AI That Works in Production.

Context, Control, Confidence. The three things your AI stack is missing, and the only AI orchestration platform built to deliver all three.

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You need Meibel if

RAG works in demo. It fails in production

Every team building on LLMs hits the same wall: retrieval that degrades, agents that behave differently between runs, and outputs no one can verify. The stack is not broken. It's incomplete.

Your retrieval is probabilistic

Results change across loads, data updates, and versions. No one knows why, and no one can reproduce the failure.

Your agents are unpredictable

Execution paths vary between runs. Tool calls fail silently. You only find out when the final answer is wrong.

You can't measure trust

The model sounds confident whether it's right or wrong. Evals look fine. Production doesn't match.

The Solution

One Orchestration Engine. No glue code

Meibel replaces the brittle stack of DIY RAG, custom preprocessing, MCP gateways, and observability tools. Point it at your data. Build. Ship.

Ingest & Understand

Build Agents

Score & Verify

Embed or Deploy

Context

Ingest & Understand

Connect any data source, Cloud or API. Meibel automatically classifies, segments, and structures every element: charts, tables, images, text, and cross-document references.

Control

Build Agents

Compose agentic workflows with your data as tools. Attach external capabilities. Your data, external APIs, and LLMs work together in a single, inspectable execution environment.

Workflow & Agent Control Runtime Governance

Confidence

Score & Verify

Every output is evaluated across 14 confidence dimensions. See exactly which sources drove the answer and how relevant each was.

Control

Embed or Deploy

Integrate via API or SDK into your existing application. Use webhooks to trigger downstream workflows. Or publish a hosted UI for non-technical users.

Context

Your Data, Understood by AI.

Standard RAG fragments your documents. Meibel understands them. Every uploaded file is broken down into its structural components: charts, tables, images, text blocks, and cross-references. Then, it’s classified and stored in the representation that makes retrieval accurate.

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Control

Build AI agents. See every step.

Compose agentic workflows where your own data becomes a tool. Attach external APIs, configure fallback models, and define the exact execution behavior you need. Every decision in the pipeline is traceable; the platform records it by design.

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Confidence

14-Dimensions of AI Confidence.

Meibel evaluates every output across 14 dimensions: LLM-based judges for coherence and completeness, and NLP-based techniques for source grounding. The result tells you precisely where to trust, where to question, and what to block before it reaches production.

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Data Ingest

Any Data Format. Enterprise Volumes.
No preprocessing

Point Meibel at your data. It handles classification, processing, and structuring. No pipeline to build, no schema to define,
no infrastructure to manage.

Use Cases

From Idea to Working AI

One data corpus. Multiple experiences. Meibel lets you process your data once and build as many solutions as you need on top, without reprocessing or rebuilding your pipeline.

AI Orchestration Platform

RECOMMENDED

DIY Stack

RAG

DIY Stack

Custom Agents

Bottom Line

Production-Ready from Day One.

No infrastructure to manage, no glue code to write. Go from data to working agent without building a platform first.

Demo-ready. Production-fragile.

Fast to start, expensive to scale. Quality degrades as data grows. No visibility into what failed or why.

Flexible. Expensive to operate.

You're building and maintaining a platform. Context engineering, confidence scoring, and drift detection are all yours to solve.

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AI Orchestration Platform

RECOMMENDED

DIY Stack

RAG

DIY Stack

Custom Agents

Bottom Line

Production-Ready from Day One.

No infrastructure to manage, no glue code to write. Go from data to working agent without building a platform first.

Demo-ready. Production-fragile.

Fast to start, expensive to scale. Quality degrades as data grows. No visibility into what failed or why.

Flexible. Expensive to operate.

You're building and maintaining a platform. Context engineering, confidence scoring, and drift detection are all yours to solve.

01 — Context

AI Orchestration Platform

Tables, charts, images

Tables stay tables. Charts are auto-described. Images classified and captioned. Structure survives ingest end-to-end.

Text splitters destroy tabular structure. Charts become orphaned tokens. Models receive text soup.

Possible with custom parsers, but requires significant engineering per document type.

Cross-document reference graphs

Implicit + explicit links

Both explicit citations and implicit references identified at ingest. Graph built automatically. Retrieval follows the full chain.

Chunks are isolated. Cross-references never resolve. Regulatory and legal documents are effectively broken.

Not addressed by agent frameworks. Requires a separate knowledge graph layer to build and maintain.

Mixed modality retrieval

Structured + unstructured

CSV auto-routes to SQL for precision. Documents route to semantic retrieval. Both work in the same agent, automatically.

RAG is semantic-only. Structured data fed to LLMs produces approximate results. Math is consistently unreliable.

Possible by combining retrieval backends, but routing logic must be hand-built and maintained per use case.

Visual element comprehension

Charts, diagrams, blueprints

Charts and diagrams auto-described during ingest. Architectural blueprints processed for semantic retrieval. Zero manual labelling.

Images are discarded or embedded as opaque blobs. No semantic content extracted.

