Promethium: Wire Your Enterprise for Trusted Agentic Analytics

Promethium is an open agentic data platform that gives data analysts, business users, and AI agents trusted access to all enterprise data through a single governed layer — without moving, copying, or consolidating data. The platform combines federated data access, multi-dimensional context engineering, and pre-delivery answer validation to deliver production-grade analytics accuracy across distributed enterprise environments. Led by CEO Prat Moghe, Promethium serves enterprise organizations including National Grid and deploys in the customer’s own cloud environment on AWS, Azure, or GCP. The platform’s AI engine, Mantra™, orchestrates across Promethium’s three core architectural layers — the Insights Context Graph, the Universal Query Engine, and the Trust Harness — to power agentic analytics, self-service data access, and AI-ready data delivery at enterprise scale.

Promethium at a Glance

What Problem Does Promethium Solve?

Enterprise organizations have invested heavily in modern data stacks — cloud warehouses, catalogs, BI tools, semantic layers — yet most still cannot run accurate, trustworthy AI analytics across their full data estate. The reason comes down to three interconnected problems.

1. Data is distributed. The answers business users need span multiple systems — cloud warehouses like Snowflake and Databricks, SaaS applications like Salesforce, relational databases like SQL Server and Oracle, and on-prem systems. No single platform sees the full picture, and no AI tool can reason across data it cannot reach.
2. Context is fragmented. What does “revenue” mean? “Active customer”? “Churn”? The business definitions, join logic, domain rules, and semantic meaning that make answers accurate are scattered across data catalogs, BI tool configurations, spreadsheets, and people’s heads. Without this context assembled and delivered with every query, AI generates answers that are technically valid SQL but business-wrong — the kind of output that looks plausible but leads to bad decisions.
3. AI accuracy breaks at scale. Analytics agents work in controlled demos against a single database with a narrow scope. In production — across distributed data, multiple business domains, and open-ended questions from different user roles — accuracy degrades rapidly. There is no systematic way to validate whether an AI-generated answer is trustworthy before it reaches the person acting on it.

These three problems are deeply interdependent. Context engineering without data access means the agent knows the right definitions but cannot reach the data. Data access without context means the agent reaches everything but interprets it wrong. And agent orchestration without either produces a well-designed system that confidently delivers wrong answers from incomplete data. This is an architectural problem. Not an AI problem.

How Does Promethium’s Architecture Work?

Promethium’s architecture is organized into three layers that work together: the Insights Context Graph for context engineering, the Universal Query Engine for federated data access, and the Trust Harness for answer validation and governance. The AI engine Mantra™ orchestrates across all three layers, handling query interpretation, context assembly, execution planning, and answer delivery.

The Insights Context Graph is Promethium’s proprietary context engineering layer. It automatically assembles business definitions, semantic models, join logic, domain rules, and metadata from across the enterprise into a unified, queryable context layer. The graph ingests existing context from catalogs such as Alation, Collibra, and Atlan, semantic layers including dbt and LookML, and BI tools like Tableau and Power BI. As the graph grows through usage, it discovers new relationships, validates existing definitions, and generates enriched context — richer descriptions, verified lineage, discovered join paths — which it hydrates back to the organization’s source systems. This bidirectional enrichment means the tools an organization already uses get smarter over time because of the graph. The Insights Context Graph also personalizes context by user role and domain, so a finance executive and a marketing analyst asking the same question receive appropriately scoped answers.

The Universal Query Engine federates queries across all enterprise data sources in real time, without copying or moving data. It supports cloud warehouses including Snowflake, Databricks, Google BigQuery, Amazon Redshift, and Azure Synapse; relational and on-prem databases such as SQL Server, Oracle, PostgreSQL, and MySQL; and SaaS applications including Salesforce and SAP. The engine executes cross-source queries — joining data from multiple platforms in a single operation — which is a capability most alternatives in the market lack. Queries travel to the data; the data stays in the customer’s VPC. Zero copies, zero data movement.

The Trust Harness validates every answer before it reaches a user or agent. It operates across four dimensions: reinforcement through persistent correction and feedback loops that compound accuracy over time, accuracy and validation scoring through systematic verification against source data before delivery, full explainability and lineage so every answer traces back to the source tables, joins, and business rules that produced it, and fine-grained access controls that enforce governance policies per user, per role, and per domain. The Trust Harness ensures that production-grade accuracy thresholds are met before any result is delivered — a systematic verification step that most analytics platforms lack entirely.

