İMBİK AI
İMBİK provides a model distillation system that embeds an organization's specific quality standards and workflow preferences directly into custom AI model weights. The system catches and corrects failure modes during training, then deploys the resulting specialist model as an API endpoint that continuously evolves from production logs. It targets enterprise teams in specialized knowledge domains like life sciences, legal, and finance where generic models fail to follow organization-specific instructions.
- Artificial Intelligence
- AI Agents
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Teams deploying AI agents in specialized fields such as life sciences, legal, and finance consistently encounter a common problem: AI agents fail to follow specific instructions and do not adapt to the particular needs and workflows of individual organizations. Generic models are trained once by centralized organizations and cannot internalize the intangible quality standards, style preferences, and domain-specific best practices that define good work in real-world enterprise settings.
Solution
İMBİK provides a fully functional model distillation system that captures what "good work" means for a specific team and bakes those preferences directly into the weights of a custom model. The system feeds customer's biggest pain points into the distillation pipeline, catching and correcting failure modes as they occur, and iterates until the model behaves correctly. The resulting specialist model is deployed as an API endpoint—either hosted by İMBİK or delivered to the customer's infrastructure—that drops directly into existing agents, and the environment updates continuously from production logs as data and tools change, allowing the model to evolve with the organization.
Target Audience
İMBİK's primary customers are enterprise teams deploying AI agents in specialized knowledge work domains including life sciences, legal, tax, and finance, who need models that reliably follow organization-specific instructions and quality standards.
Features
- Model distillation system that converts customer-defined quality metrics into model weights, embedding preferences for output style, citation behavior, and conciseness directly into the model
- Failure-mode detection and correction pipeline that identifies and fixes behavior issues during distillation training
- Custom API endpoint deployment with flexible hosting options—cloud-hosted by İMBİK or delivered to the customer's own infrastructure
- Continuous learning loop that ingests production logs to update the model environment as enterprise data and tools evolve
- Specialized for non-verifiable domains where standard reinforcement learning fails, such as tax law, life sciences, and physical machinery maintenance, addressing quality attributes like grounded answers, appropriate output length, and adherence to professional standards