PRODUCT

The technology behind the relentless accuracy

The innovations, features and architecture that power PromptQL’s reliability on real-world business problems.

Agentic semantic layer

Captures the evolving business context that exists nowhere else

Traditional semantic layers and knowledge graphs fail because they're hard to build and only capture documented knowledge. But the most valuable business insights – tribal knowledge – live in the heads of experienced employees.

PromptQL automates what traditional systems do manually: it introspects schemas, documentation, and code. Then it goes further – learning the unwritten expertise that no system captures, but teams rely on every day.

Bootstrapped automatically: Introspects schema, documentation, and code to generate the initial semantic graph.

Learns tribal knowledge: Captures insights like “deals pushed twice are at risk” and context-specific meanings like “GM” as gross margin vs. general manager.

Self-improving: Learns team-specific language and usage patterns to evolve the graph continuously.

Domain specific language

Decouple query planning & execution

PromptQL doesn't rely on LLMs for final answers – instead, it uses LLMs to generate rich, multi-step query plans in a domain-specific language for data retrieval, computation, and semantic tasks.

By decoupling planning from execution, PromptQL avoids hallucinations, inconsistencies, and context window limits. This separation enables it to handle billion-row datasets and complex multi-system queries that overwhelm traditional methods.

Distributed query engine

Query data where it lives. No ETL required.

PromptQL engine pushes query plans directly to the source systems across databases, APIs, SaaS apps, or unstructured sources.

No data movement or centralization needed

Federated execution across heterogeneous systems

Granular, field-level access control enforced at source

The runtime and semantic layer operate in a closed loop – enabling continuous learning and improvement.

How it works

For your org, ACME, PromptQL helps you build AcmeQL, a language that an LLM can use to plan, reason and act with the level of reliability you can expect from an analyst or engineer on staff

PromptQL is an AI platform that continuously adapts LLMs to your domain by capturing and encoding the proprietary know-how into a planning language the LLM can understand and generate.

The planning language incorporates terminology, processes and a semantic graph of data and tools.

Importantly, this generated language can be compiled into precise machine executable code that can run deterministically and hence handle arbitrarily large amounts of data and complex plans without LLM context limitations.

Request demo

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.

PromptQL logo

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

PromptQL use cases

With a deep understanding of your business’s unique language, data, and workflows, PromptQL enables a wide range of decision intelligence and automation use cases

FEATURES

The building blocks that make reliability possible

Connectors

Connectors

Out-of-the-box connectors make it easy to ground AI with live context from internal and external systems – databases, SaaS apps, APIs, web sources, and more.

Structured memory artifacts

Editable query plans

Reliability score

PromptQL Playground

PromptQL APIs

Fine-grained access control

Bring your own LLM

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