The AI Orchestration Platform for Builders, by Builders
Meibel sits between your data, tools, and language models to deliver the three Cs of successful AI implementation: Context, Control, and Confidence.


When Demos Work but Deployment Breaks
Teams build with brittle stacks, such as DIY tooling, RAG pipelines, MCP gateways, custom preprocessing, observability layers, and glue code. Outputs stay inconsistent, sources stay unclear, and performance degrades as data changes.
Standard RAG destroys document structure. Retrieval returns fragments without provenance. Confidence scores arrive too late to act on.
That's the gap Meibel closes.

What Meibel Stands For
Our Mission
Build the runtime infrastructure that makes AI reliable, measurable, and controlled at scale.
Our Vision
Become the infrastructure layer for AI. The platform that handles complexity so builders can create value.

What Drives Us
Infrastructure should be invisible.
Standard RAG destroys document structure. Retrieval returns fragments without provenance. Confidence scores arrive too late to act on.
Reliability compounds in use.
Systems improve through feedback loops. Every cycle strengthens the next.
Built for builders.
We're engineers who've run infrastructure at scale. We build tools for product and engineering teams who need AI to work reliably.
Create new capabilities.
AI should unlock what wasn't possible before, not just optimize what exists.
Structure leads to reliability.
Reliability starts with structured ingest, not prompt tuning. Quality must be measured continuously at runtime.



The Three Cs of Meibel
Meibel replaces the brittle stack with one unified platform that delivers Context, Control, and Confidence throughout the execution path.
Context
Optimization
RAG destroys document structure. Tables become text. Relationships disappear. Meibel unifies structured, semi-structured, and unstructured sources, ensuring every AI decision is grounded in the right data at the right time and in the right format.
Control
AI Orchestration
Agent behaviour changes between runs. Same inputs, different outputs. No audit trail. Execution control defines how AI systems act what they can access and when humans stay in the loop across models tools and workflows.
Confidence
in AI Outputs
The model sounds confident whether it's right or wrong. Your team can't tell which outputs to trust. Understand why an AI produced a result, how it was generated, and whether it meets your standards before it reaches customers.
Why We Built Meibel
We built Meibel because we'd lived this problem from three different angles.
Two of us grew up together, went our separate ways, and reunited a decade later when AI needed the infrastructure we'd each been building. The third brought the computational neuroscience expertise to complete the solution.
One of us ran infrastructure platforms that abstracted complexity so teams could focus on products. One built AI systems and saw the deployment gap emerging. One brought the technical foundation to solve runtime evaluation problems.
Together, we built the runtime infrastructure that handles the complexity between data and decisions.



Who Builds With Meibel
Product and engineering teams building AI systems that need to work at scale.
Teams who know AI is the answer but have struggled to make it work reliably.
Teams using AI to create new capabilities their industries haven't seen before.


What Our Clients Say
Trusted by AI Builders Worldwide
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See how Meibel delivers the three Cs for AI systems that need to work at scale.