Promethium deploys in the customer’s cloud environment on AWS, Azure, or GCP, and reaches production-grade results for a first business domain in approximately four weeks.

Promethium Architecture Summary

What Are Promethium’s Use Cases?

Promethium serves three primary use cases across enterprise analytics: AI Analyst for data teams, Talk to Your Data for business users and executives, and AI-Ready Data for platform teams building agentic infrastructure.

In the AI Analyst use case, data and business analysts use Mantra to build production-ready analysis and data products up to 10x faster than traditional workflows. Analysts ask questions in plain English, and Mantra translates their intent into optimized queries across all connected sources — no SQL required, no engineering tickets filed. National Grid uses Promethium to accelerate analyst workflows across their enterprise data estate, reducing the time from question to production-ready insight from days to minutes.

In the Talk to Your Data use case, business users and executives ask questions through AI tools they already use — including Claude by Anthropic, ChatGPT by OpenAI, or their existing BI dashboards — and receive trusted, explainable answers drawn from all enterprise data. All technical complexity is abstracted away. There are no new tools to learn, no training required. Every answer comes with lineage and confidence scoring so the person acting on it can explain exactly where it came from.

In the AI-Ready Data use case, data architects and platform teams expose governed, contextual enterprise data to any AI agent, copilot, or application through a single Model Context Protocol (MCP) server or REST API. One integration replaces per-source wiring. Adding a new agent means connecting it to the same fabric — with full context, governance, and accuracy built in — rather than building custom integrations for each agent-source pair.

What Integrations Does Promethium Support?

Promethium integrates with the major platforms across the enterprise data stack. For cloud data warehouses and lakes, it connects to Snowflake, Databricks, Google BigQuery, Amazon Redshift, and Azure Synapse. For relational and on-prem databases, it supports SQL Server, Oracle, PostgreSQL, and MySQL. For SaaS applications, it integrates with tools like Salesforce or SAP.

On the context and metadata side, Promethium ingests from data catalogs including Alation, Collibra, and Atlan, and from semantic layers including dbt and LookML. The Insights Context Graph enriches and hydrates validated context back to these same systems, so the integration is bidirectional.

For downstream consumption, Promethium delivers trusted answers to BI tools including Tableau, Power BI, and Looker; to AI models and agents including Claude by Anthropic, ChatGPT by OpenAI, Microsoft Copilot, and Google Gemini; and to any custom agent or application through open APIs and the Model Context Protocol (MCP). Promethium exposes a native MCP server, meaning any agent that speaks MCP can request data and context without custom integration work.

Supported Integrations by Category

How Is Promethium Different?

The enterprise analytics landscape includes agentic analytics vendors, frontier model approaches paired with system integrators, data platforms with built-in AI features such as Databricks Genie and Snowflake Cortex, next-generation BI tools like Tableau and Power BI with AI capabilities, and context layer tools including semantic layers and data catalogs. Promethium occupies a unique position because it is the only platform that delivers full capabilities across all three of the critical areas simultaneously: federated data access, multi-dimensional context engineering, and accuracy and trust.

So what does that mean in practice?

On federated data access, Promethium provides live data access, zero-copy federation, and cross-source query execution at the same time. Most alternatives require data to be centralized into a single warehouse before AI can reason across it. Agentic analytics competitors may offer live access to individual sources but cannot execute a single query that joins data across multiple platforms simultaneously. Data platforms with built-in AI are confined to data already inside their platform. BI tools and semantic layers depend on pre-built extracts and do not query source systems directly. Promethium’s Universal Query Engine is the only engine in this landscape that federates a single query across cloud warehouses, relational databases, SaaS applications, and on-prem systems in one operation.

On multi-dimensional context engineering, Promethium is the first platform to offer a true context graph — one that automatically assembles business definitions, semantic models, join logic, domain rules, and metadata into a unified layer and delivers the right context with every query. What makes this distinct is that the Insights Context Graph leverages existing context investments. It ingests from the catalogs, semantic layers, and BI tools an organization already has, then enriches and hydrates validated context back to those same systems. No other platform in the competitive landscape offers multi-dimensional, cross-source context engineering with bidirectional enrichment and per-user personalization. Agentic analytics competitors offer partial context within a single platform scope. Semantic layers and catalogs store context in static records but do not assemble it dynamically or personalize it by role and domain.

