How Agentic AI Is Powering the Next Wave of Mining Intelligence

Mining is a high-stakes industry where companies rely on data to determine where to drill and how long to tap existing brownfield sites, maximize operational throughput, and protect multimillion-dollar equipment. Despite this reality, about 60% of mining professionals report having insufficient information to make data-driven decisions. Industry companies are investing in advanced data management, integrated reporting, and analytic decision support systems to support modern mining operations. As just one example, mining equipment downtime can cost $180K per incident, meaning that reducing these failures can add millions of dollars back into budgets annually. 

A leading natural ore and mining enterprise is initiating a strategic digital transformation project to build DigitalChatbot. This enterprise-grade, generative and agentic AI-powered chatbot platform addresses data silos, streamlines decision-making, and enables advanced business analytics across its operations, starting with the mining business and expanding to other domains.

Currently, the company’s data assets reside across disconnected enterprise and non-enterprise applications, resulting in significant inefficiencies in accessing insights, responding to leadership queries, and taking timely action. Teams rely on manual processes to generate reports, which creates delays. The reports lack cross-domain analytics and offer limited dashboard capabilities, hampering leaders’ and teams' ability to make holistic, data-driven decisions. In addition, existing tools are inadequate to manage the scale, speed, and complexity of semi-structured and unstructured data being generated as the business evolves.

Supporting Business Growth with a Generative and Agentic AI Reporting Chatbot 

To address these gaps, the company has created a DigitalChatbot as a unified conversational AI solution that combines:

• Generative AI (GenAI) for natural language understanding and dynamic content generation,
• Agentic AI (Text2SQL) to translate user queries into database queries to enable real-time structured data retrieval
• Advanced analytics to provide predictive, diagnostic, and prescriptive insights
• A single intelligent interface capable of integrating data across business functions and domains.

The chatbot enables conversational access to data for a wide range of stakeholders, automate insight delivery and action initiation based on stakeholder approval, and provide a centralized source of truth across the organization.

Tredence built DigitalChatbot with:

• A modular, scalable architecture to support growing data volumes and future AI model upgrades,
• Real-time processing pipelines, robust monitoring, logging, and governance features,
• Role-based access control and data security aligned with industry standards,
• A user-friendly, voice-enabled UI to promote adoption and collaboration.

The GenAI and Agentic AI Chatbot Architecture at a Glance  

Our solution delivers a scalable, flexible, and large-language-model (LLM)–agnostic GenAI chatbot built on a modern, agentic AI architecture. It enables employees to use natural language queries to gain real-time, cross-domain insights, supporting intuitive decision-making on a secure, future-ready platform.

Tredence built the DigitalChatbot within the Databricks ecosystem. This architecture leverages the Customized CHASE framework to generate dynamic Text2SQL queries across a complex business data environment with 500+ structured and semi-structured data objects. The platform brings both operational intelligence and enterprise scalability.

 Agentic AI flow for Text2SQL using the Chase framework with customization 

From the initial design, the solution now leverages AI/BI Genie for Text2SQL components.

Supporting Modern Mining Business Operations 

Tredence leveraged its domain fluency in coal mining and supply chain management to build DigitalChatbot and provide capabilities that speak the language of the business, from equipment diagnostics and safety protocols to inventory management and logistics optimization—delivering contextual, actionable insights from day one. 

DigitalChatbot is built to support complex business questions across coal mining operations and is designed to scale across other company business functions, such as logistics and renewables. More than 350 CXOs, senior executives, operations leads, analysts, and field engineers can self-serve analytics in natural language. Since DigitalChatbot bridges siloed data sources, it provides cross-domain insights, streamlined access to key performance indicators (KPIs), and operational data that were previously locked behind static reports or disconnected systems.

