Generative Assisted Retrieval (GAR) introduces a new paradigm for enterprise analytics—combining AI transparency with traceable, data-driven reasoning. By grounding responses in a verifiable knowledge model, GAR eliminates hallucinations and enables business leaders to ask complex “why” questions conversationally. The result: faster decisions, greater trust, and a leap from static dashboards to dynamic intelligence.
GAR transforms enterprise analytics by merging AI reasoning with data verification. Built on a structured knowledge model, it delivers transparent, traceable, and conversational answers that empower faster, more confident business decisions.
Generative Assisted Retrieval (GAR) transforms enterprise analytics by replacing static dashboards and opaque AI outputs with conversational, transparent, data-backed answers. By building a knowledge model, constraining AI to verifiable queries, and ensuring full traceability, GAR reduces hallucinations, accelerates decision-making, and makes complex “why” questions as accessible as simple reports.
A new approach to business intelligence promises to transform how organizations interact with their data—moving from static dashboards to dynamic, conversational analysis
While generative AI has transformed how individuals interact with information, enterprise analytics remains stuck in outdated workflows. Business leaders still wait weeks for custom reports, navigate complex dashboard systems, and struggle to get answers to the dynamic questions that drive real decisions. But a breakthrough approach called Generative Assisted Retrieval (GAR) may finally bring the conversational AI revolution to enterprise data.
The Enterprise Analytics Gap
Despite the remarkable capabilities of tools like ChatGPT for document analysis and general queries, enterprise environments have seen little impact from the AI revolution. The reasons are both technical and practical.
First, most conversational AI tools simply don't have access to enterprise databases. If you ask ChatGPT "What's my sales forecast for next quarter?" it can only hallucinate an answer without real data.
Second, even with database access, traditional text-to-SQL approaches fail for non-technical users. While they might serve as coding assistants for SQL experts, they can't handle the complexity of real enterprise environments with hundreds of interconnected tables.
Perhaps most importantly, generative AI's "black box" approach to answers is fundamentally incompatible with business decision-making. Leaders need traceable, verifiable insights they can defend and explain—not mysterious outputs from an opaque process.
Introducing GAR: A New Approach
Ilya Lipkind, founder of analytics company Claritype, believes his team has solved these fundamental challenges with GAR—Generative Assisted Retrieval.
The approach builds on Claritype's existing technology: an AI system that creates conceptual models of organizations' data ecosystems. Think of it as a detailed map of how a business operates, built automatically with minimal human guidance.
"We don't generate the final answers—we retrieve and compute," Lipkind explains. "That's why they can be trusted and inspected, which is a requirement for something to be used in analytics."
How GAR Actually Works
GAR's breakthrough comes from a fundamentally different architectural approach that addresses the core problems plaguing enterprise AI implementations.
Knowledge Model Foundation: GAR starts by creating a "knowledge model"—a formal business vocabulary that defines concepts like Customer, Order, and Invoice, along with their relationships and constraints across different systems. This isn't just technical mapping; it's a pragmatic description of how the business actually operates, written in a language both humans and AI can understand.
Constrained Reasoning: The crucial difference is that GAR constrains the AI to only work within this defined knowledge model. If the system encounters a concept it doesn't understand, it must ask for clarification rather than fabricating an answer. This dramatically reduces hallucination while maintaining conversational interaction.
Fact-Based Execution Chains: Instead of generating answers, GAR decomposes complex questions into logical reasoning steps. Each step becomes a real query against authoritative data systems, with results that are retrieved, computed, and validated rather than generated. When you ask "Why did customer churn increase last quarter?" GAR creates a verifiable chain of intermediate queries that build toward the final analysis.
The result is transformative. Rather than being limited to single questions about "what" happened, business users can ask high-level analytical questions about "how" and "why" it happened. These are questions they'd typically pose to human analysts, and GAR makes it possible to get comprehensive, traceable answers.
Transparency and Learning
What makes GAR particularly powerful is its complete transparency and ability to learn. Every analytical step links back to the underlying data and queries, with full provenance tracking. Users can see exactly what query was run, what data was retrieved, and how each conclusion was reached.
If a user disagrees with how customer attrition is calculated, they can provide their preferred definition, and the system will automatically recompute the entire analysis. More importantly, this correction becomes part of the knowledge model—when anyone else asks about churn in the future, the system already knows the company's specific definition.
"Everything is traceable," Lipkind notes. "Every step has a link to our data explorer where you can see what query it ran, what data was retrieved. And if you're unhappy with how it computed something, you can redefine it once for your entire company."
The system also enforces what Claritype calls "typed semantics"—concepts, attributes, and relationships have formal definitions with constraints and referential integrity. This ensures deterministic execution where identical inputs always yield identical outputs, something traditional generative AI cannot guarantee.
Beyond Single Answers
GAR goes beyond answering individual questions. After completing an analysis, it suggests relevant follow-up questions based on its understanding of the industry, the specific company, and the current conversation context. These suggestions often prove remarkably insightful—either confirming questions users already had in mind or surfacing important considerations they hadn't thought to explore.
Accelerating Decision-Making
Perhaps most importantly, GAR addresses what Lipkind calls "organizational clock speed"—the time it takes to get answers and make decisions. Instead of waiting weeks for reports and then weeks more for follow-up analysis, leaders can get immediate, data-backed answers that support real-time decision-making.
"You can increase the clock speed of your organization," he explains. "When you wait for a report or a new analysis for two weeks, and then you have a new question and another two weeks... Here you could get dynamic answers that are based on the data in your enterprise and make decisions right there."
The Path Forward
Lipkind envisions specialized analytical agents built on this foundation—a financial analyst focused on CFO reporting, a strategic consultant for CEO planning, or a network analyst for IT operations. Each would be tailored for specific roles and data sets while sharing the same underlying infrastructure.
His prediction about adoption is confident: "As soon as people see that they can ask questions about enterprise data the way they've been taught to interact with ChatGPT, I don't think they'll ever turn it off."
While still emerging, GAR represents a fundamental shift in enterprise analytics—from generated to retrieved, from opaque to transparent, from static to conversational. For organizations struggling to turn their data into actionable insights, this approach offers a compelling path to the analytics-driven future that generative AI has long promised but struggled to deliver in enterprise environments.
