Every major tech company is working actively on AI.

Notion, Hubspot, Apollo, and every other tool that I use on a daily basis integrated AI into their systems.

Some people are even letting ChatGPT be the CEO of their company.

We’re only at the beginning of this trend and there is a lot of opportunity to do amazing stuff.

But how can we actually build systems and products with AI that are going to benefit our startup and customers?

Let’s jump in 👇

Identify areas for AI integration:

Start by identifying specific areas within your startup where AI can make a significant impact. Some thoughts:

• Customer service: Train a bot to be the ChatGPT of your company for customers. ChatBase a fantastic tool for this purpose.
• Internal Communications: Similarly, you can have a ChatGPT bot in Slack to answer employee questions. Once again, I recommend Chatbase
• Product Development: Analyze hundreds of data points in seconds and come up with data-based feature requests
• Marketing: Improve how quickly you make videos, images, and more with AI.
• Finding product-market fit → A great post on using GPT for this purpose on Indiehackers called Can ChatGPT help you reach product-market fit?

Choose the right AI tools and models:

I have listed a bunch of AI tools below, but do your research and choose the ones that align with your business needs. 👇

Rationale AI: AI assists business owners and managers in making rational decisions.

Algorithmia: Deploy and scale ML models in the cloud. Good for startups looking to implement their own AI model.

CleverTap: An AI-powered mobile marketing automation platform that can help startups increase user engagement and retention.

Phind: The AI search engine for developers. Code your startup faster, fix bugs sooner, and get more done.

Appier: AI-powered marketing automation platform that can help you optimize your ad campaigns and increase your ROI.

You can view more at https://nobsstartupguide.com/top-ai-tools-for-startups-in-2023/

Hire an AI engineer to help:

Here how you can do this:

• Identify the specific expertise needed.
• Explore platform where you can find Good engineers, here are some of them.

OnlineJobs

Upwork

Indeed

Alternatively, you can also use LinkedIn and GitHub to find great engineers.

Data is the KEY 🔑

Common areas to look at:

1. OpenAI Whisper: A tool by OpenAI for speech recognition and transcription.
2. Kaggle Datasets: Provides a wide range of datasets for machine learning projects.
3. Google Dataset Search: Helps discover publicly available datasets for research and analysis

You can use DATA from recorded interviews, support tickets, emails, and notes to fine-train your ML models.

The more diverse and high-quality data you have, the better your AI models can perform.