Why

Modern LLMs work wonders with public domain data. Yet, enterprise AI systems often struggle to harness their full potential. Distance and Lossy Compression are two core reasons hindering adoption.

Distance Challenge

Enterprise Information                            LLM parameters
          💎                                             ⚓️
----------∙-------------------\\-------------------------∙------->
        here                                           there

Enterprise data remains fundamentally isolated from the LLM’s learned parameters.

Lossy Compression Challenge

Artificial neural networks (NN) convert data to floating-point embeddings, effectively discarding the original data format. Then dense representation enables the learning of universal concepts while sacrifices individual data points.

Fine-tuning may bridge the gap of the Distance Challenge but won’t achieve precise computations for, say, stock market analysis.

When and How To

Your data interacts with the LLM at least twice in the pipeline or agentic flow:

1. When retrieving records relevant to the query
2. When answering the query using retrieved records

To ensure accurate responses, the engineering team must address 4 “How” questions:

What Is Xfrukt

Xfrukt emphasizes key information in your data, tailoring it for LLMs to facilitate accurate inference. Unlike naive prompting with top k records, where vital information is buried among thousands of irrelevant tokens.

With just a few samples from your data sources, Xfrukt interactively analyzes and delivers:

1. An optimized schema in JSON or SQL DDL, forming an ontology that integrates insights from your data with standard, refined knowledge of the relevant business vertical.
2. (optionally) A custom API endpoint https://api.xfrukt.com/your-structuring-endpoint-id to structure all your data according to the schema.

By handling essential steps in Business Analyzis, Data Modeling and Data Processing, Xfrukt allows AI team to leap forward, skipping the notorious “data preparation is 80% of the project” phase.

How To Xfrukt

1. Join the Early Access program.
2. Interactivelly model your data to receive the schema and optional API endpoint
3. Start a data analysis session.
4. Upload small samples of your data.
5. Interactivelly answer several specifying questions.
6. Receive the schema and optional API endpoint.
7. Run schema files to create stuctures in your database.
8. Populate the database by processing data via the endpoint or, using the provided schema, implement your own data extractor.

Your data pipeline is all set! Now, dive straight into implementing response synthesis or reasoning agents.