online degrees

Data Collections

Include pertinent business data and documents related to the ongoing conversation directly in your prompts, without requiring a vector database.

short courses

Users & Chat Sessions Management

In Dataconvo, prioritizing users and their persistent chat sessions effectively streamlines the management of interactions with your Assistant.

Instantly retrieve, comprehend, and extract data from chat histories to enrich personalized AI experiences.

Adhere to corporate and regulatory requirements for records retention while ensuring compliance with privacy regulations like CCPA and GDPR.

Comply with corporate and regulatory mandates for records retention.

Handle requests efficiently with a single API call or through the Dataconvo Web App.
About Us

What make us the best?

Dataconvo provides a rapid user experience. Memory recall, dialog classification, data extraction, and other functions perform significantly faster than comparable features offered by leading LLM vendors, achieving speeds up to 80% faster.

Dataconvo

LLMs and other AI tools

Dataconvo for Data Extraction

Dataconvo's data model covers text, dates, numbers, phones, emails, and zip codes. Its Regex field manages e-nums and CSV parsing, outperforming OpenAI’s LLM providers

Structured Data Extraction

Dataconvo’s Structured Data Extraction is a powerful tool designed for extracting data from chat histories stored in Zep’s Long-term Memory service. Notably, it outperforms GPT-4 by being up to 10 times faster.

Search and Enrichment, Enhanced Scalability

Dataconvo now supports searching chat history summaries, enabling developers to provide rich, concise context to LLMs.

Records Retention and Right to Erasure

Dataconvo simplifies compliance with data privacy regulations like GDPR and CCPA for LLM applications by providing robust data handling and management features.

HNSW Indexes and Personalized Prompts

Hierarchical Navigable Small World (HNSW) indexes are faster, more accurate, and easier to maintain than the previously used IVFFLAT indexes in Dataconvo, as they don't require a manual indexing step.

Intent Routing for LLM Applications

Creating an intent router with Langchain ensures your LLM app understands user intent effectively, automatically selecting the most suitable prompt for each task.