Implementing a Cloud-Native Stack for the Chatbot 

The solution uses a modern, cloud-native stack on Azure, including Azure Databricks for scalable Lakehouse storage and computation, Databricks Mosaic ML for Model serving. The architecture supports multi-agent orchestration, real-time Text2SQL generation, LLM hallucination control, and prompt guardrails. Key performance and governance features are built on top of Unity Catalog Governance. LangGraph orchestrates dynamic, stateful agent workflows, while Langfuse ensures full observability through traces, evals, prompt management, and performance metrics, enabling continuous debugging and optimization of the LLM system. As the Databricks ecosystem has evolved, we are converting some domains to use Genie Spaces for Text2SQL capabilities. The multi-agent orchestrator from Agentbricks is also being used for some of the out-of-the-box responses.

• Ensuring governance and observability:
  Governance was embedded from day one. The solution ensures privacy by design through data anonymization, strict access controls (via Azure AD), and encryption at rest and in transit. Audit trails, query logs, and data lineage are captured, enabling transparency and traceability across chatbot interactions. This setup enables the chatbot to be both trusted and compliant, with controls to support future scale and audit readiness.

• Delivering a LLM ingestion and context enrichment framework:
  Unified pipelines for ingesting real-time and batch data from hybrid sources—structured, semi-structured, and unstructured, enable dynamic context injection into chatbot interactions with minimal engineering effort.

• Providing conversational schema blueprints:
  Predefined dialogue flows, agent orchestration templates, and knowledge graph schemas accelerate chatbot development and deployment, ensuring consistent interaction design across business domains and use cases.

• Creating a prompt quality and response validation module: 
  Automated response evaluation detects hallucinations, assigns confidence scores, and triggers guardrail actions, ensuring reliable, safe, and high-quality conversational outputs from LLMs.

• Providing an LLMOps and cost optimization toolkit: 
  Integrated Azure-native tools monitor token usage, track inference latency, manage API quotas, and automate prompt tuning—delivering an efficient, scalable, and cost-optimized lifecycle for chatbot operations.

• Offering access to a dedicated team: 
  A specialized, cross-functional team with deep expertise in GenAI, agentic AI, Azure OpenAI, and cloud-native engineering accelerates the development of intelligent, autonomous chatbots—transforming enterprise knowledge into scalable, production-grade conversational agents capable of reasoning, decision-making, and dynamic task execution.

• Accelerating time-to-value: 
  Our prebuilt, Azure-native chatbot accelerators reduce deployment timelines by up to 60%, enabling faster rollout of intelligent assistants that deliver real-time insights across coal mining operations and supply chain workflows.

• Enabling conversational AI with governance built-In: 
  Designed with enterprise-grade governance, our chatbot frameworks ensure secure, consistent, and compliant interactions, empowering teams with self-service access to operational data and decision support, without compromising control.

Achieving Business Benefits 

DigitalChatbot is built on a modular, agentic architecture that is LLM-agnostic and domain-extensible. The mining company’s users benefit by:

• Speeding decision-making: Users receive instant, data-driven responses to business questions, with 80% faster query response times for business leaders.
• Increasing data accessibility: Users have natural language access to over 300+ data objects without SQL or business intelligence tools, increasing insight access by 60%. 
• Enabling cross-domain business insights: Enabling unified queries across finance, operations, supply chain, HR, and ESG.
• Achieving LLM accuracy with guardrails: Integrating schema alignment, SQL validation, and hallucination control to achieve around 90% accuracy in phase one.
• Reducing analytics cost at scale: Built on Azure-native, serverless infrastructure to scale without significant cost increases, the solution delivers 25–40% long-term savings in analytics and reporting infrastructure.
• Providing robust governance and security: Enforcing privacy, role-based access control, logging, and encryption using Azure AD, Key Vault, and Purview to ensure compliance with audit and enterprise data security standards.

With easy access to cross-functional data and reporting functionality, the company’s DigitalChatbot users can make better, faster decisions that enable the company to maximize business opportunities, identify and mitigate risks, and modernize operations. 

AUTHOR - FOLLOW
Aswin Ramachandran
Senior Manager,<br> Data Engineering