PromptQL for Data & Analytics Teams
Accurate AI for analysis and automation
Activate your enterprise data platform – deploy custom conversational analytics agents that your business trusts and relies on.
CHALLENGES
The "talk to your data" promise is oversold
Most “AI analysts” in the market don’t understand your business well enough to be useful. They answer surface-level questions – but lack the context, rigor, and trust needed in production.
This question appears simple on the surface, but tackling it accurately requires tacit business knowledge, cross-source intelligence, and deep contextual understanding that current AI products simply don't have. PromptQL fixes this gap.
What is PromptQL
AI that works like your 10x analyst
PromptQL lets data and analytics teams build specialized AI agents grounded in their unique data models, logic, and workflows – delivering near-perfect accuracy.
It creates a dynamic analytics intelligence layer that continuously learns your company’s internal metrics, definitions, naming conventions, and decision patterns – mirroring how your business thinks and works.
The result: AI that brings the rigor and reasoning of an experienced analyst – and improves with every query and interaction.
PromptQL has allowed us to actually deliver on the promise of AI at scale for business users. It is a game changer.
Sr. Director, Product Management
F100 Technology Enterprise
DIFFERENTIATORS
Why PromptQL?
In a crowded AI landscape, PromptQL stands apart – delivering AI that deeply understands your data and produces accurate, explainable outcomes your business users can trust.
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Near-perfect accuracy
PromptQL powers your most critical analytics workflows with precision, transparency, and consistency you can rely on.
Fluent in your business speak
PromptQL adapts to your models, metrics, and logic – grounded in your rules, definitions, and governance.
Deploy in hours, not weeks
Connect to your data warehouse and start delivering insights in hours – with enterprise-grade security and native integration with your stack.
Hands-on partnership
Partner with our embedded forward deployed engineers to scope, build, and scale high-impact analytics solutions — from prototype to production.
Core Innovation
The technology behind the accuracy
PromptQL combines semantic understanding, deterministic execution, and distributed query engine to deliver reliable, domain-specific AI – grounded in how your business actually works.
QUESTION / TASK
[From a business or customer]
FOUNDATIONAL LLM
“ACMEQL”
“ACMEQL” LLM: A specialized model fine-tuned on your org’s semantics and data based on in-context learning – enabling accurate, context-aware reasoning.
“ACMEQL” SEMANTIC GRAPH
“ACMEQL” Semantic Graph: A dynamic semantic model that captures your unique business concepts, entities, and logic and acts as a contextual brain for the LLM.
“ACMEQL” PLAN
“ACMEQL” Plan: A structured, multi-step execution plan with human-like reasoning
PROMPTQL LEARNING LAYER
PromptQL Learning Layer: Continuously learns from user behavior and data changes to evolve the semantic graph—no manual tagging required.
PROMPTQL RUNTIME
PromptQL Runtime: Programmatically runs the plan, with structured memory outside LLM context.
AI Platform
PROMPTQL FEDERATION
PromptQL Distributed Query Engine: Federates data across multiple data sources with governance and access control policies automatically enforced.
MCP
UNSTRUCTURED DATA
SaaS
APIs
WEB
STRUCTURED DATA
Continuously learns your unique context
Bootstraps and self-improves a semantic layer from code, docs, and tribal knowledge – not just what’s written down.
Deterministic plan execution
Generate precise, human-readable query plans – then executes them deterministically for consistent outcomes.
Query data where it lives
Runs distributed queries (with authorization) across databases, APIs, and tools – without data movement or ETL.
Use Case Playground
Give it a try
Explore live, interactive analysis scenarios – from sales pipeline analysis to supply chain investigation – and see how PromptQL delivers accurate results on complex tasks.
Why did ENT pipeline drop in the last Q? How does this correlate with rep activity?
How should we optimize data plan allocations to maximize revenue while maintaining customer satisfaction?
Which materials are we consistently over ordering or under ordering based on actual production needs?
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More resources to start your conversational data journey with PromptQL.
