COMING SOONIn Development

From scattered signals to decisions you can trust

Quality Intelligence connects complaints, process data and audit notes, finds the pattern behind them and suggests the next step. A person always decides.

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Quality signal console

Concept preview

Scanning incoming signals…

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Core Modules

Applications in Quality Management

Pick an area to see what it does and a real-world example.

Customer Needs & Feedback Analysis

Planned Capability

Aggregating customer complaints, warranty reports, post-market surveillance data, and satisfaction surveys to identify emerging defect patterns and understand evolving quality expectations before minor dissatisfaction escalates into systemic failures.

Realistic Business Use Case

A medical device supplier analyzes multi-channel complaint logs across quarterly releases to cluster recurring packaging seal issues, identifying lot-specific trends that warrant preventive packaging line adjustments.

Execution Flow

A Practical Quality Intelligence Workflow

Click through the steps: AI helps with the analysis, people make every decision.

Quality Data Ingestion

System Input

Gather operational logs, inspection measurements, customer feedback, and documented procedures from verified internal sources.

Key Governance Mandate: An AI model cannot inspect physical parts, calibrate measuring tools, or observe gemba conditions. Human review and output verification are non-negotiable steps before committing to any corrective action or process change.

System Design

Architecture Overview

Data Ingestion & Processing
Accepts structured inspection measurements, operational logs, and procedure documentation. Future multi-stream connectors will ingest batch inspection records.
Retrieval-Augmented Generation (RAG)
Rather than relying on ungrounded generative recall, the architecture pairs language models with retrieval-augmented generation. When synthesizing nonconformity drafts, the model queries internal standard operating procedures and historical CAPAs to reference exact organizational guidelines.
Model Integration & Fine-Tuning
Integration of foundational language models evaluated against quality engineering taxonomies (QMS terminology, 8D problem-solving structures). Domain fine-tuning is currently an exploratory development activity aimed at improving terminology precision and formatting consistency.
Evaluation Harness & Verification Gates
Systematic evaluation benchmarking model outputs against verified engineering ground truths. All generated drafts pass through mandatory human verification gates before incorporation into official quality records or export.

Ethics & Security

Responsible AI & Data Governance Principles

Data Minimization
Processing only the operational telemetry and quality records strictly required for the specific diagnostic or investigative task.
Access Control
Role-based governance ensuring that proprietary manufacturing specifications and sensitive customer data remain accessible solely to authorized personnel.
Appropriate Data Retention
Clear data lifecycle policies where investigation drafts and uploaded logs are retained only as long as necessary for review and compliance requirements.
Source Traceability
Every generated suggestion, cited standard clause, or synthesized root cause hypothesis links back to traceable source documents and raw records.
Human Oversight
A strict human-in-the-loop mandate where quality professionals retain sole responsibility and approval authority for all quality decisions and releases.
Model Output Validation
Systematic verification of AI-assisted outputs against physical shop-floor observations, calibrated measurements, and domain engineering judgment.
Privacy & Security Assessment
Regular evaluation of model endpoints, data transmission encryption, and local storage safeguards to protect organizational data integrity.

Current Status

Development Roadmap & Current Status

Live in QMS
Phase 1 — docamin: AI-Written Controlled Documents
- Policies, procedures, work instructions and forms written by AI from a short interview
- Automatic document numbering and revision history
- Multi-person review and approval with separation of duties
- Professional DOCX and PDF exports with header, approvals and revision history
- Documents in English, Indonesian, Japanese, Spanish, Italian and Portuguese
- One isolated workspace per client company, with a full audit trail
In Active Development
Phase 2 — AI-Assisted Synthesis & Retrieval
- Retrieval-Augmented Generation (RAG) prototype for querying internal SOPs
- Structured 8D problem-solving draft generator connected to QMS outputs
- Quality engineering domain benchmark harness for output validation
- Early pilot program framework with selected enterprise partners
Planned Roadmap
Phase 3 — Multi-Stream QMS Intelligence & Connectors
- Multi-channel customer complaint and warranty trend aggregation
- Multi-process SPC variance monitoring and anomaly alerts
- Documentation gap analysis against your QMS requirements
- Enterprise integrations with eQMS, ERP, and MES systems

Limitations & Intended Use Disclaimer

1. Human Review RequirementAI-generated summaries, causal suggestions, and draft action plans are assistive in nature and must be independently reviewed, verified, and approved by qualified quality professionals before operational implementation or regulatory submission.

2. Dependence on Input QualityThe reliability of AI-assisted outputs depends directly on the accuracy, completeness, and objectivity of input data. Biased, incomplete, or inaccurate records will produce flawed recommendations.

3. Regulatory Advisory NoticeQuality Intelligence provides general quality management support and does not guarantee QMS, regulatory, or FDA/cGMP compliance, audit success, or certification. It does not replace formal third-party conformity assessments, official registrar audits, legal counsel, or certified quality professional judgment.

Help shape Quality Intelligence

Join the early pilot and tell us which signals matter most in your plant.