Blockify Benchmark: The Proprietary RAG Accuracy Data, With Full Methodology
Every Blockify performance figure in one place — vector-search accuracy, token efficiency, data reduction, aggregate accuracy, and healthcare accuracy — each traced to a documented benchmark with named sources. Cite it, replicate it, scrutinize it.
What does the Blockify benchmark show? In documented enterprise benchmarks, Blockify improves RAG accuracy by up to 78X on an aggregate basis, delivers 2.29X more accurate vector search (a 56.26% precision gain), reduces token usage per query by 3.09X, and shrinks datasets by up to 40X to roughly 2.5% of their original size. In a safety-critical healthcare evaluation across nine clinical questions, Blockify improved combined accuracy and source fidelity by 261.11% on average, peaking at 650% on diabetic-ketoacidosis management. Every figure below is traced to its source benchmark and method.
How These Benchmarks Were Measured
These are proprietary enterprise benchmarks, and we publish the method alongside every number so you can scrutinize or replicate it. The headline figures come from two documented studies: the Blockify Performance Analysis conducted on a Big Four consulting firm’s dataset (17 documents / 298 pages), and an “Evaluation of Blockify” medical-accuracy study across nine clinical questions. Both compare Blockify’s distilled IdeaBlocks against naive fixed-size (~1,000-character) chunking — a method, not a named product.
The most important distinction on this page is between measured single-benchmark figures and documented cross-deployment averages. Vector-search accuracy (2.29X) and token efficiency (3.09X) are measured directly in the Big Four benchmark. The aggregate accuracy and data-reduction headlines you may have seen elsewhere — “up to 78X” and “up to 40X” — are cross-deployment averages. On this single Big Four dataset, the transparent worked example is a 68.44X aggregate and a 2.00X base (29.93X enterprise-adjusted) word reduction. We label every number accordingly.
Vector accuracy is measured as the mean cosine distance to the best-match result — the smallest distance — which deliberately favors the chunking baseline. Token counts assume a 4:1 character-to-token ratio. Enterprise-scale figures apply IDC’s documented 15:1 average data-duplication factor (a range of 8:1 to 22:1). Where a figure is an average across deployments rather than this benchmark’s direct output, it is marked as such.
Vector-Search Accuracy: 2.29X
Blockify IdeaBlocks return 2.29X more accurate vector-search results than naive 1,000-character chunking (a 56.26% precision improvement).
Source: Iternal Technologies Blockify Performance Analysis (Big Four consulting-firm dataset, 17 documents / 298 pages).
Method: mean cosine distance to best-match, 0.3624 chunking vs 0.1585 distilled IdeaBlocks; best match defined as smallest distance to deliberately favor the chunking baseline.
Vector search retrieves the passage whose embedding sits closest to the query. Smaller cosine distance means a tighter, more relevant match. Distilling documents into IdeaBlocks cuts the average best-match distance by more than half, so the model grounds its answer on the right passage far more often.
| Representation | Avg. cosine distance to best match | Relative accuracy |
|---|---|---|
| Legacy chunking (~1,000 chars) | 0.3624 | Baseline |
| IdeaBlocks (undistilled) | 0.1833 | 1.98X |
| IdeaBlocks (distilled) | 0.1585 | 2.29X |
See the full vector-accuracy methodology (Performance Analysis §09)
Token Efficiency: 3.09X
Blockify cuts tokens processed per query by 3.09X, an estimated $738,000/year saving at scale.
Source: Iternal Technologies Blockify Performance Analysis.
Method: ~303 tokens/chunk vs ~98 tokens/distilled block across top-5 retrieval; priced at $0.72 per 1M tokens (Llama 3.3 70B) over 1 billion annual queries.
Because IdeaBlocks are compact and deduplicated, each retrieval pulls far fewer tokens into the context window. Fewer tokens per query compounds into lower inference cost, lower latency, and more headroom under a fixed context budget.
| Measure | Legacy chunking | Distilled IdeaBlocks | Improvement |
|---|---|---|---|
| Avg. tokens per unit retrieved | ~303 | ~98 | 3.09X |
| Estimated tokens / year | ~1.515T | ~490B | 3.09X |
| Estimated annual saving | — | — | $738,000/yr |
See the token-efficiency worked example (Performance Analysis §08)
Data Reduction: Up to 40X (~2.5% of Original Size)
Blockify distills enterprise corpora to roughly 2.5% of their original size (up to 40X smaller) on average.
Source: Iternal Technologies Blockify Performance Analysis.
Method: cross-deployment average; the Big Four dataset measured a 2.00X base word reduction (88,877 → 44,537 words), rising to 29.93X once the IDC 15:1 enterprise data-duplication factor is applied.
“Up to 40X” is a documented cross-deployment average, not this benchmark’s measured output. The transparent worked example on the Big Four dataset is a 2.00X base word reduction (88,877 → 44,537 words), which rises to 29.93X by word count (and 27.82X by character count) once IDC’s 15:1 enterprise duplication factor is applied. Smaller datasets mean fewer tokens, lower cost, and a golden corpus small enough for humans to review.
See the data-reduction table (Performance Analysis §08, Table 7)
Aggregate Accuracy: Up to 78X
Blockify delivers up to 78X aggregate RAG accuracy improvement across enterprise deployments.
