NEURAL FABRIC INGESTION[NODE 01-CORE]

Not BI with an AI chat box. BI rebuilt as AI — every layer, ingest to report.

Autonomous tabular pipeline that auto-profiles ingestion streams, instantly snaps PII shields, and synthesizes verifiable multidimensional reports without hallucinated joins.

neural-pipeline-daemon — ingest-v2.80.14ms latency

01AUTO-PROFILE CSVSTREAMING

schema: inferred 48 cols • 1.2M rows • type: float64

02PII SHIELD SYNAPSEACTIVE

SHA-256 [SSN]MASK [EMAIL]TOKEN [IBAN]

03DYNAMIC SYNTHESISRESOLVED

01 // CORE COMPARATOR

10-Stage Signal Propagation Fabric

CompleteAutomatedSelf-LearningSecure

Incumbent AI StackSiloed (Point Solution)

01Ingest

02Cleanse

03Vectorize

Dropoff

04Route

05Ground

06Reason

07Synthesize

08Verify

09Audit

10Govern PII

Storek Continuous Mesh100% Phase-Locked

✓Ingest

✓Cleanse

✓Vectorize

✓Route

✓Ground

✓Reason

✓Synthesize

✓Verify

✓Audit

✓Govern PII

10 / 10Stages Active

0.00msHandoff Friction

100%Context Retained

Signals traverse from raw data ingestion to hardened PII governance without leaving deterministic memory bounds.

<1.4msLoop Trigger

ZeroManual Glue Code

AutoFallback Routing

Self-healing telemetry auto-reroutes degraded model endpoints instantly with sub-millisecond failover.

LiveWeight Fine-Tuning

99.8%Synthesized Fidelity

AdaptiveContext Windows

Every verified inference feeds stage weights backwards, dynamically optimizing future grounding precision.

AES-256In-Cell Crypto

0%PII Leakage

ImmutableTrace Ledger

Stage 10 enforces boundary compliance at the hardware abstraction layer, isolating sensitive customer payload.

02 // VECTOR ROUTING

Dynamic Signal Bus

Latency0.84ms

Bandwidth40 Gbps

Jitter±0.01μs

03 // STAGE 10

PII Isolation Gate

🔒ZERO-LEAK

Cryptographic Ring

Anonymizes tokens prior to vector indexing with deterministic reversal barriers.

04 // CONCURRENCY

Self-Healing Matrix

100% Node Coherence

Autonomous state replication across all active computational cells.

NODE:01 // INGEST_VECTOR

Telemetry interface showing initial vector ingestion

01/ 04

Prompt Ingestion & Context Anchor

Surface complex dimensional requests across enterprise silos. Storek registers constraints, historical token trees, and perimeter boundaries in milliseconds before graph traversal begins.

Token NormalizationVector Anchor

NODE:02 // DYNAMIC_ROUTING

Dynamic network graphs adjusting runtime weights

02/ 04

Parametric Steering & Graph Assembly

Steer generation with explicit node boundaries. Observe how intermediate representations balance certainty scores across parallel validation channels without synthetic hallucinations.

Heuristic SteeringMulti-agent Sync

NODE:03 // OUTLIER_PRUNING

Auditing interface pinpointing and excluding anomalous datapoints

03/ 04

Instant Outlier Exclusion & Pinning

Isolate rogue signals with single-click precision. When an anomalous premise surfaces, prune it immediately from the active context graph and pin deterministic ground-truth nodes.

Live PruningState Freeze

NODE:04 // VERIFIED_EXPORT

C-suite audit document render preview with cryptographic verification badge

04/ 04

Cryptographic Seal & Branded PDF Export

Compile exhaustive execution transcripts into verifiable, board-ready audit dossiers. Every output embeds cryptographic provenance back to raw enterprise data seeds.

Audit ProvenanceExecutive Dossier

mcp-daemon :: runtime-sandbox:04

Mode:RAW SQLVIA CLAUDE (MCP)

01SELECT user_id, full_name, email AS contact

02FROM leads

03WHERE mcp_envelope = 'strict-tier-1';

QUERY EXECUTION RECORDKERNEL_VERIFIED

#1usr_902Sarah Jenkinss.jenkins@leadflow.io

#2usr_903Alexander Vogela.vogel@enterprise.de

#1usr_902S**** J******s*****@l*****.com MCP MASKED

#2usr_903A******** V****a*****@e*****.de MCP MASKED

3-VAULT AIR-GAP ISOLATIONLIVE SYNCHRONIZATION

VAULT IModel Context IngestToken sanitizer & query validator

VAULT IIRedaction HypervisorHardware AES-256 GCM ephemeral runtime

VAULT IIIAutonomous SynthesisClaude MCP endpoint (zero cold-storage)

Hardware Security Module cluster running MCP isolation verification

HSM Enclave Telemetry

Cryptographic seal verified across physical clusters with zero-knowledge attestations.

GDPR Art. 32

Real-time pseudonymization and zero cross-tenant memory footprint.

