Welcome to the official documentation for Meibel — the runtime platform for building, governing, and scaling explainable AI systems in production.
Meibel helps technical teams go beyond isolated model prototypes to create full AI Experiences that are traceable, measurable, and controlled. Whether you’re working with structured data, documents, logs, or APIs, Meibel equips you to build systems that explain themselves and improve continuously while live.
Our platform sits above the model — orchestrating how data is presented and retrieved, how decisions are made, how confidence is scored, and how feedback loops enable ongoing improvement. The value of AI compounds only when it’s running. Meibel makes that compounding reliable.
What Makes Meibel Different
Most AI deployments fail because they stop at the model. Meibel provides the missing runtime layer that operationalizes your system, monitors it in real time, and ensures outputs are trustworthy.Decision Traceability
Every AI decision is logged with full context — including data retrieved, tools invoked, models used, and scoring.
Confidence Scoring
Each output is scored in real time across relevance, correctness, faithfulness, and other quality metrics.
Adaptive Workflows
Orchestrates dynamic decision paths across tools, agents, and humans — adapting in real time.
Continuous Refinement
The system improves over time by observing outcomes and adjusting behavior without retraining.
Execution Control
Configurable rules determine how outputs are handled — including fallback, escalation, and approval.
How Meibel Works
Meibel is a hosted runtime platform. It handles every step of the AI decision process — from ingest and retrieval to scoring, orchestration, and refinement.1
Data Preparation & Structuring
Ingests structured and unstructured data, indexes across vector, memory, graph, and relational formats, and prepares inputs for context-aware retrieval.
2
Contextual Retrieval & Input Scoring
Dynamically retrieves data based on query and context, scores inputs for relevance, completeness, and trust, and selects top-ranked data for grounding model responses.
3
Output Evaluation & Execution Control
Evaluates outputs for quality, risk, and alignment, applies execution rules: fallback, escalation, suppression, or delivery.
4
Orchestration & Adaptive Workflows
Executes flows across agents, models, tools, and humans, supports multi-step tasks and memory coordination, and adjusts decisions based on scoring and context.
5
Continuous Refinement
Tracks usage signals and outcomes across flows, refines scoring, prompts, and decision logic with no model retraining required.