Phased AI provides an AI Operations platform that enables enterprises to integrate large language models with both structured and unstructured data while retaining full control and governance. The service includes custom LLM tool development, data architecture design, vector‑based retrieval‑augmented generation pipelines, and consulting‑plus‑training to ensure safe, real‑time insights for regulated industries.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often have large volumes of structured and unstructured data but lack a coherent framework to integrate large language models (LLMs) safely and effectively into business operations. This results in fragmented AI initiatives, high technical debt, and delayed decision‑making due to reliance on manual data analysis.
Solution
Phased AI offers an AI Operations (AI Ops) platform that helps organizations harness both structured and unstructured data while maintaining control over LLM usage. The company builds custom LLM tools, designs data architectures, and implements vector databases and retrieval‑augmented generation pipelines to enable reliable, real‑time insights. It also provides consulting, data‑preparation services, and training to embed AI best practices and governance within existing teams. By delivering end‑to‑end solutions—from data tagging and preprocessing to prompt engineering and model integration—Phased AI creates a scalable AI ecosystem that augments human decision‑makers rather than replacing them.
Target Audience
Primary customers are mid‑size to large enterprises across regulated industries (e.g., finance, legal, healthcare) that need to operationalize generative AI while managing data complexity and compliance.
Features
- Custom LLM tool development that connects directly to an organization’s data sources while preserving data ownership
- Metadata tagging and standardized preprocessing pipelines for both structured and unstructured data
- Integration of vector databases and embeddings to enable fast, relevant retrieval for generative AI applications
- Retriever‑Augmented Generation (RAG) pipelines with domain‑specific prompt templates (e.g., legal, fintech) for accurate output
- Open‑source framework support (e.g., LangChain) for flexible, extensible model orchestration
- AI governance and safety consulting, including risk assessment, prompt engineering standards, and human‑in‑the‑loop controls
- Training programs and workshops to upskill staff on safe LLM deployment and AI‑driven workflow design