Key Features and Functions include:

Enterprise-Grade RAG Infrastructure
Provides retrieval-augmented generation capabilities with ingest, storage, search, and evaluation functions accessible via APIs for seamless integration into legal and other applications. 

Advanced Document Processing
Uses computer vision and ML models trained on large corpora to parse and transform complex legal files — including contracts, court filings, police reports, and affidavits — into structured data suitable for LLM workflows.

Secure Deployment Options
Supports cloud, on-premises, and air-gapped installations to meet stringent data protection and confidentiality requirements common in legal and regulated environments.

Custom Model Fine-Tuning
Enables tailor-made ingestion models and workflows aligned to specific legal document types or organizational needs, improving relevance and accuracy of AI outputs.

Scalable Architecture
Built on Kubernetes and optimized for open-source models to ensure performance and reliability under high-volume workloads typical of legal and enterprise contexts.

Evaluation and Quality Tools
Offers built-in evaluation capabilities that help developers detect and diagnose errors across the RAG pipeline from ingestion to retrieval and generation.

Legal AI Use Cases
The platform supports legal workflows by enabling rapid search and question-answering across case-specific discovery and legal files. 

This offering initially began as a litigation-focused AI assistant and has since expanded to support applications across broader legal practice areas and enterprise domains.

Funding Information:
EyeLevel.ai has raised a total of $2M in funding over 2 rounds. Their latest funding was raised on Feb 22, 2019 from a Seed round from Alumni Ventures, Berkeley SkyDeck Fund, Birchmere Ventures, Inspiration Ventures, Service Provider Capial, and Strawberry Creek Ventures.