Clustey

Survey Analytics · Statistical Modeling · Data Visualization

Where statistical rigor meets visual clarity.

Transform raw survey datasets and enterprise telemetry into publication-grade statistical charts, automated hypothesis tests, and cross-tab distributions in seconds.

Start Free Analysis Explore Visual Cells

REDCap, Qualtrics & CSV Sync

50+ SciPy Statistical Tests

Vega & Python Visual Enclaves

Dataset

N = 12,450 Responses

Significance

p = 0.0018 (Significant)

Cross-Tabs

4 Demographic Cohorts

Effect Size

Cohen's d = 0.86 (Large)

survey_hypothesis_model.ipynbLive Kernel

Survey Likert Scale (N=12.4k)

Mean: 3.88 / 5.0

Strongly Agree

5,11041%

Agree

3,49028%

Neutral

1,87015%

Disagree

1,24010%

Strongly Disagree

7406%

Reliability: Cronbach α = 0.89High Internal Consistency

Gaussian Density & 95% Confidence Interval

t = -4.82 · p < 0.001

Baseline Manual Sample (N₁ = 6,220)Optimized Flow (N₂ = 6,230)

-37.7% Variance Shift

Demographic Cross-Tabulation BreakdownANOVA F(3, 12446) = 18.4 · p < 0.001

Enterprise Orgs

4.42±0.48 SD

Growth Teams

4.15±0.62 SD

Academic & Labs

3.88±0.74 SD

Independent

3.54±0.81 SD

SciPy Automated Synthesis:"Hypothesis H₁ validated with 99.8% statistical power. Null hypothesis rejected."

Formats: CSV, REDCap, Vega-Lite, PNG

Trusted by operations teams at leading companies

ATLASNorthwindKeplerMeridianFoundryLumen

Interactive Analytics Engine

Visualize your data with instant clarity.

Explore inventory throughput and stock accuracy trends through reactive notebook chart cells rendered with classic Vega visualization standards.

[1]Columnquarterly_inventory_throughput.sql

12ms · 5 groups

X:quarterY:volume (k)

Q1 '25

Q2 '25

Q3 '25

Q4 '25

Q1 '26

Current Quarter:104.2k units (Q1 '26)

+28.4% vs Q1 '25

[2]Linestock_accuracy_trajectory.sql

15ms · 8 snapshots

X:weekY:accuracy (%)

Actual

Baseline Target

Actual vs Baseline:99.4% vs 96.2% (W8)

Target exceeded (+3.2%)

Event-Driven Orchestration

Workflow automations, triggered by your data.

Connect inventory events to automated sequential and parallel actions. Define your trigger conditions and let background pipelines execute procurement, re-routing, and reconciliation in seconds.

Real-Time Event Bus

Deterministic · Parallel Pipeline Support

Auto-Replenish Low Stock

Triggered 84 times today

Stock < Safety Buffer

Create ERP PO

Notify Warehouse

Auto-Dispatch Order

Supplier SLA Delay Penalty & Backup Route

Triggered 14 times this week

Inbound > 48h Late

Deduct SLA Credit

Re-Route Stock

Issue Cure Notice

Cycle Count Variance Reconciliation

Runs Daily at 06:00

RFID Scan Uploaded

Match ERP Balance

Audit Ledger Variance

Post Journal Voucher

Unusual Demand Spike Allocation Guard

Real-time webhook listener

Order Velocity > 3σ

Lock Priority Reserve

Verify Credit Line

Escalate to Ops

100% Deterministic Execution·Parallel Branching Supported

Inference & Hypothesis Testing

Statistical proof, not guesswork.

Verify operational changes with automated two-sample t-tests and ANOVA variance modeling. Plain-language inference engines turn complex p-values into actionable decisions.

t-Statistic

-4.82

Negative indicates reduction

p-value

0.0018p < 0.05

p < 0.05 threshold

Deg. of Freedom

248

df = N₁ + N₂ - 2

Effect Size (Cohen's d)

0.86

Large practical impact

[Stat-1]Two-Sample Independent t-Testfulfillment_latency_ttest.py

Confidence: 99.82%

Group Means Comparison & Confidence Bounds

Legacy

Stackwise

Delta: -8.6 hours (-37.7%)Significant difference (p < 0.05)

Group Descriptive Statistics Table

Group	N	Mean	SD	95% CI
Legacy Route (Manual)	125	22.8	4.2	[22, 23.6]
Stackwise Flow (Automated)	125	14.2	3.1	[13.6, 14.8]

Null Hypothesis (H₀):μ₁ = μ₂ (No Difference)

Alternative (H₁):μ₁ ≠ μ₂ (Significant)

Hypothesis Verdict:Reject H₀ (p = 0.0018)

Calculated via Welch's variance estimation with unequal variance correction.

Inference InsightStatistically Significant (p = 0.0018)Confidence: 99.82%

Strong Evidence of Genuine Operational Improvement

The p-value of 0.0018 means there is only a 0.18% chance that this 8.6-hour speed advantage happened by random luck. Because p < 0.05, we have 99.82% statistical certainty that the automated workflow genuinely speeds up fulfillment.

Recommendation: The efficiency leap is statistically confirmed. Fully transition the remaining warehouse routes from the legacy manual process to the Stackwise automated flow to capture consistent 37% cycle time gains.

Executive Verdict

Deploy to Production

Risk of false positive is less than 0.2%.

Confidence99.8%

Autonomous Agentic Intelligence

Delegate to AI. From chat to autonomous actions.

Don't just query data — put AI to work. Chat naturally with your workspace, or deploy autonomous agents powered by world-class frontier models to audit anomalies, draft purchase orders, and execute end-to-end tasks.

Frontier Model Agnostic

Select your engine · Zero vendor lock-in

Select Frontier Intelligence Engine:

AnthropicFrontier Reasoning

Claude 3.7 Frontier

Active Engine

OpenAIHigh-Speed Execution

GPT-4o & o1 Frontier

Switch

GoogleMassive Context

Gemini 2.5 Frontier

Switch

Autonomous Execution Graph[Live Pipeline]

Scenario:

prompt >"Audit low inventory thresholds and trigger supplier PO"Synced

Stage 01 · Telemetry

Warehouse Stock Ledger1,420 SKUs

Safety Buffer ThresholdsActive

Supplier Lead TimesEDI Feed

Real-time Ingest Active

Frontier Inference

Anthropic

Claude 3.7 Frontier

Zero Data Retention (ZDR)

Stage 02 · Execution

PO #9418 Drafted ($14.2k)

Buffer Updated (18d)

Dispatcher Notified

Automated Dispatch

Cryptographically Isolated Inference · Enterprise AuditedEngine: Anthropic (Claude 3.7 Frontier)

Conversational Chat

Autonomous Tasks

Frontier Flexibility

Human-in-the-Loop

Security & Compliance

Built on enterprise-grade trust & compliance.

Clustey adheres to rigorous global security standards to ensure your inventory, operations, and business data remain protected, private, and continuously compliant.

SOC 2 Type II

EU / GDPR

ISO/IEC 27001

HIPAA Compliant

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