Belvedir provides a platform that captures every AI agent interaction and tool usage, converting raw company data and production traces into structured training datasets and simulation environments. It then applies reinforcement learning and specialized optimizers to train model weights and memory layers, allowing organizations to deploy AI models on their own infrastructure and continuously improve performance with each deployment cycle. This end‑to‑end workflow lets companies maintain control over their AI while iteratively enhancing its capabilities.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often deploy AI models that become outdated because they lack a systematic way to capture production interactions, convert them into training data, and continuously refine model weights and memory components. Traditional fine‑tuning APIs provide only a single static checkpoint, leaving organizations without an ongoing improvement loop.
Solution
Belvedir offers an end‑to‑end platform that ingests raw call logs, tool usage records, and other proprietary data from an organization’s AI agents. The service transforms these production traces into structured training datasets and simulated environments, then applies reinforcement‑learning techniques and specialized optimizers to generate updated model weights and memory layers. Updated models can be deployed on the customer’s own infrastructure, ensuring data sovereignty. Each deployment feeds new production traces back into the pipeline, creating a continuous cycle of data collection, training, and improvement. The platform also provides benchmarking tools to evaluate model performance across iterations.
Target Audience
Primary customers are large enterprises and organizations that operate AI‑driven agents or assistants and require ongoing model refinement while maintaining control over their data and infrastructure.
Features
- Automated capture of all agent calls, tool usage, and raw company data for comprehensive trace collection
- Conversion of unstructured traces into curated training datasets and reproducible simulation environments
- Reinforcement‑learning pipelines that generate both model weight updates and memory‑layer optimizations
- Deployment package compatible with on‑premises or private cloud infrastructure, keeping models and data under customer control
- Continuous feedback loop where each model release supplies new production traces for the next training cycle
- Integrated benchmarking suite to measure performance improvements across iterations