Build AI That Actually Understands Your Data.

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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Why you need meibel

Most AI Stacks Give You Fragments.  Meibel Gives You Answers

Every team building on LLMs hits the same wall: retrieval that degrades, agents that behave differently between runs, and outputs no one can verify. Your 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.

Your documents contain charts that reference tables, tables that reference earlier sections, and paragraphs that only make sense with both. Typical AI pipelines flatten them, but 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.

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.

Confidence

14-Dimensions of AI Confidence.

Most confidence frameworks judge the output. Meibel instruments the entire system. Every tool call, retrieval step, and model decision contributes to an end-to-end fidelity signal, evaluated across 14 dimensions using LLM-based judges for coherence and completeness, and NLP-based techniques for source grounding. By the time an answer surfaces, you know exactly how it was built, where to trust it, where to question it, and what to block before it reaches your users.

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. Formats built for human eyes are made machine-readable. Meibel lets you process your data once and build as many solutions as you need on top, without reprocessing or rebuilding your pipeline.

Bottom Line

AI Orchestration Platform

Meibel

RECOMMENDED

DIY Stack

RAG

DIY Stack

Custom Agents

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.

Learn More

AI Orchestration Platform

Meibel

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

Document structure preservation

Tables, charts, images

Charts become structured tables and searchable text. Images are described and captioned. What was visual is now retrievable.

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. Complex document formats are effectively broken.

Often costly, complex and indiscriminate in its use of graph tools. Requires a separate knowledge graph layer to build and maintain.

Mixed modality retrieval

Structured + unstructured

Meibel intelligently adapts the retrieval modality to the representation of the data: automatically, within the same agent.

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.

Document comprehension pipeline

Visuals, charts, diagrams, blueprints

Tables are preserved, Meibel identifies sections, creates hierarchical awareness of document structure, builds meta data, internal citations and provenance tracking.

Images are discarded. Documents are not understood according to their hierarchy. No semantic content extracted.

Don't deal with data and require stringing together multiple tools with significant integration complexity and maintenance overhead.

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.

User-Defined Metadata

Filtering + ranking axis

User-defined metadata adds a deterministic second axis to ranking. When two chunks are semantically similar but contextually worlds apart, metadata makes the right call.

Ranking is embedding similarity alone, a blunt instrument. Semantically close chunks are treated as equally relevant regardless of context.

Metadata filtering is possible but must be manually wired into retrieval logic. No native support for using it as a ranking signal alongside semantic similarity.

02 — control

Reproducible execution

Same input → same path

Execution paths are fully auditable. 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

Combines tool calling and structured outputs across all models. 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.

Agentic capability

Data, APIs, external tools

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.

Infrastructure management

Hosting, scaling, ops

Fully managed. Scales with your data without infrastructure changes, additional configuration, or operational overhead. Connect your sources and build.

You own the vector database, embedding service, chunking pipeline and retrieval layer. Every integration adds complexity. Every scale event requires engineering work.

Highest overhead of any approach. Every tool, connector and orchestration layer is yours to build, scale and maintain. Complexity compounds as use cases grow.

Orchestration Controls

Rules, tools, multi-agent, HITL

Define rules and policies, configure tool calling and MCP, chain agents together, and gate outputs with human approval steps, all within the same execution environment.

No orchestration layer. Single retrieval call with no policy enforcement, tool calling, or approval mechanisms.

All of this is possible but must be hand-built. Each capability, MCP wiring, HITL gates, agent chaining, is a separate engineering investment.

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

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.

From your data

Enterprise Knowledge Agents

Connect your document corpus: contracts, reports, technical specs. Build agents that answer questions with full source traceability. Ideal for legal, finance, and compliance teams that need auditable answers.

REQUEST A DEMO

Take Control of Your AI

See Meibel on your data. Explore document processing, agent construction, and confidence scoring during our custom demo.

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Build AI That Actually Understands Your Data.

Most AI Stacks Give You Fragments. Meibel Gives You Answers

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

From Idea to Working AI

Enterprise Knowledge Agents

Take Control of Your AI

Build AI That Actually Understands Your Data.

Most AI Stacks Give You Fragments.  Meibel Gives You Answers

Any Data Format. Enterprise Volumes.
No preprocessing