CONTROL
Confidence is verification. Score every output against its evidence. Enforce thresholds on what proceeds. Give teams the signals to trust, question, or block before anything reaches production.
WHY CONFIDENCE MATTERS
No way to score what an AI output is based on. Teams are stuck between two bad options: automate blindly or review everything manually. These are the symptoms.
The model sounds confident whether it is right or wrong. No signal tells teams which outputs are supported by evidence and which are fabricated. Every answer looks the same.
When an output is wrong, teams cannot tell whether the issue came from retrieval, a tool call, or generation. Failures blend into one broken result. No indication of where things went wrong.
Common queries may work. Edge cases and long-tail queries produce ungrounded content with no warning. Fabricated answers arrive in the same format and tone as correct ones.
Offline evaluations use synthetic data and controlled conditions. They pass in staging. In production, retrieval paths, context assembly, and tool behavior differ. Eval results stop reflecting what the system actually does.
Embedding changes, data updates, and workflow edits degrade output quality over time. No drift signals. Teams discover the problem when users complain, not when the system detects it.
Teams cannot verify what evidence supported a given answer. Claims arrive without provenance. Auditing means manually searching for the source, if it exists at all.
HOW CONFIDENCE WORKS
Confidence is not a model guess. It is a measurable signal derived from execution. Meibel evaluates confidence by measuring evidence quality, context completeness, and step-level behavior, then uses those signals to determine what proceeds, what retries, and what stops.
Every output receives a confidence score based on the quality and relevance of the evidence behind it. Meibel evaluates whether the retrieved context actually supports the claims in the output, not just whether context was present. This is not a model self-assessment. It is an independent evaluation of how well evidence maps to the generated result.
You define the agent: instructions, data sources, tools, and output schema. Hardrails enforce exactly what the agent can see and do at runtime, before the model call, during data access, and at every tool execution.
Agent definitions versioned, immutable
Execution policies enforced before the model runs
Approval gates on any step
One agent, unlimited scopes
Meibel scores every extraction and every agent step across independent dimensions: Correctness, Coherence, Completeness, Faithfulness, Relevance, and OCR Confidence. Scoring is built into execution, not bolted on after.
Evidence-Backed Decisions
Outcome Provenance
Decision-Aware Automation
How Meibel measures evidence quality, enforces confidence thresholds, and detects drift before it reaches production.
CONFIDENCE SIGNALS
Confidence is evaluated across retrieval, context assembly, tool execution, and model generation. Scores are consistent regardless of which model, tool, or data source is involved. When any component changes, the confidence layer detects the impact. No need to rebuild the evaluation framework.
WHAT ENTERPRISE AI TEAMS BUILD WITH MEIBEL
Retrieval by meaning is one of three modes, not the whole system. Standard RAG matches a query against chunks and returns passages. A Meibel datasource also keeps your structured data queryable by its columns and values, and keeps the references between documents traversable, and the agent chooses which mode fits the question. The platform recovers structure during ingestion rather than relying on similarity alone.
No. The datasource is separate from the agent definition. Agents bind to it, so changing a model or publishing a new agent version does not touch the prepared data.
Tables are rebuilt as grids with rows, columns and spanning cells, and stay addressable by cell position. Line and scatter charts are digitised back into series data rather than described in prose.
25+ formats including PDFs, office documents, images and scans, email and archives. Medical records are supported. CAD and blueprints are not.
There is no practical page ceiling. Documents up to 1 GB, and that limit is raisable per customer through the API.
No. Meibel orchestrates how data is prepared and used for AI without replacing your storage. It adds structure, metadata and provenance between your sources and your model calls.
No. Meibel orchestrates how data is prepared and used for AI without replacing your storage. It adds structure, metadata and provenance between your sources and your model calls.
Upload a real document, the messy kind. Get back structured data. Then build an agent on it and run the same thing at whatever volume you have.
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