G2

TAMBADI R.

AI Engineer

Small-Business (50 or fewer emp.)

9/7/2026

"A powerful AI research partner for technical projects"

4.5/5

What do you like best about Lila Sciences?

What I like best about Lila Sciences is how much it simplifies the research and analysis stage of technical projects. I work with Python and have built projects such as real time object recognition and data analysis workflows, so I appreciate having a tool that can help me explore a research question and work through information before moving into implementation. The AI is particularly useful for narrowing down relevant information and exploring different directions without having to manually organize everything from scratch. I also find the workflow fairly easy to follow, and the performance is good for the type of research tasks I use it for. From an ROI perspective, the biggest benefit is the time saved during the early research stages. I would also like to see broader integrations with technical tools and existing workflows as the platform develops. Review collected by and hosted on G2.com.

What do you dislike about Lila Sciences?

The main thing I would improve is the learning curve around some of the more advanced capabilities. I am comfortable working with Python, data analysis, and AI projects, but it can still take some time to understand the best way to use the platform for a particular research task. I would also like to see more integrations with technical tools and existing workflows, especially for users who move between research and implementation. More practical examples, clearer onboarding, and better guidance around advanced features would make it easier to get consistent value from the platform. Review collected by and hosted on G2.com.

What problems is Lila Sciences solving and how is that benefiting you?

Lila supported my Real Time Object Recognition project by helping me research different approaches to object detection, compare relevant methods, and work through technical details before implementation. For my Oceanographic Data Analysis project, I used it to explore the dataset and its research context, identify patterns worth analysing, and refine the questions I wanted to answer. Across both projects, it reduced the time I spent manually searching through technical and scientific information and helped me move faster from an initial research question to practical experimentation. Review collected by and hosted on G2.com.