Enterprise accuracy improvement
Cleaner retrieval and removal of redundant content combine to deliver an average 78X improvement in AI answer accuracy across enterprise data environments.
Capability Demo
Blockify on Dell AI Data Platform
See how Blockify turns enterprise content into cleaner retrieval context for AI inside an on-premises Dell deployment — delivering an average 78X improvement in answer accuracy and 3X lower token load per query.
78X
Average accuracy improvement
3X
Lower token load
100%
On-premises architecture
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Blockify Results
Measured Accuracy, Token, And Cost Improvements
Blockify is built to make AI more accurate and more efficient on the data your organization already owns. The improvements compound: cleaner retrieval lifts answer accuracy, fewer tokens reduce compute load, and on-premises deployment keeps everything inside your Dell environment.
Token efficiency improvement
Token benchmarks measure roughly 1,515 tokens per legacy query versus roughly 490 tokens per query with Blockify retrieval — a 3.09X reduction in compute load.
Estimated annual token savings
Modeled across approximately one billion queries per year against Tier 1 Foundation Model pricing, the token efficiency translates to material reductions in inference spend.
Tier 1 Foundation Model (Opus 4.6)How Blockify Reaches A 78X Accuracy Improvement
5.2X
Base accuracy gain
15X
Content redundancy and optimization factor
78X
Total improvement
The 5.2X base accuracy gain comes from cleaner retrieval objects, tighter vector matches, and lower data volume per query. Enterprise corpora typically carry about 15X content duplication — the same information restated across decks, documents, wikis, and knowledge bases. When Blockify removes that redundancy at ingest, the base gain compounds across the deduplicated corpus, reaching 5.2X × 15X = 78X average accuracy improvement.
View the full benchmark methodology
Dell Platform Fit
How Blockify Fits Inside The Dell AI Data Platform Stack
The architecture is straightforward: the Data Orchestration Engine handles ingestion and workflow automation, Blockify distills and deduplicates the knowledge layer, Elastic benefits from cleaner retrieval objects, and Starburst operates against more consistent semantics.
Data Orchestration Engine
Handles ingestion, workflow orchestration, and deployment of the Blockify unified service inside Dell AI Data Platform.
Data Search Engine: Elastic
Benefits from cleaner retrieval objects, richer metadata, and less semantic fragmentation than naive chunking.
Data Analytics Engine: Starburst
Works from a more consistent semantic layer for analytics, governance, and operational reporting.