Enterprise Compress

117× compression.
0% false positives. Verify on Qdrant.

Enterprise embedding compression with deterministic safety lens. Production · AWS Marketplace Ready · v1.0.0-rc18.

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Per-document footprint

WP-20260113-01 · deterministic · signal-physical

ProductionAWS Marketplace Ready8/8 Connectors10/10 Step-Handlers0 HIGH Vulnerabilities

At 1 billion documents

The same RAG pipeline. Two cost envelopes.

Standard Stack

Storage: 4 TB
Cloud cost: $100,000/yr
Bandwidth: 4 TB/sync

With AQEA

Storage: 35 GB
Cloud cost: $850/yr
Bandwidth: 35 GB/sync

117× smaller. 99.15% cost reduction.

Section 01

Hallucination as compliance liability.

Hallucination liability

Clauses with opposite meaning — shall vs shall not — sit too close in embedding space. False-positive retrievals get cited, audited, litigated.

RAG storage cost

1B embeddings at 1024D float-32 ≈ 4 TB on disk and roughly $100,000/year in cloud hot-tier storage.

Compliance risk

Legal, medical, financial RAG pipelines need provable controls, audit trail and zero-knowledge deployment paths from day one.

Section 02

Compress + Lens architecture.

Two layers, one deployable artefact. Drop into your existing vector-DB, keep your embedding model, your RAG framework, your prompt-chain. No retraining, no re-ingest.

  ┌────────────────────────┐    ┌──────────────────────┐
  │  Your embedder         │ →  │  Lens  (~45 KB)      │  ← domain steering
  │  (E5 / BGE / OpenAI)   │    │  matrix multiply     │     legal · medical · financial
  └────────────────────────┘    └──────────┬───────────┘
                                           │
                                           ▼
                                ┌──────────────────────┐
                                │  Compress  (117×)    │  ← deterministic, signal-physical
                                │  1024D → 11D         │     93–99% quality preservation
                                └──────────┬───────────┘
                                           │
                                           ▼
                          ┌────────────────────────────────┐
                          │  Vector-DB (Qdrant · pgvector) │
                          │  S3 · Azure Blob · GCS · HTTP  │
                          └────────────────────────────────┘

Section 03

Validated performance.

LexGLUE 50k, EDGAR, ContractNLIv2 — reproducible against a live read-only Qdrant Cloud endpoint.

Metric	Value	Source
Compression	117–1,229×	WP-20260113-01
Quality Preservation	93–99%	WP-20260113-01
False-Positive Rate (LexGLUE 50k)	56% → 0%	WP-20260109-01
HitC@10 (Correct Hit)	80.0% → 83.2%	WP-20260109-01
Storage (1B embeddings)	4 TB → 35 GB	WP-20260113-01
Cloud Storage Cost (1B embeddings)	$100k → $850/yr	WP-20260113-01
Qdrant Live-Bench	1024D → 11D, 6.9× faster ingest	WP-20260113-02
Lens Weight Artefact	~45 KB	WP-20260109-01

Section 04

One container. Two architectures. Zero config.

1.42 GB · multi-arch · amd64 + arm64

1.42 GB

All-in-One image

amd64 + arm64

Multi-arch

8 / 8

Connectors

10 / 10

Pipeline Step-Handlers

8 Connectors

s3
qdrant
pgvector
azure_blob
gcs
fs
sharepoint
http

10 Pipeline Step-Handlers

ingest
chunk
embed
lens
compress
index
verify
audit
export
sync

Security on day one

bcrypt-12 · CSRF · hash-chained audit log · rate-limiting · API + Auth

Drops into your stack day one. Same container, same SHA-256 manifest, same multi-arch image — whether you ship to AWS, Azure, GCP, on-prem, or air-gapped.

Verify it yourself