Why it matters

AI models aren’t broken. They’re just hungry. And most of what they’re fed is unstructured, unanchored, and unaccountable.

At Perigon°, we’ve built the opposite:

• Real-time data pipelines with provenance trails

• Entity-tagged, sentiment-scored, fact-labeled context graphs

• Precision filters that separate attention-worthy from attention-seeking

The trick is feeding this into AI systems in a way that:

1. Preserves the logic of the dataset

2. Respects the integrity of the model

3. Augments the judgment of the human operator

MCP is the missing link.

Next steps

We’re drafting an MCP adapter internally, working on a modular parser that auto-generates model-ready prompt scaffolds from live Perigon queries. We’re not trying to reinvent the model. We’re just teaching it to listen.