AI Agents builds and deploys autonomous AI agents that are customized to fit specific business workflows, handling integration, execution, and result tracking from day one. Their services include creating Retrieval‑Augmented Generation (RAG) pipelines that connect a client’s knowledge base to AI‑driven workflows, as well as fine‑tuning models and providing on‑premise AI infrastructure. This end‑to‑end approach lets organizations automate complex tasks while maintaining control over data and performance.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often struggle to integrate generative AI into existing processes because building custom retrieval‑augmented generation pipelines, fine‑tuning large language models, and automating AI‑driven workflows require specialized expertise and secure infrastructure. This leads to fragmented solutions, long development cycles, and reliance on external cloud services that may not meet data‑sovereignty requirements.
Solution
AI Agents provides end‑to‑end design and deployment of autonomous AI agents that embed directly into a company’s workflows. The service includes custom RAG pipelines that connect large language models to proprietary knowledge bases, domain‑specific model fine‑tuning (including LoRA and domain adaptation), and fully managed AI workflow automation. All components run on sovereign on‑premise infrastructure, ensuring data remains under the client’s control while delivering real‑time decision support. AI Agents handles data ingestion, model training, and continuous operation, allowing organizations to achieve measurable outcomes from day one without building in‑house MLOps capabilities.
Target Audience
Primary customers are mid‑size to large enterprises seeking to embed generative AI into their operational workflows, particularly those with strict data‑sovereignty or compliance requirements.
Features
- Custom Retrieval‑Augmented Generation pipelines that index and query enterprise knowledge bases for context‑aware responses
- Fine‑tuning services for large language models using LoRA and domain adaptation to improve relevance and accuracy
- Autonomous AI agents that execute tasks, trigger actions, and interact with existing software systems
- End‑to‑end workflow automation integrating AI outputs into business processes with real‑time decision making
- Sovereign on‑premise deployment on dedicated GPU stations, ensuring data privacy and compliance
- Managed AI stack covering data ingestion, model training, monitoring, and continuous updates