No-code machine learning for factories, classrooms, and applied research.
Modliqer helps manufacturing teams, teachers, students, professors, and research scholars explore data, run EDA, compare ML models, validate results, and generate professional reports — without writing code.
Modliq Console — Guided No-Code Workflow
Modliq Gating: Engineers stay in control. Every step requires clear confirmation before ML setpoint recommendations are generated.
One no-code ML platform. Two practical use cases.
Analyze data, build predictive models, and prove results without writing code — tailored for manufacturing plants and academic institutions.
For Manufacturing Industries
Turn production logs into optimization, SPC, Cp/Cpk, OEE, supplier traceability, and buyer-ready Quality Passports.
Key Capabilities
For Education & Research
Teach, learn, and apply EDA, data visualization, model comparison, feature importance, and research reporting without complex Python setup.
Key Capabilities
Modliq for Manufacturing Industries
Manufacturing teams can use Modliq to convert Excel logs, QC reports, supplier records, machine data, and production databases into analysis, optimization, quality validation, and buyer-ready evidence.
Quality & SPC Math
Calculate process capability indices (Cp, Cpk, Pp, Ppk), X-bar/R control limits, defect rates, and subgroup statistics verified by a single Python engine.
PPAP / ISIR & Quality Passport
Generate buyer-accepted Part Submission Warrants (PSW) and Initial Sample Inspection Reports (ISIR) with traceable Math Verification Records for OEM buyers.
OEE & Process Optimization
Analyze equipment availability, performance loss, quality yield, downtime Pareto, and run constrained AutoML process setpoint optimization.
Modliq for Education & Research
Teachers, professors, students, and research scholars can use Modliq as a no-code environment for data analysis and machine learning practice.
Teachers
Create interactive classroom demonstrations for EDA, data visualization, and model comparison without managing complex coding environments.
Professors
Teach applied analytics, quality engineering, AutoML, and manufacturing data science with structured workflows and repeatable examples.
Students
Learn data analysis and machine learning visually. Upload datasets, ask questions, compare models, and understand results step by step.
Research Scholars
Use Modliq for early-stage exploratory analysis, feature discovery, visualization, model benchmarking, and research report preparation.
How Modliq guides manufacturing decisions.
Click through the 6 stages below to see what the user does, what Modliq calculates, and what output is generated.
01. Ingest Data
No-Code StepUploads CSV/Excel files, extracts tables from PDF/Word, or connects read-only Supabase/Postgres or MongoDB databases.
Auto-maps column data types, identifies target metrics vs controllable process features, and parses batch timestamps.
Clean dataset preview & structural column profiling schema.
Transparent methods, not black-box claims.
Modliq clearly separates predictive machine learning, deterministic quality calculations, and AI language assistance. Modliq calculates. AI explains. Engineers approve.
1. No-Code ML Engine
Surrogate models trained on historical plant data to predict outcomes and recommend safe setpoints.
- • Random Forest & Gradient Boosting
- • SHAP feature driver rankings
- • Constraint-bounded optimization
- • Safe parameter trial windows
- • Model Zoo: 16 Regression Models
2. Engineering Calculations
Exact mathematical and statistical formulas computed directly without neural hallucination risks.
- • Dataset readiness score (0–100)
- • SPC control limits (UCL / LCL)
- • Cp & Cpk process capability index
- • OEE (Avail × Perf × Qual) & AQL tables
- • Pure Math & Statistical Standards
3. AI Copilot Assistance
Multi-provider LLM gateway assisting engineers with explanations, SOP drafts, and CAPA summaries.
- • Natural language goal parsing
- • SHAP driver plain-English translation
- • CAPA action plan drafting
- • Standard Operating Procedure (SOP) drafts
- • Guardrailed Multi-Provider Gateway
Manufacturing-Specific ML vs Generic Tools
Generic AutoML tools can train models, but they don't understand manufacturing workflows like SPC, Cp/Cpk, OEE, supplier lots, trial SOPs, or Quality Passports. Modliq is no-code ML built specifically for factory process decisions.
| Capability / Feature | Generic AutoML | Modliq Platform | Why It Matters |
|---|---|---|---|
| Manufacturing natural language goal parser | No | Yes | Extracts target, direction & plant limits |
| Visual Review & Confirm setup wizard | No | Yes | Gating safety check before model runs |
| Dataset Health check & target leakage warnings | Basic | Yes | Tailored to plant sensor & lab data |
| Statistical Process Control (SPC & Cpk math) | No | Yes | I-MR control charts & capability |
| 7-Batch trial SOP generation | No | Yes | Step-by-step factory trial instructions |
| Buyer-Ready Quality Passport | No | Yes | Audit evidence report for OEM buyers |
| OEE calculator & downtime Pareto | No | Yes | Operations & line bottleneck metrics |
| Supplier material lot risk traceability | No | Yes | Correlates vendor lots to batch yield |
Everything you need to know about Modliq for industry and education.
Analyze data. Build models. Prove results — without code.
Whether you are a manufacturer, teacher, student, professor, or research scholar, Modliq helps you explore data and machine learning without code.