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Gen AI

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Leveraging Gen AI: Xgrid's Enterprise Implementation

At Xgrid we are developing RAG-based models, scaling LLM applications for enterprises, and creating generative AI solutions to transform businesses.

Key Differentiators

  • Iterative Proof of Concept (POCs): Developing interactive and workable prototype collaborating with clients to refine models using feedback and data.
  • Hybrid Expertise: A team blending Gen AI engineers with individuals possessing deep tech-sector knowledge.
  • Security & Compliance-Focused: Prioritizing data protection aligned with industry regulations.

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Key Focus Areas

  • Retrieval Augmented Generation (RAG) Developing RAG-based solutions to enable contextualized interaction with external sources of data.
  • Cloud Infrastructure Design Deploying Gen AI Applications at Enterprise Scale.
  • Natural Language to Database Querying (NL2SQL) Designing and developing solutions for interaction with enterprise scale SQL databases using natural language.

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Scope of Work at Xgrid

RAG (Retrieval-Augmented Generation) 

Cloud Infrastructure Design

Natural Language to Database Querying

Business Use Case

Enabling users to interact with documentation using LLMs and gain contextualized insights.

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Business Use Case

Manual Terraform code writing by cloud architects is time-consuming and error-prone.

Business Use Case

Enable business users without technical skills to access and utilize valuable data resources.

Solution

DocBot, an AI assistant that uses natural language to help users find information in documents and external sources.

Solution

Draw-TF Tool - Trained on Terraform syntax to generate code from architect diagrams, reducing errors and saving time.

Solution

Fintech Data Access Chatbot translating natural language into SQL databases for informed decision making

Outcome

Better decision-making, enhanced platform UX, potential customer gains.

Outcome

Reduced errors, and reduced cloud resource deployment time.

Outcome

Faster issue resolution, improved information accessibility, higher customer satisfaction.

Technical Approach

Employs RAG models extracting knowledge.

Utilizes semantic search techniques to find relevant information within complex text.

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Technical Approach

A diagram is converted into a json file. Information is then extracted by a series of functions and fed to the backend genAI application in a feedback loop to generate terraform code that's ready to plan.

Technical Approach

Uses GenAI for language understanding and query conversion.

Employs Lang chain for AI & Database interaction.

Utilizes Oracle for secure, scalable data handling.

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Scope of Work at Xgrid

Business Use Case

On-premise data processing needed to generate secure, department-wide insights while complying with data security policy.

Business Use Case

Using LLMs to analyze data in an environment with limited internet access.

Solution

Developed a application that empowers users to gain summaries, reports, and data-driven insights from the company's dataset.

Solution

An application was deployed on a locally hosted server, interacting with company data to provide contextualized answers from large datasets.

Multi-Agent OnPrem RAG

Enterprise Scale Observability in OnPrem LLM Applications

Outcome

Outcome

Contextualized insights enhanced visibility & capacity planning, while automated updates improved project health summary accuracy.

Technical Approach

Fine-tuned multi-agent RAG model generates reports & insights from diverse stats, integrates with Google Workspace & Github.

Technical Approach

Utilizing a locally hosted Mistral 8x7B instance with accuracy and hallucination filters for contextualized responses, alongside a MoE model with enterprise-scale LLM response observability.

Boosted team productivity with internal data access, and reduced LLM hosting costs by 99.9% while ensuring security compliance.

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DocBot: Boosting Customer Support with Intelligent Knowledge Extraction and Generation

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Use Case

  • Need to simplify user interaction with complex company documentation. Manual methods were time-consuming and inefficient.

Solution

  • Developed DocBot, a RAG-powered conversational AI tool trained on the specific company knowledge base.

Business Impact

  • Faster, 24/7 assistance freeing up support staff for complex issues
  • Reduced support ticket volume, customer queries and complaints
  • Increased customer satisfaction thanks to personalized support, and a 24/7 availability
  • Faster resolution times, and improved customer communication

Technical Aspects

  • Employs RAG models specializing in extracting knowledge from the company's documentation.
  • Utilizes semantic search techniques to locate the most relevant information within complex text.

Asia Pacific-Based

  • Automation-driven company delivering enterprise solutions in cloud network management

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Draw-TF: Automate Infrastructure Design – Visualize, Generate, Deploy

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Use Case

  • Improve efficiency and accuracy in cloud infrastructure provisioning. Current processes, relying on manual Terraform code writing by cloud architects, are time-consuming and error-prone.

