Compute-ready data for AI with Futurum Signal65

Enterprises are discovering that PDFs are for people; machines need something else. As AI systems move from experimentation to production, raw documents limit accuracy, performance, and trust. This session covers a proof-of-concept engagement exploring why unstructured content must be transformed into secure, compute-ready assets to deliver faster insights, predictable performance, and true data sovereignty. This solution enables processing data once to power AI everywhere in your organization. Brian Martin, VP of AI and Data Center Performance at Signal65, presented the work of their AI lab, which is sponsored by Dell Technologies and focuses on real-world impact tests of AI workloads using extensive AI infrastructure, including various Dell XE servers and NVIDIA GPUs. The lab also developed a digital twin to optimize its physical layout, particularly for complex designs such as slab-on-grade data centers with overhead utilities, demonstrating an immediate positive ROI by identifying costly design changes early.

The presentation then transitioned to the critical challenge of preparing data for AI models. Echoing the sentiment that “garbage in, garbage out” becomes “expensive garbage out” with AI infrastructure, Signal65 highlighted how raw, unstructured data, particularly PDFs, hinders AI accuracy, performance, and trust. PDFs are designed for human consumption, not machine processing. Gadget Software addresses this by offering “compute-ready data,” which transforms unstructured content into AI-digestible formats. This process involves semantic decomposition to maintain topic continuity, LLM enrichment to generate useful metadata such as summaries, keywords, sentiment, and sample Q&A pairs, and robust governance and security through unique IDs and lineage tracking. This approach overcomes the limitations of traditional chunking and pure vectorization, which often lose context and attribution, making it difficult to cite sources or enforce security policies.

In a proof of concept using the vast United States Federal Register, Signal65 demonstrated the tangible benefits of this compute-ready data pipeline. The process ensured that all AI responses could be traced back to the original documents, which is crucial for governance and security. Performance testing revealed a significant advantage for local GPU processing (using L40S and RTX Pros) compared to accessing cloud LLM APIs. Local processing delivered remarkably consistent, flat latency during data ingestion and enrichment, in contrast to the spiky, unpredictable latency observed with cloud APIs, regardless of document size. This “write once, read many” approach ensures that once data is processed and enriched, it can be reliably accessed by various AI applications, such as chatbots or BI tools, delivering consistent, attributable results. Furthermore, the prepared data facilitates user interaction by enabling intuitive dashboards that showcase data content and suggest relevant queries, addressing the common user challenge of “what can I ask?”