Modliqer is a no-code manufacturing intelligence and machine learning platform that helps factory teams analyze production data, optimize process settings, validate quality, and generate buyer-ready Quality Passports.

Modliqer is for manufacturing companies, factory owners, plant heads, quality managers, process engineers, operations managers, supplier quality teams, and lean improvement teams.

Do I need a data scientist to use Modliqer?

No. Modliqer is designed so manufacturing teams can use analytics and machine learning workflows without hiring a data analyst, data scientist, or ML engineer to get started.

What is a Quality Passport?

A Quality Passport is a buyer-ready report generated by Modliqer that summarizes dataset readiness, process capability, optimization evidence, operations performance, supplier traceability, and improvement actions.

Is Modliqer an AutoML platform?

Modliqer includes no-code AutoML workflows, but it is not a generic AutoML tool. It is designed specifically for manufacturing workflows such as yield optimization, SPC, Cp/Cpk, OEE, supplier risk, and Quality Passport reporting.

Modliq

⚡ Modliq Manufacturing Intelligence

Dual-Stack AI & Machine Learning Architecture

MODLIQER — Models, Operations, Data, Learning, Intelligence, Quality, Engineering & Research

Analyze data. Build models. Prove results — without code.

Last verified: 17/08/2026

Dual-Stack Architectural Architecture

MODLIQER explicitly formalizes classical predictive tabular ML alongside unstructured Generative AI and autonomous agentic state machines.

1. Traditional ML Stack

Predictive & Manufacturing

Deterministic structured data processing, tabular modeling, SPC Quality Passports, and real-time process monitoring.

✓ Data: MongoDB Atlas, Postgres/Supabase connectors
✓ Frameworks: Scikit-Learn, AutoML Leaderboard, Optuna (Beta)
✓ Explainability: SHAP drivers & feature importances
✓ Registry: Joblib & ONNX export abstraction

2. Generative AI & Agentic Stack

Unstructured & Autonomous

LLM orchestration, retrieval-augmented generation, page-cited PDF search, and approval-gated agent state machines.

✓ Gateway: Groq, Gemini, NVIDIA, Cohere, OpenRouter
✓ Vector DB: Qdrant vector database (Beta)
✓ RAG: DocuMind PDF extraction with page citations
✓ Agents: Agent Task Pilot with human approval gates

Service Boundaries & Security Isolation Topology

System Architecture

User Browser→Next.js Frontend (React)

↓

Express API Gateway(Node.js / TypeScript)

↓

MongoDB AtlasPrisma ORM

Redis + BullMQJob Queue

FastAPI ML EnginePython / scikit-learn

↓

AI GatewayGroq · Gemini · NVIDIA · OpenRouter · Cohere · Cloudflare

Security: Frontend never talks directly to ML or AI providers. API keys remain server-side. Connector credentials are encrypted. ML compute endpoints are protected by service key authentication. Project data is scoped by user and organization.

Security & Credential Isolation Principles

Frontend never calls ML or AI providers directly

All calls are mediated through the Express API Gateway.

Credential Vault isolation

Agents receive credential reference IDs only; raw secrets are never passed to agents or clients.

Vector DB protected

Qdrant vector search DB is never exposed to browser clients.

ML engine compute-only

FastAPI ML engine runs pure compute with service key authentication.

Grounded RAG citations

DocuMind answers cite exact document pages to prevent hallucinated claims.

Human approval gates

Risky agent actions require explicit user approval before execution.