Conduit by Automorphic
Infuse domain knowledge into language models beyond context limits
Target Audience
- ML Engineers implementing production AI systems
- AI developers working with domain-specific language models
- Enterprises requiring secure model deployment
Hashtags
Overview
Conduit helps developers efficiently train AI models without hitting context window barriers. It uses fine-tuning and modular adapters to embed specialized knowledge/behavior into language models. Continuously improves models through real user feedback while keeping sensitive data secure in your own cloud.
Key Features
Adapter Stacking
Combine specialized model behaviors like building blocks
Closed-Loop Training
Improve models using real-world user feedback automatically
On-Prem Deployment
Keep sensitive data secure in your own cloud environment
OpenAI Compatibility
Works with existing OpenAI API implementations seamlessly
Use Cases
Fine-tune models for domain-specific tasks
Deploy secure custom models in private cloud
Continuously improve production AI with feedback
Combine multiple specialized model behaviors
Pros & Cons
Pros
- Bypasses LLM context window limitations
- Enables rapid iteration with live feedback
- Maintains data sovereignty through private deployment
- Modular architecture reduces retraining costs
Cons
- Requires ML engineering expertise to implement
- Primarily benefits OpenAI ecosystem users
Frequently Asked Questions
How does Conduit solve context window limitations?
Uses fine-tuned adapters instead of prompt stuffing to embed knowledge
Can I use my existing OpenAI integrations?
Yes, works with OpenAI API implementations without code changes
Where is my data stored?
Supports deployment in your own VPC for data security
Integrations
Reviews for Conduit by Automorphic
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