HIPAA §164

Protected Health Information (ePHI) automatically excised before inference.

CCPA / CPRA

Granular opt-out enforcement at the Model Context Protocol layer.

PCI-DSS 4.0

PAN and cryptographic CVV filters isolated from AI prompt cache.

Autonomous Engine

Storek

Direct warehouse agent with live semantic graph

Bolt-on Layer

BI + AI Add-ons

Text-to-SQL wrappers on legacy dashboards

Native Feature

Warehouse AI

Vendor-locked assistant prompts

Point Solution

CSV Chatbots

Vectorized text chunks and RAG pipelines

In-House

DIY LangChain

Custom agents and brittle prompt chains

Hallucination GuardSchema consistency & join validation

0.0% Hallucination

Compiler verifies all joins against warehouse catalog before execution.

High Risk

Invents non-existent dimension keys on complex star schemas.

Medium

Synthesizes plausible queries without validating business metric logic.

Critical Risk

Blind similarity search returns disconnected table excerpts.

Continuous Drift

Requires custom evaluator harnesses that degrade over time.

Petabyte Scale LatencyPartition pruning & pushdown execution

Sub-second Execution

Pushes AST directly to Snowflake, BigQuery, or ClickHouse with zero egress.

Slow (15-40s)

Extracts wide aggregates into browser memory before computing answers.

Native Speed

Executes in-engine but lacks cross-database federation capability.

Crashes >100MB

Context-window limits fail entirely on enterprise warehouse scales.

Variable (10-60s)

Multiple iterative agent loops cause massive warehouse scan costs.

Security & GovernanceRow-level access control & audit logging

Strict IAM & MCP

Inherits native user permissions; zero raw data cached or exposed to LLMs.

Partial

Relies on dashboard viewer credentials, bypassing table-level guards.

Siloed

Limited to single-cloud governance trees; no audit exports to external tools.

Zero Control

Files ingested into 3rd-party vector stores with no access audit trails.

High Dev Burden

Engineers must hand-code RBAC middleware for every single tool endpoint.

Deployment VelocityTime to trusted production queries

< 15 Minutes

Auto-discovers semantic relations, constraints, and data distribution stats.

3 - 6 Months

Demands complete semantic layer re-architecture across legacy BI cubes.

1 - 2 Weeks

Requires manual tagging and custom SQL view curation per department.

Instant

Trivial CSV drop but completely useless for dynamic production datasets.

6+ Months

Ongoing engineering maintenance, eval suites, and prompt regression debt.

Bill ConsolidationAutomated Synergy

Unbundled Fragmentation vs Unified Sovereign Core

Watch high-variance SaaS tolls flow together and consolidate into a flat, predictable engine runtime.

Warehouse Compute

$4,200/mo spike tax

Per-Seat BI Licenses

$1,850/mo dead seats

GenAI Middleware

$2,900/mo token tax

STOREK UNIFIED

1 Fixed Instance

Zero token penalties • Zero idle seat charges

Net Infrastructure TCO

68%

Average reduction in cross-stack query compute & seat licensing over a 12-month window.

Zero-Egress Security

Your raw schema never trains a public foundational model.

Queries execute against cached semantic embeddings locally. Telemetry, tokens, and schemas remain inside your VPC enclosure.

Frequently Validated Questions

Direct technical answers to common architecture, safety, and integration inquiries.

How does Storek prevent hallucinations on financial and operational metrics?

Storek avoids open-ended generative guessing by decoupling semantic mapping from SQL generation. User intents are compiled into strict deterministic ASTs (Abstract Syntax Trees) verified against your verified schema catalog. If a required dimensional grain or join key is ambiguous, Storek prompts for human confirmation rather than synthesizing plausible but incorrect figures.

Can Storek coexist with our existing Snowflake, BigQuery, or Databricks deployment?

Yes. Storek acts as a high-velocity semantic intelligence layer sitting directly over your warehouse. It does not replace your storage or core transformations; it eliminates the brittle BI dashboards, ad-hoc analyst queue bottlenecks, and redundant reporting tables while honoring your warehouse permissions and role hierarchies.

How is enterprise data security and PII handled during query parsing?

Your customer records and private table contents never pass to external LLM providers. Only structural metadata and sanitized semantic descriptors are parsed during reasoning. All SQL execution occurs strictly within your provisioned VPC perimeter with zero-retention guarantees.

Why not build this internally using LangChain, LlamaIndex, or raw text-to-SQL?

Internal text-to-SQL prototypes invariably hit a 70% accuracy ceiling once joins, seasonal definitions, and metric definitions compound. Maintaining vector stores, schema sync pipelines, fine-tuned adapters, and PII guardrails requires 3–5 dedicated data engineers. Storek gives you continuous self-learning semantic graphs out of the box at a fraction of the headcount cost.

Governance & Intelligence Layer

One platform.
Every layer intelligent.
Every answer governed.

“A pilot is an upload, not a migration.”

0msSchema migration

Air-gappedEnterprise tenant isolation

100%Deterministic audit trail