Research and Open-Source
Leverage our research and open-source toolkits to gain a development advantage in solving growth-stage B2B challenges
The AI Lab researches emerging AI techniques to facilitate portfolio company adoption.
01
Identify common growth-stage challenges and conduct research to understand the problem.
LLM perforamnce and fine-tuning is an example.
02
Develop solutions and team capabilities to address these challenges.
We ran benchmarking tests on a series of LLMs.
03
Package solutions as toolkits and research insights.
We published and LLM fine-tuning toolkit alongside blog series on the work.
04
Portfolio companies can adopt these solutions, saving R&D time.
Our Github repositories have 1k+ stars.
AI, Applied 2024
Access benchmarking data from a survey of 600+ B2B growth-stage executives.
Research Projects and Open-Source Toolkits
Our research focuses on emerging techniques and challenges faced by growth-stage software companies as they adopt AI.
You can find some of our open-source repos and technical blogs from our AI Lab below.
LLM - Fine-tuning Toolkit
Fine-tune open source LLMs
Time Series Anomaly Detection
Identify irregular patterns in time series data.
Multimodal-Toolkit
Multimodal model for text and tabular data with HuggingFace transformers as a building block for text data.
Georgian GenAI Bootcamp
This repository contains demos and code from our bootcamps.
Open-source LLM Evaluation
We've run our own benchmarking on a variety of open-source LLMs.
LLM Delivery Methods
We delve into the world of open source tools that facilitate the deployment of machine learning models, including LLMs.
Read Our Research Papers
Discover research our AI team has published in leading academic journals.