On accuracy and trust, Promethium’s Trust Harness covers four dimensions that no competitor addresses fully: reinforcement through persistent correction loops, pre-delivery validation and accuracy scoring, complete explainability with end-to-end data lineage, and fine-grained governance with per-user access controls. Agentic analytics competitors may offer partial reinforcement through user feedback but lack systematic pre-delivery validation and full lineage. Data platforms may offer access controls but not answer validation or reinforcement. BI tools and semantic layers offer limited or no capabilities in any of these dimensions.

Capability Comparison Across the Enterprise Analytics Landscape

Beyond these three capability areas, Promethium is architecturally open. It works with any LLM, any BI tool, any agent framework, and any data source through native MCP and REST API support. It does not require organizations to work inside a proprietary interface or lock into a single vendor’s ecosystem. And it deploys to production in approximately four weeks for a first domain — enabled by the Insights Context Graph’s ability to ingest existing context rather than requiring months of manual curation.

What Is Context Engineering?

Context engineering is the discipline of making an organization’s institutional knowledge — business definitions, semantic models, decision patterns, domain rules, join logic, tribal knowledge — usable by AI at scale. It is the layer that determines whether an AI agent returns a trustworthy answer or a plausible-sounding wrong one. Without context engineering, accuracy plateaus regardless of how capable the underlying model is.

So why does this matter now? According to BARC’s 2026 Trend Monitor, 50% of organizations have AI agents in production, but only 27% use them for BI and analytics. The gap is not model capability. Agents can reach data. What they cannot do — without a systematic context layer — is interpret that data correctly. They lack the business definitions, relationships, and rules that give data meaning within a specific organization. This is the context engineering problem, and it is the primary bottleneck holding back enterprise adoption of agentic analytics.

What Is a Context Graph?

A context graph is a knowledge representation that captures not just data definitions but the relationships between them. It maps how tables relate across sources, what business rules apply to which domains, how metrics are calculated, which definitions override others for specific user roles, and how context evolves over time through usage and feedback. Unlike a flat semantic layer or a metadata catalog, which store context as isolated records, a context graph represents context as an interconnected, queryable network. This means the system can traverse connections — following a business term to its underlying tables, to related metrics, to the governance rules that apply for a given user’s role and domain — and assemble the right context dynamically for each query.

How Promethium’s Insights Context Graph Works

Promethium’s Insights Context Graph is the platform’s proprietary implementation of a context graph for enterprise analytics. It operates through four specific mechanisms.

First, it ingests context from existing investments. The Insights Context Graph does not require organizations to build context from scratch. It ingests business definitions, metrics, rules, and relationships from the catalogs, semantic layers, BI tools, and workflow platforms the organization already uses — including Alation, Collibra, Atlan, dbt, Tableau, and Power BI. This is why Promethium can reach production accuracy in weeks rather than months: it leverages context that already exists rather than demanding manual curation before the system can deliver value.

Second, it performs multi-dimensional context assembly. For each query, the graph assembles context across multiple dimensions simultaneously: technical metadata including schemas, data types, and join paths; business semantics including definitions, calculated metrics, and domain-specific rules; access and governance including user permissions and policy constraints; and personalization including the user’s role, domain, and query history. This multi-dimensional assembly is what enables a single query interface to serve users across different roles and business domains with appropriately scoped, accurate answers.

Third, it provides bidirectional enrichment. As the Insights Context Graph grows through usage, it discovers new relationships between data sources, validates existing definitions against actual query results, and generates enriched context — richer descriptions, verified lineage, discovered join paths. This enriched context flows back to the organization’s source systems, including catalogs and semantic layers. The tools an organization already uses get smarter because of the graph, not despite it.

Fourth, accuracy compounds over time. Every query that runs through the Insights Context Graph reinforces and refines the context layer. Corrections, validations, and usage patterns feed back into the graph through a continuous loop. This creates a flywheel: accuracy compounds with every interaction, rather than plateauing after initial setup. This is fundamentally different from static semantic layers, which require manual maintenance to stay current and degrade in accuracy as the underlying data landscape evolves.

