Today, I’m proud to share that Meibel has raised $7 million in seed funding to expand our platform and bring confident AI to more teams and more industries. The round was led by Mosaic General Partnership with participation from Array Ventures, Denver Ventures, Cofounders Capital, and Service Provider Capital.
This announcement is a milestone for us, but more importantly, it reflects a larger shift happening across the AI landscape. As organizations move from experimentation to implementation, the stakes for AI behavior have changed. It is no longer enough to get good outputs. Teams need systems that can explain themselves, adapt in real time, and earn trust from users, operators, and stakeholders.
That is exactly why we built Meibel.
The Real Gap in AI Today
In our conversations with product and engineering leaders across industries, one theme kept coming up. Many teams have made impressive progress in building with generative AI. They have prototypes, demos, and internal tools that showcase what’s possible. But very few of those efforts make it into full deployment.
Not because the models are weak or the data is wrong, but because there is no system-level control once the model generates an output. Teams cannot trace how a decision was made. They cannot measure how confident the output is. They cannot easily route results for review or adjust decision logic without manually updating prompts or workflows.
Most of all, they cannot guarantee that what works in a demo will still work under pressure, in production, in front of customers, or under regulatory oversight.
Meibel exists to solve that.
What Meibel Is
Meibel is the runtime platform for confident AI.
We provide the infrastructure that manages how AI systems behave in real time, including how they retrieve data, generate responses, make decisions, and respond to uncertainty.
Our platform helps teams move from brittle prototypes to systems that are structured, explainable, and adaptable.
With Meibel, teams can:
- Ingest and structure data from PDFs, APIs, databases, and spreadsheets
- Define how data is retrieved and used in context-aware workflows
- Score outputs using customizable, multi-dimensional confidence metrics
- Route low-confidence or high-risk decisions to human reviewers or external systems
- Apply rules and logic that govern how decisions are made and adjusted over time
- Capture feedback and improve system behavior without retraining or rebuilding
These capabilities are all part of a single runtime definition, versioned, testable, and callable through a comprehensive API.
Why Runtime Matters
Most tooling in the generative AI ecosystem is focused on composition, prompting, or observability. These are important pieces of the stack, but none of them own what happens when a system is running and decisions need to be made in real time.
That is where things fail.
If you cannot see what data was retrieved, or how confident the output was, or why a certain path was taken, you cannot trust the decision. And if your users cannot trust the system, you will not ship the product.
Meibel is designed to sit between your models and your business logic. It is the layer that manages live behavior. It is how you make AI systems explainable and auditable, not just interesting.
As Paul Baier, CEO and Co-Founder of GAI Insights, recently said:
“For product and engineering teams building AI-driven features, success requires more than integrating models. It’s about delivering customer experiences that create real business value. That means using infrastructure like Meibel that provides transparency, ensures confidence in every output, and integrates seamlessly into the product. These capabilities are now essential for turning AI into a strategic advantage.”
This captures exactly how we think about the role Meibel plays in the evolving AI stack.
Trusted by Early Customers
One of our earliest customers, SpecBooks, used Meibel to solve a problem many had written off as too complex to automate. In commercial construction, quoting from architectural plans involves pulling product specifications from dense, inconsistent documents and matching them to a live product catalog.
With Meibel, SpecBooks was able to ingest thousands of spec sheets, extract structured data, and generate trusted recommendations with real-time confidence scoring and traceability. They now have a quoting engine that is not just fast, but explainable, consistent, and repeatable across regions and project types.
We also work with FAST, a public sector integrator supporting U.S. Army initiatives. These teams operate in environments where auditability, accountability, and transparency are not just good practices. They are requirements. Meibel helps them manage AI behavior with clarity and control, even in mission-critical workflows.
What Happens Next
With this seed round, we are expanding our engineering team, investing in advanced capabilities for runtime orchestration, feedback integration, and retrieval optimization, and growing our network of customers and partners across industries that need AI systems to work under real conditions.
We will also be sponsoring and participating in The AI Summit in London, taking place June 11–12 at Tobacco Dock. We will be showcasing how runtime infrastructure can enable AI systems to act with transparency, confidence, and responsiveness from day one.
Final Thoughts
We believe that the next generation of AI platforms will not be defined by how creative their outputs are, but by how well they support decisions. That means giving teams the infrastructure to monitor, adjust, and trust how AI behaves in the real world.
That is what we are building at Meibel. If you are working on AI products and need to control how decisions are made and justified, we would love to talk.