The Connito Story
Fine-tuning large models for specific business needs is expensive, brittle, and doesn't compound across projects. The root cause is architectural — monolithic models share all their weights, so improving one capability can quietly degrade others (catastrophic forgetting).
Connito takes a different approach.
The Solution
Connito is a decentralized MoE training platform. Instead of modifying one monolithic model, it trains isolated expert modules that slot into a shared architecture without interfering with each other.
Every engagement adds experts to a compounding library — new projects start from stronger baselines. Contributors across the Bittensor network train experts in parallel, and validators score quality using Proof-of-Loss.
Business Case
The custom AI market sits between cheap self-serve APIs and expensive consulting. Connito fills this gap with usage-based pricing and compounding value across engagements.
Design Principles
Four principles guide the distributed training system: aligned training/validation distributions, Proof-of-Loss scoring, top-N merging (not winner-take-all), and low per-miner hardware requirements through expert parallelism.
How We Compare
Connito's expert-parallel approach differs from other distributed training methods and existing Training-as-a-Service models. The key differences: contributions don't conflict, value compounds, and quality is incentive-aligned.