Solution

  • Draw-TF streamlines infrastructure provisioning. It leverages AI models trained on Terraform syntax to generate code from architect diagrams, reducing errors and saving time.

Silicon Valley Tech Leader

  • Providing networking security, collaborate, cloud management and IoT solutions.

Business Impact

  • Reduce time-to-deployment for new cloud resources by automating code writing.
  • Visual diagrams facilitate communication between architects and development teams.
  • Helps enforce infrastructure best practices through consistent code generation.

Technical Aspects

  • Employs a language model trained on Terraform syntax and common cloud architecture patterns.
  • Leverages a specialized parser to extract the cloud components and their relationships from the diagram.
  • The model translates the interpreted diagram into Terraform code, ensuring it follows best practices and desired configurations.

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Streamlining Fintech Decision-Making: NL2SQL Chatbot Powered by AI & Oracle

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Use Case

  • Build a data access chatbot for non-technical SMB/Startup clients of the fintech platform.

Solution

  • Chatbot integrating OpenAI, Langchain, Oracle tools, and Python for implementation.

Business Impact

  • Designed to increase data-driven decision-making for users, enhanced UX of the fintech platform.
  • Improved client satisfaction and retention by providing a valuable self-service tool.
  • Non-technical business users can get insights without needing SQL expertise or waiting for data team assistance.
  • Improved decision-making speed and efficiency by enabling users to interact with data independently.

Technical Aspects

  • Leverages generative AI models for natural language understanding and query translation.
  • Employs Langchain to facilitate interaction between AI models and the database.
  • Utilizes Oracle technologies for secure and scalable data storage and execution.

A US-Based Retail Company

  • With multiple retail and warehousing operations.

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Report Generator: Contextualized Reports from Locally Hosted Models

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Use Case

  • Need to generate insights from data for multiple teams in the organization. Data security policy requires on-premises processing.

Solution

  • Developed an application for deriving summaries, reports, and insights from various sources from the company’s dataset.

Business Impact

  • Contextualized insights lead to increased visibility and better capacity planning.
  • Increased accuracy in project health summaries through automated updates.

Technical Aspects

  • Employs a fine tuned multi-agent RAG model for generating reports and insights from various statistics.
  • Multi-Agent workflows seamlessly with multiple workspace integrations i.e. Google Workspace, and Github.

Asia Pacific-Based

  • Enterprise specialized in delivering secure cloud infrastructure, user interface & experience solutions.

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On-Prem Chat with Your Data Application

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Use Case

  • Need to interact with data with LLMs in an environment where public internet access is restricted.

Solution

  • An application deployed on a locally hosted server that interacts with company data to give contextualized answers from large datasets.

Business Impact

  • Enhanced productivity of development teams by allowing quick lookup from multiple sources within organization data.
  • 99.9% reduction in application costs compared to cloud-hosted solution, in addition to compliance with company’s data security policy.

Technical Aspects

  • Utilization of locally hosted Mistral 8x7B instance RAG model with accuracy and hallucination filters for generating contextualized responses.
  • Utilization of a MoE model with agents, and enterprise scale LLM response observability solutions.

Asia Pacific-Based

  • Enterprise specialized in delivering secure cloud infrastructure, user interface & experience solutions.

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Xgrid’s Developmental Roadmap: Phases of Project Implementation

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Data Acquisition

Development & Refinement

Implementation & Deployment

Client Data Acquisition

  • Align with client requirements, discuss business objectives, and procure data relevant to the specific business goal at hand.

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Client-Centric Data Audit

  • Assess quality, structure, volume, and potential biases of client data across domains with a focus on understanding the specific business problem.

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Solution Architecture Design

  • Develop solution architecture, iteratively refine them for precision and align with desired outputs.

Robust Pipelines

  • Build secure pipelines to centralize data from diverse sources, incorporating cleaning, transformation, and best practices for storage, automation and security.

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Data Security

  • Establish data governance protocols in collaboration with the client, addressing privacy, security, and compliance requirements during the development phase.

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Testing and Evaluation

  • Test solutions while incorporating feedback to drive continuous refinement and ensure they meet specific needs and performance expectations.

User Analysis

  • Introduce analytics and observability into the application stack to identify usage patterns and gain meaningful insights

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Deployment & Monitoring

  • Integrate Gen AI solutions into existing workflows, monitor performance, and use feedback for refinements.

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Security & Compliance Focus

  • Work with the client to develop protocols for managing data governance, focusing on ensuring privacy, security, and adherence to regulatory standards, during and after the implementation phase.

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Building Blocks Of Xgrid Services