Requires a multimodal pipeline and OCR integration. Adds significant complexity and maintenance.

Scalable enterprise ingest

S3, SharePoint, GCS, API

Native connectors for enterprise storage. No published volume limit. Async processing with webhook notifications.

Ingestion pipelines must be written and hosted. Connector maintenance falls on your team.

Same as Standard RAG: custom connectors required, with ongoing maintenance overhead.

02 — control

Reproducible execution

Same input → same path

Execution paths are deterministic and inspectable. Every step recorded. Any run can be replayed with exact context and decisions.

Single-shot. No execution record. Behavior changes across embedding updates and data changes with no visibility.

Frameworks offer tracing, but reproducibility requires custom logging infrastructure to build and maintain.

Full execution trace

Step-by-step visibility

Every tool call, retrieval query, token count, and model decision is visible. Sources linked to exact document passages.

No trace. You see input and output. What happened in between is opaque.

Available via third-party tools (LangSmith, Arize, etc.), but requires separate integration and adds cost.

Model selection + fallback

Per-agent LLM config

Choose any model per agent. Configure fallback models for production reliability.

Model is configurable, but fallback logic and routing must be built and maintained manually.

Configurable in most frameworks, but production fallback and versioning require a custom implementation.

Infrastructure management

Hosting, scaling, ops

Your data sources become agent tools natively. Combine internal retrieval with web search or external APIs in one workflow.

No agentic capability. Single retrieval call, single response. Cannot iterate or use multiple tools.

Core strength. Custom agents are designed for tool orchestration, but data retrieval logic must be hand-wired.

03 — Confidence

Output confidence scoring

14-dimension evaluation

Every output scored across coherence, faithfulness, context grounding, and 11 additional dimensions. LLM judges + NLP techniques.

No confidence evaluation. The model returns an answer. Whether it's right is your problem.

Basic evals possible, but multi-dimensional confidence scoring requires custom implementation or third-party eval tools.

Source grounding heuristics

Fabrication vs. retrieval

Detects when outputs use prior model knowledge vs. your data. Flags answers where key terms don't appear in retrieved context.

No mechanism to distinguish retrieval-grounded answers from fabrication. Silent failures.

Not provided by agent frameworks. Requires custom grounding checks, which significantly increases evaluation complexity.

Source traceability

Answer → document → passage

Every answer links back to the exact document passage that informed it. Relevance score per source. Full audit trail by design.

Chunk-level source retrieval possible, but passage-level linking and relevance scoring require custom work.

Possible with careful tool design, but requires explicit citation logic wired into every agent step.

Threshold-based automation

Block, escalate, or retry

Low-confidence outputs are automatically flagged, blocked, or escalated based on configured thresholds.

Always returns something. No mechanism to withhold or escalate low-quality outputs.

Possible if you build a confidence evaluation layer and wire it into your agent's control flow, which is costly.

Quality drift detection

Across model/data changes

Drift signals across retrieval quality, context completeness, and model behavior. Catch regressions before customers do.

No monitoring. Quality degradation after embedding updates or data changes is invisible until users complain.

Requires a separate monitoring layer integrated into your pipeline. Adds cost and operational complexity.

Human annotation loop

Calibrate to ground truth

Confidence scores calibrate to your human annotations, not abstract benchmarks. Small sample sets. Continuous improvement without redeployment.

No feedback loop. Prompt tuning is the only lever, and its effect on quality is unpredictable.

Possible with RLHF pipelines or custom eval harnesses, but requires significant ML infrastructure investment.

Use Cases

Your Data. Your Use Cases. Meibel Infrastructure

Meibel is on a mission to make explainable AI the standard. We envision a world where AI systems are transparent, accountable, and harnessed for the benefit of all.

From your data

Automated Contract Summarization with Confidence Scoring

Contracts reviewed in minutes, with summary + clause map
80–90% of routine summaries handled automatically
Legal teams trust AI suggestions due to clear confidence thresholds
Every summary field tied to source clause for compliance and audit

Manufacturing

Intelligent Document Extraction for Quoting and Compliance

95% of structured fields extracted without manual review
High-risk values flagged before reaching downstream systems
Audit-ready: every number traceable back to the original PDF
Integration with quoting, billing, and compliance workflows

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Ready to Build Confident AI?

See how Meibel delivers the three Cs for AI systems that need to work at scale.

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AI That Works in Production.

RAG works in demo. It fails in production

Your retrieval is probabilistic

Your agents are unpredictable

You can't measure trust

One Orchestration Engine. No glue code

Ingest & Understand

Build Agents

Score & Verify

Embed or Deploy

Context

Your Data, Understood by AI.

Control

Build AI agents. See every step.

Confidence

14-Dimensions of AI Confidence.

Any Data Format. Enterprise Volumes. No preprocessing

From Idea to Working AI

Your Data. Your Use Cases. Meibel Infrastructure

Automated Contract Summarization with Confidence Scoring

Intelligent Document Extraction for Quoting and Compliance

Ready to Build Confident AI?

Any Data Format. Enterprise Volumes.
No preprocessing