INFACTORY VS RAG
People often assume Infactory is a RAG solution. It’s not–we’ve built something fundamentally different. While RAG attempts to make LLMs more accurate by feeding them relevant documents and content, Infactory focuses on structured data and takes a fundamentally different approach to enterprise AI reliability.
Determinism: Precision Over Approximation
At Infactory’s core is an architectural breakthrough that’s almost an oxymoron to Gen AI: determinism. When working with structured data, our technology guarantees that the same queries will always produce the same results. Every output is traceable, every calculation is verifiable, and every result is consistent. This is in stark contrast to the approximations and variations inherent in many probabilistic AI systems used today, including RAG.
Infactory’s Deterministic Advantage:
Unlike RAG’s document chunking that loses context and results in summarization loss, Infactory processes your entire dataset as a unified whole. This allows our system to:
- Preserve every data relationship and business rule intact
- Deliver consistent verifiable results every time
Retrieval: Understanding Data, Not Just Text
While both Infactory and RAG leverage LLMs, they leverage Gen AI at different parts of the process.
The RAG Data Retrieval Approach:
- RAG searches through chunks of text and relies on semantic similarities to retrieve relevant information
- This chunking process can lead to incomplete context and the potential loss of relevant details
- Results may vary depending on the specific text chunks it selects, making it unsuitable for applications requiring precise, consistent outcomes
- RAG also tends to struggle with numerical and relational data
Infactory’s Structural Advantage:
- Infactory understands the underlying data types, structures, and relationships within your enterprise data
- This deep comprehension of the data’s semantic layer enables Infactory to retrieve relevant information with high precision; even for numerical, relational, and complex datasets
- Infactory is able to reliably perform complex calculations over large datasets
Enterprise Readiness
Although an effective option for less-business critical queries over unstructured data, RAG solutions struggle to meet the rigorous demands of enterprise-grade data operations. Infactory, on the other hand, is designed from the ground up to address the strict requirements of large organizations with complex proprietary data.
RAG
- Maintaining consistent results and efficient retrieval over large datasets is a significant challenge for RAG; RAG solutions can’t guarantee consistency for critical business operations
- RAG’s performance can degrade as the size and complexity of the data increase
- RAG may only retrieve partial context and therefore cannot guarantee all relevant information is found when providing an answer (nor does RAG provide an easy way to validate the accuracy of retrieved information)
Infactory
- Infactory’s deterministic approach and structural understanding of data ensure consistent, reliable performance at scale, ultimately offering enterprises more accuracy and efficiency
- Infactory provides complete data visibility, with auditable lineage and traceability - critical features for regulated industries and mission-critical operations
Choosing The Right Solution
As organizations continue to grapple with the ever-growing complexity of their data and the question of how best to leverage AI, the need for reliable, accurate, enterprise-ready solutions has never been more pressing. While RAG is “good enough” for certain text-centric applications, “almost” is not good enough for sensitive enterprise data. Infactory’s breakthrough combination of determinism, structural understanding, and enterprise readiness make it the clear choice for companies looking to build AI products with their proprietary data and unlock new levels of insight.