Source: Iternal Technologies Blockify Performance Analysis.
Method: aggregate of vector accuracy and data-volume reduction; the Big Four benchmark measured 68.44X (4.56X base × ~15X IDC enterprise duplication factor). 78X is the documented cross-deployment average, not a single-test result.
78X is the documented cross-deployment average. On this single Big Four benchmark, the transparent measured aggregate is 68.44X. The derivation is fully shown: a 4.56X base improvement (2.29X vector accuracy × 2.00X word-count reduction) is multiplied by roughly 15X — IDC’s average enterprise data-duplication factor (8:1 to 22:1) — for a 68.44X enterprise aggregate. We present 68.44X as the worked example precisely because it is what this dataset produced; 78X is the average across deployments.
Healthcare Accuracy: Up to 650% (261.11% Average)
In a healthcare RAG evaluation, Blockify improved accuracy and source fidelity by 261.11% on average and up to 650% on the highest-stakes topic (diabetic-ketoacidosis management).
Source: Iternal Technologies “Evaluation of Blockify” medical accuracy study.
Method: 9 clinical questions, Blockify vs chunking; 650% is the peak single-topic gain (DKA), 261.11% is the mean across all nine.
650% is the peak, not the average. Across nine clinical questions, Blockify improved combined accuracy and source fidelity by 261.11% on average, with the largest gains on the highest-stakes topics. In one safety-critical case, chunking recommended “D5W” (a dextrose solution) as an initial IV fluid, where standard protocol calls for isotonic saline first; the Blockify response avoided the error by not specifying the fluid type prematurely.
| Clinical question | Accuracy & source-fidelity improvement |
|---|---|
| DKA management (peak) | 650% |
| Pneumonia lab tests | 500% |
| Headache red flags | 250% |
| Heart-failure prognosis | 250% |
| Other queries | 100–300% |
| Average across 9 questions | 261.11% |
How to Cite This Page
Cite this data
Iternal Technologies. “Blockify Benchmark: RAG Data Optimization Performance.” iternal.ai/blockify-benchmarks. Retrieved 1970.
Data: Iternal Technologies, 1970. Free to cite with attribution.
Sources & References
- Iternal Technologies. Blockify® Performance Analysis Report for a Big Four Consulting Firm — the primary benchmark for vector accuracy (2.29X), token efficiency (3.09X), data reduction, and the 68.44X aggregate.
- Iternal Technologies. Evaluation of Blockify (Medical Accuracy Study) — the healthcare RAG evaluation across nine clinical questions (261.11% average, 650% peak).
- IDC. “Accelerating Efficiency and Driving Down IT Costs Using Data Duplication” — source for the 8:1–22:1 (average 15:1) enterprise data-duplication factor used in the enterprise-adjusted figures.
- Gartner. Lack of AI-Ready Data Puts AI Projects at Risk (2025) — context on why organizations abandon AI projects unsupported by AI-ready data.
- McKinsey. The economic potential of generative AI — context on the share of working hours generative AI can automate.
- Blockify open documentation (GitHub) — public reference for the ingestion, distillation, and governance pipeline.
- Blockify’s ingestion, distillation, and governance methods are patented by Iternal Technologies.
The complete methodology is published on this page and in the Blockify Performance Analysis — formulas, dataset scope, and derivations included.
Frequently Asked Questions
In documented benchmarks Blockify delivers up to 78X aggregate RAG accuracy improvement across enterprise deployments, driven by 2.29X more accurate vector search (a 56.26% precision gain) and up to 40X data reduction. On a single Big Four consulting-firm dataset the measured aggregate was 68.44X. All figures come from Iternal Technologies' Blockify Performance Analysis.
78X is the cross-deployment aggregate average. It combines the measured vector-search accuracy gain with data-volume reduction. On the transparent Big Four worked example, a 4.56X base improvement (2.29X vector accuracy × 2.00X word-count reduction) is multiplied by roughly 15X — IDC's average enterprise data-duplication factor (8:1 to 22:1) — for a 68.44X enterprise aggregate. The full calculation is published in the Blockify Performance Analysis.
Distilled IdeaBlocks average ~98 tokens versus ~303 tokens for a naive chunk, a 3.09X reduction in tokens processed per query. At 1 billion annual queries priced at $0.72 per million tokens (Llama 3.3 70B), that is an estimated $738,000 per year in savings, alongside lower latency and compute.
In an "Evaluation of Blockify" study across nine clinical questions, Blockify improved combined accuracy and source fidelity by 261.11% on average versus chunking, peaking at 650% on diabetic-ketoacidosis management and 500% on pneumonia lab tests. In one case, chunking recommended D5W dextrose as an initial IV fluid where isotonic saline is protocol; Blockify avoided the error.
On average Blockify shrinks a corpus to roughly 2.5% of its original size — up to 40X smaller. The measured base reduction on the Big Four dataset was 2.00X by word count, rising to 29.93X once enterprise-wide duplication (IDC's 15:1 average) is factored in. Smaller datasets mean fewer tokens, lower cost, and faster human review.
Yes. The full methodology, dataset scope, distance formulas, and token assumptions are published in the Blockify Performance Analysis and the healthcare evaluation, both linked on this page. Cite as: "Iternal Technologies, Blockify Benchmark, iternal.ai/blockify-benchmarks."