VectorCat
by sci2sci Enterprise AI agents for your data
Build domain-specific AI applications by safely integrating proprietary data with any AI model
VectorCat
by sci2sci Enterprise AI agents for your data
Build domain-specific AI applications by safely integrating proprietary data with any AI model
Customize and deploy AI agents right on your data
With VectorCat's agent orchestration framework, technical teams can develop, test and deploy AI agents directly on the platform, with instant access to organizational data and context.
Whether for data analysis, research assistance or process automation, VectorCat offers a powerful AI development environment while supporting familiar tools like Python and its libraries.
Workflow visualization
Automatically visualizes complex DAG-based flows for both developers and business users, making it easy to test, validate, and ensure the quality of each step — enabling enterprise-grade agent implementations.
Distributed execution
Leverages a distributed execution mechanism where client machines control the flow, reducing server infrastructure costs and complexity while improving responsiveness and interactivity in agentic workflows.
Model-agnostic approach
Supports seamless integration with any model from any provider, enabling rapid performance testing and effortless upgrades when better models become available.
AI agent use cases developed on VectorCat:
AI research assistant
Biotech and pharma companies collect massive amounts of data throughout their history, but using historical data is always a challenge.
To address this, we have developed an AI research assistant. In response to a user’s question, it is able to run its own search on the organization’s resources and find relevant files/documents/data assets, providing the summary and references to all findings. It’s able to answer questions that typically require domain-specific knowledge and multiple years of experience in the company, e.g. “what experiments have we performed with compound N?”
Data extraction agent
Biotech and pharma companies struggle with non-standardized data from different sources (people, machines, software). Before doing data analysis, people often spend hours harmonizing and normalizing the data into consistent formats manually.
To resolve this issue, we built an LLM-based data extraction agent. It extracts data from multiple structured or unstructured documents (lab/field notes, protocols, log files etc.) and converts it to the format required by the user (e.g. table, json etc.). If necessary, the outputs can always be manually corrected by the user before validation.
The data extraction agent reduces time on data annotation and curation from several hours to 1-2 minutes.
It can:
- convert languages (even if some notes are in a user’s native language, it will fill in the info in English);
- convert units (e.g. 2 mL → 2000 µL);
- extract information not explicitly stated in the texts but possible to infer from the context;
- collect metadata from multiple documents simultaneously.
Documentation agent
Managing documentation across biotech and pharma organizations is complex, with critical information often scattered across various formats, versions and locations.
To streamline this process, we developed Documentation agent. It automatically generates and maintains up-to-date documentation by understanding the context of your data assets, including their relationships to other assets within the same project or workflow.
The agent can create comprehensive documentation for data pipelines, experimental protocols and analytical workflows, while ensuring consistency across different teams and departments. Our Documentation agent understands technical and domain-specific content, making it valuable for both wet lab scientists documenting procedures and computational teams maintaining code documentation.
