG – Enterprise Data Search

Our Retrieval-Augmented Generation (RAG) product revolutionizes enterprise search by transforming unstructured data into a searchable knowledge base. With advanced parsing, embedding creation, hosting LLMs privately and a dynamic user interface, this tool empowers businesses to extract insights from vast unstructured datasets with ease.

To battle the hallucination and boost the credibility of generated answers this TCG RAG solution also provides the references to the answers being generated. Hosting LLMs privately ensures that your data and IP is secure.

GenAI-Powered Fabric Design

Innovative Fabric Patterns creation:
Leverages Generative Adversarial Networks (GANs) and Neural Style Transfer to create unique fabric that blend traditional aesthetics with modern AI technology.

Neural Style Transfer Algorithm:
  • Merges content images (base fabric design) with style images (textures, color schemes, patterns).
  • The content image provides structure and layout, while the style image contributes aesthetic features.
  • The algorithm minimizes the difference between the original images and the generated design, preserving the fabric’s base structure and integrating artistic styles.
User Interface (UI):
  • Allows image uploads for both content and style images.
  • Provides sliders to adjust the ratio between “content contribution” (retaining original design) and “style contribution” (influencing the design with style elements).
  • Designers can preview real-time iterations, experimenting with different settings before finalizing a design.
Creative Flexibility:
  • Utilizes StyleGAN for high-quality image synthesis.
  • Uses Cycle GAN for unpaired style transfer, enabling transformations without the need for paired data.
Workflow:
  • Fabric image collation, pre-processing, image classification, model training, validation, and a design feedback loop.
  • Enables designers to create high-quality synthetic saree samples that blend tradition with cutting-edge innovation.
Result Images
TCG Digital

Accelerating Catalyst Formulation with Generative AI

Hydrocracker catalysts are carefully optimized for selectivity vs. activity, balancing these properties to achieve high efficiency in refining processes. These catalysts typically consist of three key ingredients: amorphous Si/Al, crystalline Si/Al, and base metals. To accelerate the formulation process, a combination of Generative AI, Design of Experiment and optimization models is employed:

LLM Fine-tuning for Knowledge Retrieval:
The first step involves fine-tuning a Large Language Model (LLM) on domain-specific documents and research papers related to catalyst formulation. This accelerates the knowledge retrieval process, allowing experts to quickly explore various ingredient combinations. By integrating historical data, the LLM can suggest optimal ratios of amorphous Si/Al, crystalline Si/Al, and base metals, while rejecting infeasible combinations early in the process. This dramatically reduces the time spent on trial-and-error formulations.

Hypothesis Generation with DOE and AI Validation:
The next step is hypothesis generation through Design of Experiments (DOE), where multiple combinations of the catalyst ingredients are generated. The LLM, fine-tuned for the catalyst domain, is then used to assess these combinations, eliminating infeasible hypotheses before they move forward to experimental validation. This AI-driven pre-screening ensures that only the most promising formulations are tested, saving time and resources.

Optimization Model for Selectivity and Activity:
To fine-tune the catalyst for optimal selectivity and activity, historical experimental data is utilized to build an optimization model. This model considers previous test results and the properties of the ingredients, helping to refine the formulation process. The goal is to achieve the best balance between selectivity and activity, ensuring that the catalyst performs efficiently under the specific conditions of hydrocracking.

Agentic Architecture – AI Agents and Text-to-Query Agents

In the context of generative AI, agents serve as autonomous intermediaries, handling tasks like data extraction, interpretation, decision-making, and even query formulation. Unlike traditional automation, agents are intelligent—they can adapt, learn, and make decisions based on the context of the data they process.

How Agents Revolutionize Generative AI:
Agents are set to transform generative AI by introducing an autonomous layer that can interpret the human query and plan the tasks for downstream agents, interact with both structured and unstructured data, bridging the gap between raw data and human-intelligible insights. With agents, users don’t need deep technical knowledge to harness the power of AI. Agents can interpret complex instructions, interact with databases, and generate valuable outputs without needing to understand the underlying code or architecture.

AI Agents – Understanding Unstructured Data:
AI Agents specialize in understanding and interpreting natural Language. Execute basic AI task like plotting trends, developing predictive models etc.

Text-to-Query Agents – Simplifying Interaction with Structured Data:
Text-to-Query Agents enable non-technical users to interact with structured datasets (like databases) using natural language instead of technical query languages. These agents convert human-readable questions into machine-readable queries and retrieve the desired information from structured sources.

Why Agents Matter in Generative AI:
Agents introduce an intelligent, user-friendly layer between complex datasets and the people who need to interact with them. Whether it’s extracting insights from unstructured data or querying structured data.