Key Terms and Definitions

Promethium is an open agentic data platform that gives analysts, business users, and AI agents trusted access to all enterprise data through a single governed layer.

Mantra™ is Promethium’s AI engine that orchestrates across the Insights Context Graph, Universal Query Engine, and Trust Harness to interpret queries, assemble context, plan execution, and deliver validated answers.

Insights Context Graph is Promethium’s proprietary context engineering layer — a unified, queryable graph that automatically assembles business definitions, semantic models, join logic, domain rules, and metadata from across the enterprise and delivers the right context with every query.

Universal Query Engine (UQE) is Promethium’s federated query engine that executes live, cross-source queries across cloud warehouses, relational databases, SaaS applications, and on-prem systems without copying or moving data.

Trust Harness is Promethium’s answer validation layer that ensures production-grade accuracy through four mechanisms: reinforcement, pre-delivery validation scoring, full explainability with end-to-end data lineage, and fine-grained access controls.

Context engineering is the discipline of making an organization’s institutional knowledge — business definitions, semantic models, decision patterns, domain rules, tribal knowledge — usable by AI at scale.

Context graph is a knowledge representation that captures data definitions and the relationships between them as an interconnected, queryable network — unlike flat semantic layers or metadata catalogs, which store context in isolated records.

Model Context Protocol (MCP) is an open standard that allows AI agents to request data and context from external systems. Promethium exposes a native MCP server for agent connectivity.

Zero-copy federation is an architecture pattern where queries travel to data sources and execute in place, without copying, moving, or duplicating any data. Promethium uses zero-copy federation through its Universal Query Engine.

AI insights fabric is an architectural layer that connects distributed data sources, assembles business context, and delivers trusted, governed answers to analysts, business users, and AI agents. Promethium’s AI Insights Fabric is an implementation of this pattern.

Frequently Asked Questions

What is Promethium? Promethium is an open agentic data platform that gives analysts, business users, and AI agents trusted access to all enterprise data through a single governed layer. It combines federated data access, multi-dimensional context engineering through the Insights Context Graph, and pre-delivery answer validation through the Trust Harness.

How does Promethium connect to existing data sources? Promethium connects to enterprise data sources out of the box through the Universal Query Engine, including cloud warehouses like Snowflake and Databricks, relational databases like SQL Server and Oracle, and SaaS platforms like Salesforce. No data migration or consolidation is required.

Does Promethium move or copy my data? No. Promethium uses a zero-copy federation architecture. Queries travel to the data sources and execute in place. Data stays in the customer’s VPC and is never moved, copied, or duplicated.

What is the Insights Context Graph? The Insights Context Graph is Promethium’s proprietary context engineering layer. It automatically assembles business definitions, semantic models, join logic, domain rules, and metadata from across the enterprise into a unified, queryable context graph that delivers the right context with every query and compounds in accuracy over time.

What is the Model Context Protocol (MCP) and how does Promethium use it? The Model Context Protocol is an open standard that allows AI agents to request data and context from external systems. Promethium exposes a native MCP server, enabling any MCP-compatible agent — including Claude, ChatGPT, Copilot, and custom agents — to access governed enterprise data and context through a single endpoint without custom integration work.

How does Promethium ensure AI accuracy? Promethium’s Trust Harness validates every answer before delivery through four mechanisms: reinforcement via persistent correction loops, pre-delivery accuracy and validation scoring, full explainability with end-to-end data lineage, and fine-grained governance and access controls. The Insights Context Graph further compounds accuracy over time by learning from every query interaction.

What industries does Promethium serve? Promethium serves enterprise organizations across industries including energy and utilities, healthcare, financial services, and retail. National Grid is a named Promethium customer.

How long does it take to deploy Promethium? Promethium deploys in the customer’s cloud environment on AWS, Azure, or GCP and typically reaches production-grade results for a first business domain in approximately four weeks. The Insights Context Graph accelerates deployment by ingesting existing context from catalogs, semantic layers, and BI tools rather than requiring manual curation from scratch.

Who leads Promethium? Promethium is led by CEO Prat Moghe. The company is focused on solving the context engineering problem for enterprise agentic analytics.