KiteFishAI builds compact, domain-tuned language models for enterprises that need AI inside their own infrastructure. The company offers a family of open-source models—ranging from 0.6B to 10B parameters—covering OCR, embeddings, reasoning, speech, vision-language, and math, with fine-tuning options for BFSI, healthcare, pharma, and legal workflows. All models are designed for on-premise, air-gapped, or private cloud deployment to keep sensitive data under organizational control.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises in regulated industries such as banking, healthcare, and legal often rely on generic AI systems that require sending sensitive data to external cloud providers, creating privacy, compliance, and governance risks. Additionally, large general-purpose models are costly to run, difficult to fine-tune for domain-specific terminology and workflows, and impractical for on-premise deployment, limiting their adoption in production environments.
Solution
KiteFishAI provides a family of compact, high-performance language models designed for sovereign, private deployment. The models range from 0.6B to 10B parameters and are built on open-source foundations, allowing organizations to evaluate and adapt them freely before moving into production. KiteFishAI offers domain fine-tuning for BFSI, healthcare, pharma, and legal workflows, enabling models to understand industry-specific terminology, document structures, and risk logic. The models are engineered for on-premise, air-gapped, or private cloud environments, ensuring sensitive data remains within the organization's infrastructure and governance boundaries. The company also provides enterprise support for custom deployments, helping teams integrate models into their existing operational constraints around cost, latency, and controllability.
Target Audience
Primary customers are enterprises in banking, financial services, healthcare, pharma, and legal sectors that need private, domain-tuned AI models for regulated workflows where data privacy, accuracy, and operational control are critical.
Features
- Model family spanning 0.6B to 10B parameters, including OCR, embeddings, reasoning, speech, vision-language, and math models
- Open-source base models available on Hugging Face for free evaluation and fine-tuning experiments
- Domain fine-tuning for BFSI, healthcare, pharma, and legal workflows, covering compliance analysis, credit risk reasoning, regulatory Q&A, and document summarization
- Sovereign deployment options including on-premise, air-gapped, and private cloud environments
- Compact model design optimized for lower inference cost, faster deployment cycles, and practical edge environments
- Enterprise support for custom domain fine-tuning and production integration